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Journal articles on the topic 'Zero-shot Retrieval'

Author: Grafiati

Published: 6 September 2023

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1

Dutta, Titir, and Soma Biswas. "Generalized Zero-Shot Cross-Modal Retrieval." IEEE Transactions on Image Processing 28, no. 12 (2019): 5953–62. http://dx.doi.org/10.1109/tip.2019.2923287.

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Seo, Sanghyun, and Juntae Kim. "Hierarchical Semantic Loss and Confidence Estimator for Visual-Semantic Embedding-Based Zero-Shot Learning." Applied Sciences 9, no. 15 (2019): 3133. http://dx.doi.org/10.3390/app9153133.

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Traditional supervised learning is dependent on the label of the training data, so there is a limitation that the class label which is not included in the training data cannot be recognized properly. Therefore, zero-shot learning, which can recognize unseen-classes that are not used in training, is gaining research interest. One approach to zero-shot learning is to embed visual data such as images and rich semantic data related to text labels of visual data into a common vector space to perform zero-shot cross-modal retrieval on newly input unseen-class data. This paper proposes a hierarchical

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Wang, Xiao, Craig Macdonald, and Iadh Ounis. "Improving zero-shot retrieval using dense external expansion." Information Processing & Management 59, no. 5 (2022): 103026. http://dx.doi.org/10.1016/j.ipm.2022.103026.

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Kumar, Sanjeev. "Phase retrieval with physics informed zero-shot network." Optics Letters 46, no. 23 (2021): 5942. http://dx.doi.org/10.1364/ol.433625.

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Lin, Kaiyi, Xing Xu, Lianli Gao, Zheng Wang, and Heng Tao Shen. "Learning Cross-Aligned Latent Embeddings for Zero-Shot Cross-Modal Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11515–22. http://dx.doi.org/10.1609/aaai.v34i07.6817.

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Zero-Shot Cross-Modal Retrieval (ZS-CMR) is an emerging research hotspot that aims to retrieve data of new classes across different modality data. It is challenging for not only the heterogeneous distributions across different modalities, but also the inconsistent semantics across seen and unseen classes. A handful of recently proposed methods typically borrow the idea from zero-shot learning, i.e., exploiting word embeddings of class labels (i.e., class-embeddings) as common semantic space, and using generative adversarial network (GAN) to capture the underlying multimodal data structures, as

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Zhang, Haofeng, Yang Long, and Ling Shao. "Zero-shot Hashing with orthogonal projection for image retrieval." Pattern Recognition Letters 117 (January 2019): 201–9. http://dx.doi.org/10.1016/j.patrec.2018.04.011.

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Zhang, Zhaolong, Yuejie Zhang, Rui Feng, Tao Zhang, and Weiguo Fan. "Zero-Shot Sketch-Based Image Retrieval via Graph Convolution Network." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12943–50. http://dx.doi.org/10.1609/aaai.v34i07.6993.

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Zero-Shot Sketch-based Image Retrieval (ZS-SBIR) has been proposed recently, putting the traditional Sketch-based Image Retrieval (SBIR) under the setting of zero-shot learning. Dealing with both the challenges in SBIR and zero-shot learning makes it become a more difficult task. Previous works mainly focus on utilizing one kind of information, i.e., the visual information or the semantic information. In this paper, we propose a SketchGCN model utilizing the graph convolution network, which simultaneously considers both the visual information and the semantic information. Thus, our model can e

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Yang, Fan, Zheng Wang, Jing Xiao, and Shin'ichi Satoh. "Mining on Heterogeneous Manifolds for Zero-Shot Cross-Modal Image Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12589–96. http://dx.doi.org/10.1609/aaai.v34i07.6949.

