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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.
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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