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- Deconfounded Video Moment Retrieval with Causal Intervention248
A causality-inspired VMR framework is proposed that builds structural causal model to capture the true effect of query and video content on the prediction, and a Deconfounded Cross-modal Matching method is developed to remove the confounding effects of moment location.
- Boundary Proposal Network for Two-stage Natural Language Video Localization196
A novel Boundary Proposal Network (BPNet) is proposed, a universal two-stage framework that gets rid of the issues mentioned above and outperforms the state-of-the-art NLVL methods.
- Invariant Grounding for Video Question Answering128
A new learning framework is proposed, Invariant Grounding for VideoQA (IGV), to ground the question-critical scene, whose causal relations with answers are invariant across different interventions on the complement, which significantly improves the reasoning ability.
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- NExT-Chat: An LMM for Chat, Detection and Segmentation96
This paper introduces a novel paradigm for object location modeling called pix2emb method, where the LMM is asked to output the location embeddings and then decode them with different decoders and demonstrates its capability of handling multiple tasks like visual grounding, region captioning, and grounded reasoning.
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- Towards Natural Language-Guided Drones: GeoText-1652 Benchmark with Spatial Relation Matching68
GeoText-1652 is introduced, a new natural language-guided geo-localization benchmark that extends the established University-1652 image dataset with spatial-aware text annotations, thereby establishing one-to-one correspondences between image, text, and bounding box elements.
- Content-Variant Reference Image Quality Assessment via Knowledge Distillation60
The content-variant reference method via knowledge distillation (CVRKD-IQA) is proposed, which uses non-aligned reference (NAR) images to introduce various prior distributions of high-quality images and can support more IQA applications with its robustness to content variations.
- Dysen-VDM: Empowering Dynamics-Aware Text-to-Video Diffusion with LLMs56
An innovative dynamic scene manager module, Inspired by human intuition, which includes an innovative dynamic scene manager (dubbed as Dysen) module, integrated into the backbone T2V DM for video generating, and realizes (nearly) human-level temporal dynamics understanding.
- Video Visual Relation Detection via Iterative Inference55
This work derives their model from the viewpoint of joint relation classification which is light-weight yet effective, and proposes a training approach to better learn the dependency knowledge from the likely correct triplet combinations.
- Online Distillation-enhanced Multi-modal Transformer for Sequential Recommendation45
This paper proposes a new model-agnostic framework for multi-modal sequential recommendation tasks, called Online Distillation-enhanced Multi- modal Transformer (ODMT), to enhance feature interaction and mutual learning among multi-source input (ID, text, and image), while avoiding conflicts among different features during training, thereby improving recommendation accuracy.
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- Rethinking the Two-Stage Framework for Grounded Situation Recognition39
A novel SituFormer for GSR which consists of a Coarse-to-Fine Verb Model (CFVM) and a Transformer-based Noun Model (TNM), which is a transformer-based semantic role detection model, which detects all roles parallelly.
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- Biased-Predicate Annotation Identification via Unbiased Visual Predicate Representation33
This work proposes a novel method that utilizes unbiased visual predicate representations for Biased-Annotation Identification (BAI) as a fundamental step for PSG/SGG tasks and shows great generalization and effectiveness on multiple datasets.
- Semantic Locality-Aware Deformable Network for Clothing Segmentation33
A semantic locality-aware segmentation model is proposed, which adaptively attaches an original clothing image with a semantically similar auxiliary exemplar by search to make the learning process of small sample learning more stable and tractable.
- Semantic Alignment for Multimodal Large Language Models31
Semantic Alignment for Multi-modal large language models (SAM) is introduced, which aims to enhance the preservation of linking information for coherent analysis and align the semantics of different images before feeding them into LLM.
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- Turing Patterns for Multimedia: Reaction-Diffusion Multi-Modal Fusion for Language-Guided Video Moment Retrieval26
Reaction-Diffusion Multimodal Fusion (RDMF) is proposed, a novel framework that reimagines video-language alignment as a reaction-diffusion process, drawing on the principles of pattern formation introduced by Alan Turing, and represents a pioneering interdisciplinary approach.
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- Mrtnet: Multi-Resolution Temporal Network for Video Sentence Grounding25
This work introduces MRTNet, a multi-resolution grounding network with four key components: a feature encoder, a Multi-Resolution Temporal (MRT) module, a Query-aware Attention (QAM) module, and a predictor.
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- Multi-queue Momentum Contrast for Microvideo-Product Retrieval24
The microvideo-product retrieval task is formulated, which is the first attempt to explore the retrieval between the multi-modal and multi- modal instances, and a novel approach named Multi-Queue Momentum Contrast (MQMC) network is proposed for bidirectional retrieval.
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 2026-10-12. 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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