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Most recent approaches for the zero-shot cross-modal image retrieval map images from different modalities into a uniform feature space to exploit their relevance by using a pre-trained model. Based on the observation that manifolds of zero-shot images are usually deformed and incomplete, we argue that the manifolds of unseen classes are inevitably distorted during the training of a two-stream model that simply maps images from different modalities into a uniform space. This issue directly leads to poor cross-modal retrieval performance. We propose a bi-directional random walk scheme to mining

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Xu, Rui, Zongyan Han, Le Hui, Jianjun Qian, and Jin Xie. "Domain Disentangled Generative Adversarial Network for Zero-Shot Sketch-Based 3D Shape Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (2022): 2902–10. http://dx.doi.org/10.1609/aaai.v36i3.20195.

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Sketch-based 3D shape retrieval is a challenging task due to the large domain discrepancy between sketches and 3D shapes. Since existing methods are trained and evaluated on the same categories, they cannot effectively recognize the categories that have not been used during training. In this paper, we propose a novel domain disentangled generative adversarial network (DD-GAN) for zero-shot sketch-based 3D retrieval, which can retrieve the unseen categories that are not accessed during training. Specifically, we first generate domain-invariant features and domain-specific features by disentangl

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Xu, Xing, Jialin Tian, Kaiyi Lin, Huimin Lu, Jie Shao, and Heng Tao Shen. "Zero-shot Cross-modal Retrieval by Assembling AutoEncoder and Generative Adversarial Network." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 1s (2021): 1–17. http://dx.doi.org/10.1145/3424341.

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Conventional cross-modal retrieval models mainly assume the same scope of the classes for both the training set and the testing set. This assumption limits their extensibility on zero-shot cross-modal retrieval (ZS-CMR), where the testing set consists of unseen classes that are disjoint with seen classes in the training set. The ZS-CMR task is more challenging due to the heterogeneous distributions of different modalities and the semantic inconsistency between seen and unseen classes. A few of recently proposed approaches are inspired by zero-shot learning to estimate the distribution underlyi

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Tursun, Osman, Simon Denman, Sridha Sridharan, Ethan Goan, and Clinton Fookes. "An efficient framework for zero-shot sketch-based image retrieval." Pattern Recognition 126 (June 2022): 108528. http://dx.doi.org/10.1016/j.patcog.2022.108528.

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Ge, Ce, Jingyu Wang, Qi Qi, Haifeng Sun, Tong Xu, and Jianxin Liao. "Semi-transductive Learning for Generalized Zero-Shot Sketch-Based Image Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (2023): 7678–86. http://dx.doi.org/10.1609/aaai.v37i6.25931.

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Sketch-based image retrieval (SBIR) is an attractive research area where freehand sketches are used as queries to retrieve relevant images. Existing solutions have advanced the task to the challenging zero-shot setting (ZS-SBIR), where the trained models are tested on new classes without seen data. However, they are prone to overfitting under a realistic scenario when the test data includes both seen and unseen classes. In this paper, we study generalized ZS-SBIR (GZS-SBIR) and propose a novel semi-transductive learning paradigm. Transductive learning is performed on the image modality to expl

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Liu, Huixia, and Zhihong Qin. "Deep quantization network with visual-semantic alignment for zero-shot image retrieval." Electronic Research Archive 31, no. 7 (2023): 4232–47. http://dx.doi.org/10.3934/era.2023215.

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<abstract><p>Approximate nearest neighbor (ANN) search has become an essential paradigm for large-scale image retrieval. Conventional ANN search requires the categories of query images to been seen in the training set. However, facing the rapid evolution of newly-emerging concepts on the web, it is too expensive to retrain the model via collecting labeled data with the new (unseen) concepts. Existing zero-shot hashing methods choose the semantic space or intermediate space as the embedding space, which ignore the inconsistency of visual space and semantic space and suffer from the

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Dutta, Anjan, and Zeynep Akata. "Semantically Tied Paired Cycle Consistency for Any-Shot Sketch-Based Image Retrieval." International Journal of Computer Vision 128, no. 10-11 (2020): 2684–703. http://dx.doi.org/10.1007/s11263-020-01350-x.

