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Journal articles on the topic 'Context Encoder'

Author: Grafiati

Published: 28 June 2021

Last updated: 29 July 2025

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1

Pinho, M. S., and W. A. Finamore. "Context-based LZW encoder." Electronics Letters 38, no. 20 (2002): 1172. http://dx.doi.org/10.1049/el:20020807.

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Alharbi, Majed, Ahmed Stohy, Mohammed Elhenawy, Mahmoud Masoud, and Hamiden El-Wahed Khalifa. "Solving Traveling Salesman Problem with Time Windows Using Hybrid Pointer Networks with Time Features." Sustainability 13, no. 22 (2021): 12906. http://dx.doi.org/10.3390/su132212906.

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This paper introduces a time efficient deep learning-based solution to the traveling salesman problem with time window (TSPTW). Our goal is to reduce the total tour length traveled by -*the agent without violating any time limitations. This will aid in decreasing the time required to supply any type of service, as well as lowering the emissions produced by automobiles, allowing our planet to recover from air pollution emissions. The proposed model is a variation of the pointer networks that has a better ability to encode the TSPTW problems. The model proposed in this paper is inspired from our

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Qian, Xiaoyan, Chang Liu, Xiaojuan Qi, Siew-Chong Tan, Edmund Lam, and Ngai Wong. "Context-Aware Transformer for 3D Point Cloud Automatic Annotation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (2023): 2082–90. http://dx.doi.org/10.1609/aaai.v37i2.25301.

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3D automatic annotation has received increased attention since manually annotating 3D point clouds is laborious. However, existing methods are usually complicated, e.g., pipelined training for 3D foreground/background segmentation, cylindrical object proposals, and point completion. Furthermore, they often overlook the inter-object feature correlation that is particularly informative to hard samples for 3D annotation. To this end, we propose a simple yet effective end-to-end Context-Aware Transformer (CAT) as an automated 3D-box labeler to generate precise 3D box annotations from 2D boxes, tra

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Han, Jialong, Aixin Sun, Haisong Zhang, Chenliang Li, and Shuming Shi. "CASE: Context-Aware Semantic Expansion." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7871–78. http://dx.doi.org/10.1609/aaai.v34i05.6293.

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In this paper, we define and study a new task called Context-Aware Semantic Expansion (CASE). Given a seed term in a sentential context, we aim to suggest other terms that well fit the context as the seed. CASE has many interesting applications such as query suggestion, computer-assisted writing, and word sense disambiguation, to name a few. Previous explorations, if any, only involve some similar tasks, and all require human annotations for evaluation. In this study, we demonstrate that annotations for this task can be harvested at scale from existing corpora, in a fully automatic manner. On

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Xing, Bowen, and Ivor W. Tsang. "Out of Context: A New Clue for Context Modeling of Aspect-based Sentiment Analysis." Journal of Artificial Intelligence Research 74 (June 7, 2022): 627–59. http://dx.doi.org/10.1613/jair.1.13410.

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Aspect-based sentiment analysis (ABSA) aims to predict the sentiment expressed in a review with respect to a given aspect. The core of ABSA is to model the interaction between the context and given aspect to extract aspect-related information. In prior work, attention mechanisms and dependency graph networks are commonly adopted to capture the relations between the context and given aspect. And the weighted sum of context hidden states is used as the final representation fed to the classifier. However, the information related to the given aspect may be already discarded and adverse information

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Marafioti, Andres, Nathanael Perraudin, Nicki Holighaus, and Piotr Majdak. "A Context Encoder For Audio Inpainting." IEEE/ACM Transactions on Audio, Speech, and Language Processing 27, no. 12 (2019): 2362–72. http://dx.doi.org/10.1109/taslp.2019.2947232.

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Xie, Wei, Haobo Jiang, Yun Zhu, Jianjun Qian, and Jin Xie. "NaviFormer: A Spatio-Temporal Context-Aware Transformer for Object Navigation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 14 (2025): 14708–16. https://doi.org/10.1609/aaai.v39i14.33612.

