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- Dual Encoding for Zero-Example Video Retrieval305
This paper takes a concept-free approach, proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own and establishes a new state-of-the-art for zero-example video retrieval.
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- Context-aware Biaffine Localizing Network for Temporal Sentence Grounding195
A novel localization framework that scores all pairs of start and end indices within the video simultaneously with a biaffine mechanism that significantly outperforms state-of-thearts on three public datasets, demonstrating the effectiveness of the proposed localization framework.
- Tree-Augmented Cross-Modal Encoding for Complex-Query Video Retrieval155
A Tree-augmented Cross-modal Encoding method by jointly learning the linguistic structure of queries and the temporal representation of videos to facilitate video retrieval with complex queries, thereby achieving a better video retrieval performance.
- W2VV++153
With W2VV++, a super version of Word2VisualVec previously developed for visual-to-text matching, a new baseline for ad-hoc video search is established, which outperforms the state-of-the-art.
- Jointly Cross- and Self-Modal Graph Attention Network for Query-Based Moment Localization152
A novel Cross- and Self-Modal Graph Attention Network (CSMGAN) is proposed that recasts this task as a process of iterative messages passing over a joint graph that is able to effectively capture high-order interactions between two modalities, thus enabling a further precise localization.
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- Exploring Human-Like Attention Supervision in Visual Question Answering117
The experiments show that adding human-like attention supervision to an attention-based VQA model yields a more accurate attention together with a better performance, showing a promising future for human- like attention supervision in V QA.
- Fluency-Guided Cross-Lingual Image Captioning109
The proposed framework comprises a module to automatically estimate the fluency of the sentences and another module to utilize the estimated fluency scores to effectively train an image captioning model for the target language.
- Fine-grained Iterative Attention Network for Temporal Language Localization in Videos106
A Fine-grained Iterative Attention Network (FIAN) that consists of an iterative attention module for bilateral query-video in-formation extraction and a content-oriented localization strategy instead of applying recent anchor-based localization is proposed.
- Adding Chinese Captions to Images105
The study reveals to some extent that a computer can master two distinct languages, English and Chinese, at a similar level for describing the visual world.
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- Reading-Strategy Inspired Visual Representation Learning for Text-to-Video Retrieval84
This paper aims for the task of text-to-video retrieval, where given a query in the form of a natural-language sentence, it is asked to retrieve videos which are semantically relevant to the given query, from a great number of unlabeled videos.
- Partially Relevant Video Retrieval83
A Multi-Scale Similarity Learning (MS-SL) network is proposed that jointly learns clip-scale and frame-scale similarities for PRVR, a novel T2VR subtask termed Partially Relevant Video Retrieval (PRVR), which is formulated as a multiple instance learning (MIL) problem.
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- Early Embedding and Late Reranking for Video Captioning77
The proposed solution for the MSR Video to Language Challenge is ranked at the 4th place in terms of overall performance, while scoring the best CIDEr-D, which measures the human-likeness of generated captions.
- Adaptive Proposal Generation Network for Temporal Sentence Localization in Videos76
This paper proposes an Adaptive Proposal Generation Network (APGN) to maintain the segment-level interaction while speeding up the efficiency and significantly outperforms previous state-of-the-art methods on three challenging benchmarks.
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- Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Network61
An Attribute-Specific Embedding Network (ASEN) is proposed to jointly learn multiple attribute-specific embeddings in an end-to-end manner, thus measure the fine-grained similarity in the corresponding space.
- Lightweight Attentional Feature Fusion: A New Baseline for Text-to-Video Retrieval58
LAFF is proposed, a new baseline for text-to-video retrieval that performs feature fusion at both early and late stages and at both video and text ends, making it a powerful method for exploiting diverse (off-the-shelf) features.
- Not All Inputs Are Valid: Towards Open-Set Video Moment Retrieval using Language49
This paper proposes a novel model OpenVMR, which first distinguishes ID and OOD queries based on the normalizing flow technology, and then conducts moment retrieval based on ID queries, and first distinguishes ID and OOD queries based on the normalizing flow technology, and then conducts moment retrieval based on ID queries.
- Fine-Grained Fashion Similarity Prediction by Attribute-Specific Embedding Learning49
Extensive experiments on three fashion-related datasets, i.e., FashionAI, DARN, and DeepFashion, show the effectiveness of ASEN for fine-grained fashion similarity prediction and its potential for fashion reranking.