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Abstract Low-shot sketch-based image retrieval is an emerging task in computer vision, allowing to retrieve natural images relevant to hand-drawn sketch queries that are rarely seen during the training phase. Related prior works either require aligned sketch-image pairs that are costly to obtain or inefficient memory fusion layer for mapping the visual information to a semantic space. In this paper, we address any-shot, i.e. zero-shot and few-shot, sketch-based image retrieval (SBIR) tasks, where we introduce the few-shot setting for SBIR. For solving these tasks, we propose a semantically ali

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Geigle, Gregor, Jonas Pfeiffer, Nils Reimers, Ivan Vulić, and Iryna Gurevych. "Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval." Transactions of the Association for Computational Linguistics 10 (2022): 503–21. http://dx.doi.org/10.1162/tacl_a_00473.

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Abstract Current state-of-the-art approaches to cross- modal retrieval process text and visual input jointly, relying on Transformer-based architectures with cross-attention mechanisms that attend over all words and objects in an image. While offering unmatched retrieval performance, such models: 1) are typically pretrained from scratch and thus less scalable, 2) suffer from huge retrieval latency and inefficiency issues, which makes them impractical in realistic applications. To address these crucial gaps towards both improved and efficient cross- modal retrieval, we propose a novel fine-tuni

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Li, Jiangtong, Zhixin Ling, Li Niu, and Liqing Zhang. "Zero-shot sketch-based image retrieval with structure-aware asymmetric disentanglement." Computer Vision and Image Understanding 218 (April 2022): 103412. http://dx.doi.org/10.1016/j.cviu.2022.103412.

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Yuan, Xu, Guangze Wang, Zhikui Chen, and Fangming Zhong. "CHOP: An orthogonal hashing method for zero-shot cross-modal retrieval." Pattern Recognition Letters 145 (May 2021): 247–53. http://dx.doi.org/10.1016/j.patrec.2021.02.016.

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Wang, Bingrui, and Yuan Zhou. "Doodle to Object: Practical Zero-Shot Sketch-Based 3D Shape Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (2023): 2474–82. http://dx.doi.org/10.1609/aaai.v37i2.25344.

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Zero-shot (ZS) sketch-based three-dimensional (3D) shape retrieval (SBSR) is challenging due to the abstraction of sketches, cross-domain discrepancies between two-dimensional sketches and 3D shapes, and ZS-driven semantic knowledge transference from seen to unseen categories. Extant SBSR datasets suffer from lack of data, and no current SBSR methods consider ZS scenarios. In this paper, we contribute a new Doodle2Object (D2O) dataset consisting of 8,992 3D shapes and over 7M sketches spanning 50 categories. Then, we propose a novel prototype contrastive learning (PCL) method that effectively

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Wei, Kun, Cheng Deng, Xu Yang, and Maosen Li. "Incremental Embedding Learning via Zero-Shot Translation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (2021): 10254–62. http://dx.doi.org/10.1609/aaai.v35i11.17229.

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Modern deep learning methods have achieved great success in machine learning and computer vision fields by learning a set of pre-defined datasets. Howerver, these methods perform unsatisfactorily when applied into real-world situations. The reason of this phenomenon is that learning new tasks leads the trained model quickly forget the knowledge of old tasks, which is referred to as catastrophic forgetting. Current state-of-the-art incremental learning methods tackle catastrophic forgetting problem in traditional classification networks and ignore the problem existing in embedding networks, whi

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Huang, Runhui, Yanxin Long, Jianhua Han, et al. "NLIP: Noise-Robust Language-Image Pre-training." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (2023): 926–34. http://dx.doi.org/10.1609/aaai.v37i1.25172.