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Learning discriminative state representations of agents, encompassing the spatial layout and temporal pose trajectory, is essential for effective navigation decisions. However, existing approaches often rely on simplistic plain networks for navigation information fusion, overlooking the complex long-range dependencies across spatio-temporal cues, which leads to suboptimal state perception and potential decision failures. In this paper, we introduce NaviFormer, an effective encoder-decoder navigation transformer, to aggregate discriminative spatio-temporal context information for object navigat

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Xu, Feng, Shengyi Jiang, Hao Yin, et al. "Enhancing Context-Based Meta-Reinforcement Learning Algorithms via An Efficient Task Encoder (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (2021): 15937–38. http://dx.doi.org/10.1609/aaai.v35i18.17965.

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Meta-Reinforcement Learning (meta-RL) algorithms enable agents to adapt to new tasks from small amounts of exploration, based on the experience of similar tasks. Recent studies have pointed out that a good representation of a task is key to the success of off-policy context-based meta-RL. Inspired by contrastive methods in unsupervised representation learning, we propose a new method to learn the task representation based on the mutual information between transition tuples in a trajectory and the task embedding. We also propose a new estimation for task similarity based on Q-function, which ca

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Dakwale, Praveen, and Christof Monz. "Convolutional over Recurrent Encoder for Neural Machine Translation." Prague Bulletin of Mathematical Linguistics 108, no. 1 (2017): 37–48. http://dx.doi.org/10.1515/pralin-2017-0007.

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AbstractNeural machine translation is a recently proposed approach which has shown competitive results to traditional MT approaches. Standard neural MT is an end-to-end neural network where the source sentence is encoded by a recurrent neural network (RNN) called encoder and the target words are predicted using another RNN known as decoder. Recently, various models have been proposed which replace the RNN encoder with a convolutional neural network (CNN). In this paper, we propose to augment the standard RNN encoder in NMT with additional convolutional layers in order to capture wider context

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Yun, Hyeongu, Yongkeun Hwang, and Kyomin Jung. "Improving Context-Aware Neural Machine Translation Using Self-Attentive Sentence Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 9498–506. http://dx.doi.org/10.1609/aaai.v34i05.6494.

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Fully Attentional Networks (FAN) like Transformer (Vaswani et al. 2017) has shown superior results in Neural Machine Translation (NMT) tasks and has become a solid baseline for translation tasks. More recent studies also have reported experimental results that additional contextual sentences improve translation qualities of NMT models (Voita et al. 2018; Müller et al. 2018; Zhang et al. 2018). However, those studies have exploited multiple context sentences as a single long concatenated sentence, that may cause the models to suffer from inefficient computational complexities and long-range dep

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Dligach, Dmitriy, Majid Afshar, and Timothy Miller. "Toward a clinical text encoder: pretraining for clinical natural language processing with applications to substance misuse." Journal of the American Medical Informatics Association 26, no. 11 (2019): 1272–78. http://dx.doi.org/10.1093/jamia/ocz072.

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Abstract Objective Our objective is to develop algorithms for encoding clinical text into representations that can be used for a variety of phenotyping tasks. Materials and Methods Obtaining large datasets to take advantage of highly expressive deep learning methods is difficult in clinical natural language processing (NLP). We address this difficulty by pretraining a clinical text encoder on billing code data, which is typically available in abundance. We explore several neural encoder architectures and deploy the text representations obtained from these encoders in the context of clinical te

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Allouche, Mohamed, Elliot Cole, Mateo Zoughebi, Carl De Sousa Trias, and Mihai Mitrea. "Stream encoder identification in green video context." Electronic Imaging 37, no. 10 (2025): 234–1. https://doi.org/10.2352/ei.2025.37.10.ipas-234.

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Lan, Meng, Jing Zhang, Fengxiang He, and Lefei Zhang. "Siamese Network with Interactive Transformer for Video Object Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 1228–36. http://dx.doi.org/10.1609/aaai.v36i2.20009.

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Semi-supervised video object segmentation (VOS) refers to segmenting the target object in remaining frames given its annotation in the first frame, which has been actively studied in recent years. The key challenge lies in finding effective ways to exploit the spatio-temporal context of past frames to help learn discriminative target representation of current frame. In this paper, we propose a novel Siamese network with a specifically designed interactive transformer, called SITVOS, to enable effective context propagation from historical to current frames. Technically, we use the transformer e

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Sun, Jun, Junbo Zhang, Xuesong Gao, et al. "Fusing Spatial Attention with Spectral-Channel Attention Mechanism for Hyperspectral Image Classification via Encoder–Decoder Networks." Remote Sensing 14, no. 9 (2022): 1968. http://dx.doi.org/10.3390/rs14091968.