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- Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding46
A Unified Multimodal Unsupervised Representation Learning framework, called UmURL, which exploits an efficient early-fusion strategy to jointly encode the multi-modal features in a single-stream manner, and is able to learn the unified representations of uni- modal or multi-Modal skeleton input.
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- Rethinking Weakly-Supervised Video Temporal Grounding From a Game Perspective42
This is the first attempt to tackle the challenging task of weakly-supervised video temporal grounding from a novel game perspective, which effectively learns the uncertain relationship between each vision-language pair with diverse granularity and flexible combination for multi-level cross-modal interaction.
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- Reasoning Step-by-Step: Temporal Sentence Localization in Videos via Deep Rectification-Modulation Network40
A novel deep rectification-modulation network (RMN) is proposed, transforming this task into a multi-step reasoning process by repeating rectification and modulation, thus enabling a further precise localization of temporal sentence localization in videos.
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- Reduction of the Impedance of a Contactless Conductivity Detector for Microchip Capillary Electrophoresis: Compensation of the Electrode Impedance by Addition of a Series Inductance from a Piezoelectric Quartz Crystal33
A low-impedance capacitively coupled contactless conductivity detector for microchip capillary electrophoresis was reported and it was shown that the sensitivity of LIC (4)D was much higher than that of C ( 4)D itself, especially in the microchip with a thick lid.
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- Multimodal Language Models See Better When They Look Shallower28
This work offers the first principled study of visual layer selection in MLLMs, showing that MLLMs can often see better when they look shallower, and proposes a lightweight feature fusion method that strategically incorporates shallower layers, achieving consistent improvements over both single-layer and specialized fusion baselines.
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- Unsupervised Domain Adaptative Temporal Sentence Localization with Mutual Information Maximization26
This paper proposes an effective and novel baseline for UDA-TSL to bridge the multi-modal gap across different domains and learns the potential correspondence between the video-query pairs in target domain, and develops separate modality-specific domain adaptation modules to smoothly balance the minimization of the domain shifts in cross-dataset video and query domains.
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- Graph Pooling Inference Network for Text-based VQA24
GPIN is a dual-path hierarchical graph architecture that progressively aggregates the evolved object graph and the evolved token graph semantics into a graph vector that serves as visual cues to facilitate the answer reasoning and can effectively eliminate object redundancy and enhance the association of semantically continuous tokens.
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- Filling the Information Gap between Video and Query for Language-Driven Moment Retrieval16
A Teacher-Student Moment Retrieval framework to fill the cross-modal information gap during the moment retrieval guided by a single query and introduces an adaptive knowledge distillation mechanism to train a student model with a single queries input by selectively absorbing the knowledge from the teacher model.
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- Transform-Equivariant Consistency Learning for Temporal Sentence Grounding13
A novel Equivariant Consistency Regulation Learning (ECRL) framework is introduced to learn more discriminative query-related frame-wise representations for each video, in a self-supervised manner, to address the temporal sentence grounding.
- Semantics-Enriched Cross-Modal Alignment for Complex-Query Video Moment Retrieval13
This paper presents a novel solution termed semantics-enriched video moment retrieval method (SVMR), which can effectively and explicitly model the hierarchical multi-granularity semantics of complex textual query and explores cross-token relations to offer multiple granularity query representations with hierarchical semantic contexts of semantically associated tokens.
- Lite-MKD: A Multi-modal Knowledge Distillation Framework for Lightweight Few-shot Action Recognition13
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- IVCR-200K: A Large-Scale Multi-turn Dialogue Benchmark for Interactive Video Corpus Retrieval6
The Interactive Video Corpus Retrieval task is introduced, a more realistic setting that enables multi-turn, conversational, and realistic interactions between the user and the retrieval system, and a comprehensive framework based on multi-modal large language models (MLLMs) is proposed to help users interact in several modes with more explainable solutions.
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- Video Infringement Detection via Feature Disentanglement and Mutual Information Maximization1
This paper proposes to disentangle an original high-dimensional feature into multiple sub-features, explicitly disentangling the feature into exclusive lower-dimensional components, and theoretically analyzed the mutual information between the label and the disentangled features, arriving at a loss that maximizes the extraction of task-relevant information from the original feature.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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