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Large-scale cross-modal pre-training paradigms have recently shown ubiquitous success on a wide range of downstream tasks, e.g., zero-shot classification, retrieval and image captioning. However, their successes highly rely on the scale and quality of web-crawled data that naturally contain much incomplete and noisy information (e.g., wrong or irrelevant contents). Existing works either design manual rules to clean data or generate pseudo-targets as auxiliary signals for reducing noise impact, which do not explicitly tackle both the incorrect and incomplete challenges at the same time. In this

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Chen, Binghui, and Weihong Deng. "Energy Confused Adversarial Metric Learning for Zero-Shot Image Retrieval and Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8134–41. http://dx.doi.org/10.1609/aaai.v33i01.33018134.

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Deep metric learning has been widely applied in many computer vision tasks, and recently, it is more attractive in zeroshot image retrieval and clustering (ZSRC) where a good embedding is requested such that the unseen classes can be distinguished well. Most existing works deem this ’good’ embedding just to be the discriminative one and thus race to devise powerful metric objectives or hard-sample mining strategies for leaning discriminative embedding. However, in this paper, we first emphasize that the generalization ability is a core ingredient of this ’good’ embedding as well and largely af

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Deng, Cheng, Xinxun Xu, Hao Wang, Muli Yang, and Dacheng Tao. "Progressive Cross-Modal Semantic Network for Zero-Shot Sketch-Based Image Retrieval." IEEE Transactions on Image Processing 29 (2020): 8892–902. http://dx.doi.org/10.1109/tip.2020.3020383.

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Xu, Xing, Huimin Lu, Jingkuan Song, Yang Yang, Heng Tao Shen, and Xuelong Li. "Ternary Adversarial Networks With Self-Supervision for Zero-Shot Cross-Modal Retrieval." IEEE Transactions on Cybernetics 50, no. 6 (2020): 2400–2413. http://dx.doi.org/10.1109/tcyb.2019.2928180.

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Zhao, Honggang, Mingyue Liu, and Mingyong Li. "Feature Fusion and Metric Learning Network for Zero-Shot Sketch-Based Image Retrieval." Entropy 25, no. 3 (2023): 502. http://dx.doi.org/10.3390/e25030502.

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Zero-shot sketch-based image retrieval (ZS-SBIR) is an important computer vision problem. The image category in the test phase is a new category that was not visible in the training stage. Because sketches are extremely abstract, the commonly used backbone networks (such as VGG-16 and ResNet-50) cannot handle both sketches and photos. Semantic similarities between the same features in photos and sketches are difficult to reflect in deep models without textual assistance. To solve this problem, we propose a novel and effective feature embedding model called Attention Map Feature Fusion (AMFF).

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McCartney, Ben, Barry Devereux, and Jesus Martinez-del-Rincon. "A zero-shot deep metric learning approach to Brain–Computer Interfaces for image retrieval." Knowledge-Based Systems 246 (June 2022): 108556. http://dx.doi.org/10.1016/j.knosys.2022.108556.

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Gorbatsevich, V., Y. Vizilter, V. Knyaz, and A. Moiseenko. "SINGLE-SHOT SEMANTIC MATCHER FOR UNSEEN OBJECT DETECTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 379–84. http://dx.doi.org/10.5194/isprs-archives-xlii-2-379-2018.

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In this paper we combine the ideas of image matching, object detection, image retrieval and zero-shot learning for stating and solving the semantic matching problem. Semantic matcher takes two images (test and request) as input and returns detected objects (bounding boxes) on test image corresponding to semantic class represented by request (sample) image. We implement our single-shot semantic matcher CNN architecture based on GoogleNet and YOLO/DetectNet architectures. We propose the detection-by-request training and testing protocols for semantic matching algorithms. We train and test our CN

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Bosselut, Antoine, Ronan Le Bras, and Yejin Choi. "Dynamic Neuro-Symbolic Knowledge Graph Construction for Zero-shot Commonsense Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (2021): 4923–31. http://dx.doi.org/10.1609/aaai.v35i6.16625.