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In recent years, convolutional neural networks (CNNs) have been widely used in hyperspectral image (HSI) classification. However, feature extraction on hyperspectral data still faces numerous challenges. Existing methods cannot extract spatial and spectral-channel contextual information in a targeted manner. In this paper, we propose an encoder–decoder network that fuses spatial attention and spectral-channel attention for HSI classification from three public HSI datasets to tackle these issues. In terms of feature information fusion, a multi-source attention mechanism including spatial and sp

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Wang, Guixian, Dandan Huang, ZhenYe Geng, Zhi Liu, and Jin Duan. "A Novel Encoder-Decoder Structure-based Transformer for Fine-Resolution Remote Sensing Images." Journal of Physics: Conference Series 2517, no. 1 (2023): 012017. http://dx.doi.org/10.1088/1742-6596/2517/1/012017.

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Abstract Full convolution neural network (FCN) based on an encoder-decoder structure has become a standard network in the semantic segmentation domain. Encoder-decoder architecture is an effective means to get finer-grained performance. Encoders constantly extract multilevel features, and then use decoders to gradually introduce low-level features into high-level features. Context information is critical for accurate segmentation, which is the main direction of semantic segmentation at present. So many efforts have been made to make better use of this kind of information, including codec struc

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Trisedya, Bayu, Jianzhong Qi, and Rui Zhang. "Sentence Generation for Entity Description with Content-Plan Attention." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 9057–64. http://dx.doi.org/10.1609/aaai.v34i05.6439.

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We study neural data-to-text generation. Specifically, we consider a target entity that is associated with a set of attributes. We aim to generate a sentence to describe the target entity. Previous studies use encoder-decoder frameworks where the encoder treats the input as a linear sequence and uses LSTM to encode the sequence. However, linearizing a set of attributes may not yield the proper order of the attributes, and hence leads the encoder to produce an improper context to generate a description. To handle disordered input, recent studies propose two-stage neural models that use pointer

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Pan, Yirong, Xiao Li, Yating Yang, and Rui Dong. "Multi-Source Neural Model for Machine Translation of Agglutinative Language." Future Internet 12, no. 6 (2020): 96. http://dx.doi.org/10.3390/fi12060096.

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Benefitting from the rapid development of artificial intelligence (AI) and deep learning, the machine translation task based on neural networks has achieved impressive performance in many high-resource language pairs. However, the neural machine translation (NMT) models still struggle in the translation task on agglutinative languages with complex morphology and limited resources. Inspired by the finding that utilizing the source-side linguistic knowledge can further improve the NMT performance, we propose a multi-source neural model that employs two separate encoders to encode the source word

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Ulacha, Grzegorz, and Mirosław Łazoryszczak. "Lossless Image Compression Using Context-Dependent Linear Prediction Based on Mean Absolute Error Minimization." Entropy 26, no. 12 (2024): 1115. https://doi.org/10.3390/e26121115.

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This paper presents a method for lossless compression of images with fast decoding time and the option to select encoder parameters for individual image characteristics to increase compression efficiency. The data modeling stage was based on linear and nonlinear prediction, which was complemented by a simple block for removing the context-dependent constant component. The prediction was based on the Iterative Reweighted Least Squares (IRLS) method which allowed the minimization of mean absolute error. Two-stage compression was used to encode prediction errors: an adaptive Golomb and a binary a

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Cai, Yuanyuan, Min Zuo, Qingchuan Zhang, Haitao Xiong, and Ke Li. "A Bichannel Transformer with Context Encoding for Document-Driven Conversation Generation in Social Media." Complexity 2020 (September 17, 2020): 1–13. http://dx.doi.org/10.1155/2020/3710104.

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Along with the development of social media on the internet, dialogue systems are becoming more and more intelligent to meet users’ needs for communication, emotion, and social intercourse. Previous studies usually use sequence-to-sequence learning with recurrent neural networks for response generation. However, recurrent-based learning models heavily suffer from the problem of long-distance dependencies in sequences. Moreover, some models neglect crucial information in the dialogue contexts, which leads to uninformative and inflexible responses. To address these issues, we present a bichannel

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Wang, Quan, Shanshan He, Miao Su, and Feng Zhao. "Context-Encoder-Based Image Inpainting for Ancient Chinese Silk." Applied Sciences 14, no. 15 (2024): 6607. http://dx.doi.org/10.3390/app14156607.

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The rapid advancement of deep learning technologies presents novel opportunities for restoring damaged patterns in ancient silk, which is pivotal for the preservation and propagation of ancient silk culture. This study systematically scrutinizes the evolutionary trajectory of image inpainting algorithms, with a particular emphasis on those firmly rooted in the Context-Encoder structure. To achieve this study’s objectives, a meticulously curated dataset comprising 6996 samples of ancient Chinese silk (256 × 256 pixels) was employed. Context-Encoder-based image inpainting models—LISK, MADF, and

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Kim, Junoh, Rui Gao, Jisun Park, Jinsoo Yoon, and Kyungeun Cho. "Switchable-Encoder-Based Self-Supervised Learning Framework for Monocular Depth and Pose Estimation." Remote Sensing 15, no. 24 (2023): 5739. http://dx.doi.org/10.3390/rs15245739.

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Monocular depth prediction research is essential for expanding meaning from 2D to 3D. Recent studies have focused on the application of a newly proposed encoder; however, the development within the self-supervised learning framework remains unexplored, an aspect critical for advancing foundational models of 3D semantic interpretation. Addressing the dynamic nature of encoder-based research, especially in performance evaluations for feature extraction and pre-trained models, this research proposes the switchable encoder learning framework (SELF). SELF enhances versatility by enabling the seamle

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Tian, Yang, and Pei Sun. "Information thermodynamics of encoding and encoders." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 6 (2022): 063109. http://dx.doi.org/10.1063/5.0068115.

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Non-isolated systems have diverse coupling relations with the external environment. These relations generate complex thermodynamics and information transmission between the system and its environment. The framework depicted in the current research attempts to glance at the critical role of the internal orders inside the non-isolated system in shaping the information thermodynamics coupling. We characterize the coupling as a generalized encoding process, where the system acts as an information thermodynamics encoder to encode the external information based on thermodynamics. We formalize the en

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Masood, Sharjeel, Fawad Ahmed, Suliman A. Alsuhibany, et al. "A Deep Learning-Based Semantic Segmentation Architecture for Autonomous Driving Applications." Wireless Communications and Mobile Computing 2022 (June 18, 2022): 1–12. http://dx.doi.org/10.1155/2022/8684138.

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In recent years, the development of smart transportation has accelerated research on semantic segmentation as it is one of the most important problems in this area. A large receptive field has always been the center of focus when designing convolutional neural networks for semantic segmentation. A majority of recent techniques have used maxpooling to increase the receptive field of a network at an expense of decreasing its spatial resolution. Although this idea has shown improved results in object detection applications, however, when it comes to semantic segmentation, a high spatial resolutio

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Cheng, Jinfeng, Weiqin Tong, and Weian Yan. "Capsule Network Improved Multi-Head Attention for Word Sense Disambiguation." Applied Sciences 11, no. 6 (2021): 2488. http://dx.doi.org/10.3390/app11062488.

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Word sense disambiguation (WSD) is one of the core problems in natural language processing (NLP), which is to map an ambiguous word to its correct meaning in a specific context. There has been a lively interest in incorporating sense definition (gloss) into neural networks in recent studies, which makes great contribution to improving the performance of WSD. However, disambiguating polysemes of rare senses is still hard. In this paper, while taking gloss into consideration, we further improve the performance of the WSD system from the perspective of semantic representation. We encode the conte

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Zhang, Biao, Deyi Xiong, Jinsong Su, and Hong Duan. "A Context-Aware Recurrent Encoder for Neural Machine Translation." IEEE/ACM Transactions on Audio, Speech, and Language Processing 25, no. 12 (2017): 2424–32. http://dx.doi.org/10.1109/taslp.2017.2751420.