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Understanding narratives requires reasoning about implicit world knowledge related to the causes, effects, and states of situations described in text. At the core of this challenge is how to access contextually relevant knowledge on demand and reason over it. In this paper, we present initial studies toward zero-shot commonsense question answering by formulating the task as inference over dynamically generated commonsense knowledge graphs. In contrast to previous studies for knowledge integration that rely on retrieval of existing knowledge from static knowledge graphs, our study requires comm

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McCartney, Ben, Jesus Martinez-del-Rincon, Barry Devereux, and Brian Murphy. "A zero-shot learning approach to the development of brain-computer interfaces for image retrieval." PLOS ONE 14, no. 9 (2019): e0214342. http://dx.doi.org/10.1371/journal.pone.0214342.

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Tian, Jialin, Xing Xu, Fumin Shen, Yang Yang, and Heng Tao Shen. "TVT: Three-Way Vision Transformer through Multi-Modal Hypersphere Learning for Zero-Shot Sketch-Based Image Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 2370–78. http://dx.doi.org/10.1609/aaai.v36i2.20136.

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In this paper, we study the zero-shot sketch-based image retrieval (ZS-SBIR) task, which retrieves natural images related to sketch queries from unseen categories. In the literature, convolutional neural networks (CNNs) have become the de-facto standard and they are either trained end-to-end or used to extract pre-trained features for images and sketches. However, CNNs are limited in modeling the global structural information of objects due to the intrinsic locality of convolution operations. To this end, we propose a Transformer-based approach called Three-Way Vision Transformer (TVT) to leve

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Zhang, Taolin, Chengyu Wang, Nan Hu, et al. "DKPLM: Decomposable Knowledge-Enhanced Pre-trained Language Model for Natural Language Understanding." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (2022): 11703–11. http://dx.doi.org/10.1609/aaai.v36i10.21425.

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Knowledge-Enhanced Pre-trained Language Models (KEPLMs) are pre-trained models with relation triples injecting from knowledge graphs to improve language understanding abilities.Experiments show that our model outperforms other KEPLMs significantly over zero-shot knowledge probing tasks and multiple knowledge-aware language understanding tasks. To guarantee effective knowledge injection, previous studies integrate models with knowledge encoders for representing knowledge retrieved from knowledge graphs. The operations for knowledge retrieval and encoding bring significant computational burdens,

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Zhao, Yuying, Hanjiang Lai, Jian Yin, et al. "Zero-Shot Medical Image Retrieval for Emerging Infectious Diseases Based on Meta-Transfer Learning — Worldwide, 2020." China CDC Weekly 2, no. 52 (2020): 1004–8. http://dx.doi.org/10.46234/ccdcw2020.268.

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Gholami, Sia, and Mehdi Noori. "You Don’t Need Labeled Data for Open-Book Question Answering." Applied Sciences 12, no. 1 (2021): 111. http://dx.doi.org/10.3390/app12010111.

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Open-book question answering is a subset of question answering (QA) tasks where the system aims to find answers in a given set of documents (open-book) and common knowledge about a topic. This article proposes a solution for answering natural language questions from a corpus of Amazon Web Services (AWS) technical documents with no domain-specific labeled data (zero-shot). These questions have a yes–no–none answer and a text answer which can be short (a few words) or long (a few sentences). We present a two-step, retriever–extractor architecture in which a retriever finds the right documents an

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Schwenk, Holger, Douwe Kiela, and Matthijs Douze. "Analysis of Joint Multilingual Sentence Representations and Semantic K-Nearest Neighbor Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6982–90. http://dx.doi.org/10.1609/aaai.v33i01.33016982.

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Multilingual sentence and document representations are becoming increasingly important. We build on recent advances in multilingual sentence encoders, with a focus on efficiency and large-scale applicability. Specifically, we construct and investigate the k-nn graph over the joint space of 566 million news sentences in seven different languages. We show excellent multilingual retrieval quality on the UN corpus of 11.3M sentences, which extends to the zero-shot case where we have never seen a language. We provide a detailed analysis of both the multilingual sentence encoder for twenty-one Europ

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Dong, Shuai, Zhihua Yang, Wensheng Li, and Kun Zou. "Dynamic Detection and Recognition of Objects Based on Sequential RGB Images." Future Internet 13, no. 7 (2021): 176. http://dx.doi.org/10.3390/fi13070176.