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Du, Tingyu, Jingxiu Ni, and Dongxing Wang. "Fast Context-Awareness Encoder for LiDAR Point Semantic Segmentation." Electronics 12, no. 15 (2023): 3228. http://dx.doi.org/10.3390/electronics12153228.

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A LiDAR sensor is a valuable tool for environmental perception as it can generate 3D point cloud data with reflectivity and position information by reflecting laser beams. However, it cannot provide the meaning of each point cloud cluster, so many studies focus on identifying semantic information about point clouds. This paper explores point cloud segmentation and presents a lightweight convolutional network called Fast Context-Awareness Encoder (FCAE), which can obtain semantic information about the point cloud cluster at different levels. The surrounding features of points are extracted as l

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Yin, Yijun, Wenzheng Xu, Lei Chen, and Hao Wu. "CoT-UNet++: A medical image segmentation method based on contextual transformer and dense connection." Mathematical Biosciences and Engineering 20, no. 5 (2023): 8320–36. http://dx.doi.org/10.3934/mbe.2023364.

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<abstract> <p>Accurate depiction of individual teeth from CBCT images is a critical step in the diagnosis of oral diseases, and the traditional methods are very tedious and laborious, so automatic segmentation of individual teeth in CBCT images is important to assist physicians in diagnosis and treatment. TransUNet has achieved success in medical image segmentation tasks, which combines the advantages of Transformer and CNN. However, the skip connection taken by TransUNet leads to unnecessary restrictive fusion and also ignores the rich context between adjacent keys. To solve these

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Kumar, Satyawant, Abhishek Kumar, and Dong-Gyu Lee. "Semantic Segmentation of UAV Images Based on Transformer Framework with Context Information." Mathematics 10, no. 24 (2022): 4735. http://dx.doi.org/10.3390/math10244735.

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With the advances in Unmanned Aerial Vehicles (UAVs) technology, aerial images with huge variations in the appearance of objects and complex backgrounds have opened a new direction of work for researchers. The task of semantic segmentation becomes more challenging when capturing inherent features in the global and local context for UAV images. In this paper, we proposed a transformer-based encoder-decoder architecture to address this issue for the precise segmentation of UAV images. The inherent feature representation of the UAV images is exploited in the encoder network using a self-attention

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Sybrandt, Justin, and Ilya Safro. "CBAG: Conditional biomedical abstract generation." PLOS ONE 16, no. 7 (2021): e0253905. http://dx.doi.org/10.1371/journal.pone.0253905.

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Biomedical research papers often combine disjoint concepts in novel ways, such as when describing a newly discovered relationship between an understudied gene with an important disease. These concepts are often explicitly encoded as metadata keywords, such as the author-provided terms included with many documents in the MEDLINE database. While substantial recent work has addressed the problem of text generation in a more general context, applications, such as scientific writing assistants, or hypothesis generation systems, could benefit from the capacity to select the specific set of concepts

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Bernabé, Pierre, Helge Spieker, Bruno Legeard, and Arnaud Gotlieb. "Encoding Temporal and Spatial Vessel Context using Self-Supervised Learning Model (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (2021): 15757–58. http://dx.doi.org/10.1609/aaai.v35i18.17875.

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Maritime surveillance is essential to avoid illegal activities and for environmental protection. However, the unlabeled, noisy, irregular time-series data and the large area to be covered make it challenging to detect illegal activities. Existing solutions focus only on trajectory reconstruction and probabilistic models that do ignore the context, such as the neighboring vessels. We propose a novel representation learning method that considers both temporal and spatial contexts learned in a self-supervised manner, using a selection of pretext tasks that do not require to be labeled manually. T

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Wen, F., Y. Zhang, and B. Zhang. "GLOBAL CONTEXT AIDED SEMANTIC SEGMENTATION FOR CLOUD DETECTION OF REMOTE SENSING IMAGES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 583–89. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-583-2020.