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Conveyors are used commonly in industrial production lines and automated sorting systems. Many applications require fast, reliable, and dynamic detection and recognition for the objects on conveyors. Aiming at this goal, we design a framework that involves three subtasks: one-class instance segmentation (OCIS), multiobject tracking (MOT), and zero-shot fine-grained recognition of 3D objects (ZSFGR3D). A new level set map network (LSMNet) and a multiview redundancy-free feature network (MVRFFNet) are proposed for the first and third subtasks, respectively. The level set map (LSM) is used to ann

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Lin, Sheng-Chieh, Minghan Li, and Jimmy Lin. "Aggretriever: A Simple Approach to Aggregate Textual Representations for Robust Dense Passage Retrieval." Transactions of the Association for Computational Linguistics 11 (2023): 436–52. http://dx.doi.org/10.1162/tacl_a_00556.

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Abstract Pre-trained language models have been successful in many knowledge-intensive NLP tasks. However, recent work has shown that models such as BERT are not “structurally ready” to aggregate textual information into a [CLS] vector for dense passage retrieval (DPR). This “lack of readiness” results from the gap between language model pre-training and DPR fine-tuning. Previous solutions call for computationally expensive techniques such as hard negative mining, cross-encoder distillation, and further pre-training to learn a robust DPR model. In this work, we instead propose to fully exploit

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Wang, Zixiang, Tongliang Li, and Zhoujun Li. "Unsupervised Numerical Information Extraction via Exploiting Syntactic Structures." Electronics 12, no. 9 (2023): 1977. http://dx.doi.org/10.3390/electronics12091977.

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Numerical information plays an important role in various fields such as scientific, financial, social, statistics, and news. Most prior studies adopt unsupervised methods by designing complex handcrafted pattern-matching rules to extract numerical information, which can be difficult to scale to the open domain. Other supervised methods require extra time, cost, and knowledge to design, understand, and annotate the training data. To address these limitations, we propose QuantityIE, a novel approach to extracting numerical information as structured representations by exploiting syntactic feature

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UEKI, Kazuya, Koji HIRAKAWA, Kotaro KIKUCHI, and Tetsunori KOBAYASHI. "Zero-Shot Video Retrieval from a Query Phrase Including Multiple Concepts —Efforts and Challenges in TRECVID AVS Task—." Journal of the Japan Society for Precision Engineering 84, no. 12 (2018): 983–90. http://dx.doi.org/10.2493/jjspe.84.983.

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Hendricks, Lisa Anne, John Mellor, Rosalia Schneider, Jean-Baptiste Alayrac, and Aida Nematzadeh. "Decoupling the Role of Data, Attention, and Losses in Multimodal Transformers." Transactions of the Association for Computational Linguistics 9 (2021): 570–85. http://dx.doi.org/10.1162/tacl_a_00385.

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Abstract Recently, multimodal transformer models have gained popularity because their performance on downstream tasks suggests they learn rich visual-linguistic representations. Focusing on zero-shot image retrieval tasks, we study three important factors that can impact the quality of learned representations: pretraining data, the attention mechanism, and loss functions. By pretraining models on six datasets, we observe that dataset noise and language similarity to our downstream task are important indicators of model performance. Through architectural analysis, we learn that models with a mu

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Kern, Stefan, and Gunnar Spreen. "Uncertainties in Antarctic sea-ice thickness retrieval from ICESat." Annals of Glaciology 56, no. 69 (2015): 107–19. http://dx.doi.org/10.3189/2015aog69a736.