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Abstract. Cloud detection is a vital preprocessing step for remote sensing image applications, which has been widely studied through Convolutional Neural Networks (CNNs) in recent years. However, the available CNN-based works only extract local/non-local features by stacked convolution and pooling layers, ignoring global contextual information of the input scenes. In this paper, a novel segmentation-based network is proposed for cloud detection of remote sensing images. We add a multi-class classification branch to a U-shaped semantic segmentation network. Through the encoder-decoder architect

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Guo, Jinyu, Kai Shuang, Kaihang Zhang, Yixuan Liu, Jijie Li, and Zihan Wang. "Learning to Imagine: Distillation-Based Interactive Context Exploitation for Dialogue State Tracking." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 12845–53. http://dx.doi.org/10.1609/aaai.v37i11.26510.

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In dialogue state tracking (DST), the exploitation of dialogue history is a crucial research direction, and the existing DST models can be divided into two categories: full-history models and partial-history models. Since the “select first, use later” mechanism explicitly filters the distracting information being passed to the downstream state prediction, the partial-history models have recently achieved a performance advantage over the full-history models. However, besides the redundant information, some critical dialogue context information was inevitably filtered out by the partial-history

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Sediqi, Khwaja Monib, and Hyo Jong Lee. "A Novel Upsampling and Context Convolution for Image Semantic Segmentation." Sensors 21, no. 6 (2021): 2170. http://dx.doi.org/10.3390/s21062170.

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Semantic segmentation, which refers to pixel-wise classification of an image, is a fundamental topic in computer vision owing to its growing importance in the robot vision and autonomous driving sectors. It provides rich information about objects in the scene such as object boundary, category, and location. Recent methods for semantic segmentation often employ an encoder-decoder structure using deep convolutional neural networks. The encoder part extracts features of the image using several filters and pooling operations, whereas the decoder part gradually recovers the low-resolution feature m

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He, Xiang, Sibei Yang, Guanbin Li, Haofeng Li, Huiyou Chang, and Yizhou Yu. "Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8417–24. http://dx.doi.org/10.1609/aaai.v33i01.33018417.

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Recent progress in biomedical image segmentation based on deep convolutional neural networks (CNNs) has drawn much attention. However, its vulnerability towards adversarial samples cannot be overlooked. This paper is the first one that discovers that all the CNN-based state-of-the-art biomedical image segmentation models are sensitive to adversarial perturbations. This limits the deployment of these methods in safety-critical biomedical fields. In this paper, we discover that global spatial dependencies and global contextual information in a biomedical image can be exploited to defend against

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López-Granado, Otoniel Mario, Miguel Onofre Martínez-Rach, Antonio Martí-Campoy, Marco Antonio Cruz-Chávez, and Manuel Pérez Malumbres. "A General Model for the Design of Efficient Sign-Coding Tools for Wavelet-Based Encoders." Electronics 9, no. 11 (2020): 1899. http://dx.doi.org/10.3390/electronics9111899.

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Traditionally, it has been assumed that the compression of the sign of wavelet coefficients is not worth the effort because they form a zero-mean process. However, several image encoders such as JPEG 2000 include sign-coding capabilities. In this paper, we analyze the convenience of including sign-coding techniques into wavelet-based image encoders and propose a methodology that allows the design of sign-prediction tools for whatever kind of wavelet-based encoder. The proposed methodology is based on the use of metaheuristic algorithms to find the best sign prediction with the most appropriate

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Su, Shaojing, Jing Zhou, Zhiping Huang, Chunwu Liu, and Yimeng Zhang. "Blind Identification of Convolutional Encoder Parameters." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/798612.

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This paper gives a solution to the blind parameter identification of a convolutional encoder. The problem can be addressed in the context of the noncooperative communications or adaptive coding and modulations (ACM) for cognitive radio networks. We consider an intelligent communication receiver which can blindly recognize the coding parameters of the received data stream. The only knowledge is that the stream is encoded using binary convolutional codes, while the coding parameters are unknown. Some previous literatures have significant contributions for the recognition of convolutional encoder

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Bansal, Mani, and D. K. Lobiyal. "Context-based Machine Translation of English-Hindi using CE-Encoder." Journal of Computer Science 17, no. 9 (2021): 827–47. http://dx.doi.org/10.3844/jcssp.2021.827.847.