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AbstractA sensitivity study was carried out for the lowest-level elevation method to retrieve total (sea ice + snow) freeboard from Ice, Cloud and land Elevation Satellite (ICESat) elevation measurements in the Weddell Sea, Antarctica. Varying the percentage (P) of elevations used to approximate the instantaneous sea-surface height can cause widespread changes of a few to ˃10cm in the total freeboard obtained. Other input parameters have a smaller influence on the overall mean total freeboard but can cause large regional differences. These results, together with published ICESat elevation prec

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Glavaš, Goran, and Swapna Somasundaran. "Two-Level Transformer and Auxiliary Coherence Modeling for Improved Text Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7797–804. http://dx.doi.org/10.1609/aaai.v34i05.6284.

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Breaking down the structure of long texts into semantically coherent segments makes the texts more readable and supports downstream applications like summarization and retrieval. Starting from an apparent link between text coherence and segmentation, we introduce a novel supervised model for text segmentation with simple but explicit coherence modeling. Our model – a neural architecture consisting of two hierarchically connected Transformer networks – is a multi-task learning model that couples the sentence-level segmentation objective with the coherence objective that differentiates correct s

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Jang, Jiho, Chaerin Kong, DongHyeon Jeon, Seonhoon Kim, and Nojun Kwak. "Unifying Vision-Language Representation Space with Single-Tower Transformer." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (2023): 980–88. http://dx.doi.org/10.1609/aaai.v37i1.25178.

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Contrastive learning is a form of distance learning that aims to learn invariant features from two related representations. In this work, we explore the hypothesis that an image and caption can be regarded as two different views of the underlying mutual information, and train a model to learn a unified vision-language representation space that encodes both modalities at once in a modality-agnostic manner. We first identify difficulties in learning a one-tower model for vision-language pretraining (VLP), and propose One Representation (OneR) as a simple yet effective framework for our goal. We

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Niu, Yilin, Fei Huang, Wei Liu, Jianwei Cui, Bin Wang, and Minlie Huang. "Bridging the Gap between Synthetic and Natural Questions via Sentence Decomposition for Semantic Parsing." Transactions of the Association for Computational Linguistics 11 (2023): 367–83. http://dx.doi.org/10.1162/tacl_a_00552.

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Abstract Semantic parsing maps natural language questions into logical forms, which can be executed against a knowledge base for answers. In real-world applications, the performance of a parser is often limited by the lack of training data. To facilitate zero-shot learning, data synthesis has been widely studied to automatically generate paired questions and logical forms. However, data synthesis methods can hardly cover the diverse structures in natural languages, leading to a large gap in sentence structure between synthetic and natural questions. In this paper, we propose a decomposition-ba

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Lazaridou, Angeliki, Georgiana Dinu, Adam Liska, and Marco Baroni. "From Visual Attributes to Adjectives through Decompositional Distributional Semantics." Transactions of the Association for Computational Linguistics 3 (December 2015): 183–96. http://dx.doi.org/10.1162/tacl_a_00132.

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As automated image analysis progresses, there is increasing interest in richer linguistic annotation of pictures, with attributes of objects (e.g., furry, brown…) attracting most attention. By building on the recent “zero-shot learning” approach, and paying attention to the linguistic nature of attributes as noun modifiers, and specifically adjectives, we show that it is possible to tag images with attribute-denoting adjectives even when no training data containing the relevant annotation are available. Our approach relies on two key observations. First, objects can be seen as bundles of attri

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Vijayaraghavan, Prashanth, and Deb Roy. "M-sense: Modeling Narrative Structure in Short Personal Narratives Using Protagonist’s Mental Representations." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 13664–72. http://dx.doi.org/10.1609/aaai.v37i11.26601.