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Bansal, Mani, and D. K. Lobiyal. "Context-based Machine Translation of English-Hindi using CE-Encoder." Journal of Computer Science 17, no. 9 (2021): 825–43. http://dx.doi.org/10.3844/jcssp.2021.825.843.

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Jing, Bo, Yan Pei, Zheng Qian, Anqi Wang, Siyu Zhu, and Jiayi An. "Missing wind speed data reconstruction with improved context encoder network." Energy Reports 8 (November 2022): 3386–94. http://dx.doi.org/10.1016/j.egyr.2022.02.177.

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Gu, Zaiwang, Jun Cheng, Huazhu Fu, et al. "CE-Net: Context Encoder Network for 2D Medical Image Segmentation." IEEE Transactions on Medical Imaging 38, no. 10 (2019): 2281–92. http://dx.doi.org/10.1109/tmi.2019.2903562.

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Valentino, Marco, Mokanarangan Thayaparan, Deborah Ferreira, and André Freitas. "Hybrid Autoregressive Inference for Scalable Multi-Hop Explanation Regeneration." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (2022): 11403–11. http://dx.doi.org/10.1609/aaai.v36i10.21392.

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Regenerating natural language explanations in the scientific domain has been proposed as a benchmark to evaluate complex multi-hop and explainable inference. In this context, large language models can achieve state-of-the-art performance when employed as cross-encoder architectures and fine-tuned on human-annotated explanations. However, while much attention has been devoted to the quality of the explanations, the problem of performing inference efficiently is largely under studied. Cross-encoders, in fact, are intrinsically not scalable, possessing limited applicability to real-world scenario

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Ali, Wazir, Jay Kumar, Zenglin Xu, Rajesh Kumar, and Yazhou Ren. "Context-Aware Bidirectional Neural Model for Sindhi Named Entity Recognition." Applied Sciences 11, no. 19 (2021): 9038. http://dx.doi.org/10.3390/app11199038.

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Named entity recognition (NER) is a fundamental task in many natural language processing (NLP) applications, such as text summarization and semantic information retrieval. Recently, deep neural networks (NNs) with the attention mechanism yield excellent performance in NER by taking advantage of character-level and word-level representation learning. In this paper, we propose a deep context-aware bidirectional long short-term memory (CaBiLSTM) model for the Sindhi NER task. The model relies upon contextual representation learning (CRL), bidirectional encoder, self-attention, and sequential cond

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Ayoub, Shahnawaz, Yonis Gulzar, Faheem Ahmad Reegu, and Sherzod Turaev. "Generating Image Captions Using Bahdanau Attention Mechanism and Transfer Learning." Symmetry 14, no. 12 (2022): 2681. http://dx.doi.org/10.3390/sym14122681.

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Automatic image caption prediction is a challenging task in natural language processing. Most of the researchers have used the convolutional neural network as an encoder and decoder. However, an accurate image caption prediction requires a model to understand the semantic relationship that exists between the various objects present in an image. The attention mechanism performs a linear combination of encoder and decoder states. It emphasizes the semantic information present in the caption with the visual information present in an image. In this paper, we incorporated the Bahdanau attention mec

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Wu, Feize, Yun Pang, Junyi Zhang, et al. "CoRe: Context-Regularized Text Embedding Learning for Text-to-Image Personalization." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 8 (2025): 8377–85. https://doi.org/10.1609/aaai.v39i8.32904.

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Recent advances in text-to-image personalization have enabled high-quality and controllable image synthesis for user-provided concepts. However, existing methods still struggle to balance identity preservation with text alignment. Our approach is based on the fact that generating prompt-aligned images requires a precise semantic understanding of the prompt, which involves accurately processing the interactions between the new concept and its surrounding context tokens within the CLIP text encoder. To address this, we aim to embed the new concept properly into the input embedding space of the t

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Liang, Yunlong, Fandong Meng, Ying Zhang, Yufeng Chen, Jinan Xu, and Jie Zhou. "Infusing Multi-Source Knowledge with Heterogeneous Graph Neural Network for Emotional Conversation Generation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 15 (2021): 13343–52. http://dx.doi.org/10.1609/aaai.v35i15.17575.