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Narrative is a ubiquitous component of human communication. Understanding its structure plays a critical role in a wide variety of applications, ranging from simple comparative analyses to enhanced narrative retrieval, comprehension, or reasoning capabilities. Prior research in narratology has highlighted the importance of studying the links between cognitive and linguistic aspects of narratives for effective comprehension. This interdependence is related to the textual semantics and mental language in narratives, referring to characters' motivations, feelings or emotions, and beliefs. However

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Li, Xiaoyu, Weihong Wang, Jifei Fang, Li Jin, Hankun Kang, and Chunbo Liu. "PEINet: Joint Prompt and Evidence Inference Network via Language Family Policy for Zero-Shot Multilingual Fact Checking." Applied Sciences 12, no. 19 (2022): 9688. http://dx.doi.org/10.3390/app12199688.

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Zero-shot multilingual fact-checking, which aims to discover and infer subtle clues from the retrieved relevant evidence to verify the given claim in cross-language and cross-domain scenarios, is crucial for optimizing a free, trusted, wholesome global network environment. Previous works have made enlightening and practical explorations in claim verification, while the zero-shot multilingual task faces new challenging gap issues: neglecting authenticity-dependent learning between multilingual claims, lacking heuristic checking, and a bottleneck of insufficient evidence. To alleviate these gaps

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Wang , Ping, Li Sun, Liuan Wang, and Jun Sun. "Zero-Shot Video Grounding for Automatic Video Understanding in Sustainable Smart Cities." Sustainability 15, no. 1 (2022): 153. http://dx.doi.org/10.3390/su15010153.

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Automatic video understanding is a crucial piece of technology which promotes urban sustainability. Video grounding is a fundamental component of video understanding that has been evolving quickly in recent years, but its use is restricted due to the high labeling costs and typical performance limitations imposed by the pre-defined training dataset. In this paper, a novel atom-based zero-shot video grounding (AZVG) method is proposed to retrieve the segments in the video that correspond to a given input sentence. Although it is training-free, the performance of AZVG is competitive to the weakl

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Nakata, Nori, and Gregory C. Beroza. "Reverse time migration for microseismic sources using the geometric mean as an imaging condition." GEOPHYSICS 81, no. 2 (2016): KS51—KS60. http://dx.doi.org/10.1190/geo2015-0278.1.

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Time reversal is a powerful tool used to image directly the location and mechanism of passive seismic sources. This technique assumes seismic velocities in the medium and propagates time-reversed observations of ground motion at each receiver location. Assuming an accurate velocity model and adequate array aperture, the waves will focus at the source location. Because we do not know the location and the origin time a priori, we need to scan the entire 4D image (3D in space and 1D in time) to localize the source, which makes time-reversal imaging computationally demanding. We have developed a n

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Long, Yang, Li Liu, Yuming Shen, and Ling Shao. "Towards Affordable Semantic Searching: Zero-Shot Retrieval via Dominant Attributes." Proceedings of the AAAI Conference on Artificial Intelligence 32, no. 1 (2018). http://dx.doi.org/10.1609/aaai.v32i1.12280.

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Instance-level retrieval has become an essential paradigm to index and retrieves images from large-scale databases. Conventional instance search requires at least an example of the query image to retrieve images that contain the same object instance. Existing semantic retrieval can only search semantically-related images, such as those sharing the same category or a set of tags, not the exact instances. Meanwhile, the unrealistic assumption is that all categories or tags are known beforehand. Training models for these semantic concepts highly rely on instance-level attributes or human captions

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Zou, Qin, Ling Cao, Zheng Zhang, Long Chen, and Song Wang. "Transductive Zero-Shot Hashing for Multilabel Image Retrieval." IEEE Transactions on Neural Networks and Learning Systems, 2020, 1–15. http://dx.doi.org/10.1109/tnnls.2020.3043298.

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Liu, Cong, Wenhao She, Minjie Chen, Xiaofang Li, and Simon X. Yang. "Consistent penalizing field loss for zero-shot image retrieval." Expert Systems with Applications, August 2023, 121287. http://dx.doi.org/10.1016/j.eswa.2023.121287.

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