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The success of emotional conversation systems depends on sufficient perception and appropriate expression of emotions. In a real-world conversation, we firstly instinctively perceive emotions from multi-source information, including the emotion flow of dialogue history, facial expressions, and personalities of speakers, and then express suitable emotions according to our personalities, but these multiple types of information are insufficiently exploited in emotional conversation fields. To address this issue, we propose a heterogeneous graph-based model for emotional conversation generation. S

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REN, Xianxiang, Hu LIANG, and Shengrong ZHAO. "An attention mechanism and multi-feature fusion network for medical image segmentation." Proceedings of the Romanian Academy, Series A: Mathematics, Physics, Technical Sciences, Information Science 24, no. 2 (2023): 191–201. http://dx.doi.org/10.59277/pra-ser.a.24.2.11.

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Recently, deep learning has been applied to medical image segmentation. However, existing methods based on deep learning still suffer from several disadvantages, such as blurred edge segmentation of image lesion regions and weak context information extraction. To tackle these problems, this paper proposes an attention mechanism and multi-feature fusion network with the encoder-decoder structure for medical image segmentation. In the proposed network, the convolutional group encoder module and the self-attention module are applied to divide images. The convolutional group encoder uses multiple

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Dhawan, Sanjeev, Kulvinder Singh, Adrian Rabaea, and Amit Batra. "Session centered Recommendation Utilizing Future Contexts in Social Media." Analele Universitatii "Ovidius" Constanta - Seria Matematica 29, no. 3 (2021): 91–104. http://dx.doi.org/10.2478/auom-2021-0036.

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Abstract Session centered recommender systems has emerged as an interesting and challenging topic amid researchers during the past few years. In order to make a prediction in the sequential data, prevailing approaches utilize either left to right design autoregressive or data augmentation methods. As these approaches are used to utilize the sequential information pertaining to user conduct, the information about the future context of an objective interaction is totally ignored while making prediction. As a matter of fact, we claim that during the course of training, the future data after the o

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Liskavets, Barys, Maxim Ushakov, Shuvendu Roy, Mark Klibanov, Ali Etemad, and Shane K. Luke. "Prompt Compression with Context-Aware Sentence Encoding for Fast and Improved LLM Inference." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 23 (2025): 24595–604. https://doi.org/10.1609/aaai.v39i23.34639.

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Large language models (LLMs) have triggered a new stream of research focusing on compressing the context length to reduce the computational cost while ensuring the retention of helpful information for LLMs to answer the given question. Token-based removal methods are one of the most prominent approaches in this direction, but risk losing the semantics of the context caused by intermediate token removal, especially under high compression ratios, while also facing challenges in computational efficiency. In this work, we propose context-aware prompt compression (CPC), a sentence-level prompt comp

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Wen, Ying, Kai Xie, and Lianghua He. "Segmenting Medical MRI via Recurrent Decoding Cell." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12452–59. http://dx.doi.org/10.1609/aaai.v34i07.6932.

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The encoder-decoder networks are commonly used in medical image segmentation due to their remarkable performance in hierarchical feature fusion. However, the expanding path for feature decoding and spatial recovery does not consider the long-term dependency when fusing feature maps from different layers, and the universal encoder-decoder network does not make full use of the multi-modality information to improve the network robustness especially for segmenting medical MRI. In this paper, we propose a novel feature fusion unit called Recurrent Decoding Cell (RDC) which leverages convolutional R

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Sang, Zijiang, Yeqin Shao, and Changyan Xu. "Multi-branch Context Awareness Network for Prostate MRI Segmentation." Journal of Physics: Conference Series 2562, no. 1 (2023): 012008. http://dx.doi.org/10.1088/1742-6596/2562/1/012008.

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Abstract Prostate image segmentation is a precondition for the diagnosis of prostate diseases and subsequent treatment. However, the blurry organ edges and low image contrast make accurate segmentation difficult. In order to overcome the difficulties, this paper proposes an innovative Multi-branch Context Awareness Network (MBCA-Net) for prostate MRI segmentation. MBCA-Net uses the 3D UNet as the backbone network with an encoder-decoder framework. To improve the feature extraction capacity of the network, a multi-branch residual module with different convolution kernels is proposed. To better

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