most citedXR: Cross-Modal Agents for Composed Image Retrieval

1 citations · 1 across the 12 of their papers we have counts for

collaborators
Showing cs.CVShow all

10 papers · 1 filter

cs.CV2026

MechReason: Benchmarking Multi-Image Multi-Hop Reasoning in Mechanical Engineering

Tengyue Wang, Kang An, Chenxu Du +8

Despite significant progress in general visual question answering and cross-modal understanding, multimodal large language models still face a pronounced gap in evaluation for comp…

cs.CV2026

Conversational Human Audio-visual Talking Dialogue Generation

Junhao Song, Lluis Guasch, Xilin He +8

Large-scale dyadic interactive audio-visual dialogue (DIAD) datasets provide fundamental data resources for developing humanoid interactive virtual agents and digital humans. Howev…

cs.CV2026

ParaVT: Taming the Tool Prior Paradox for Parallel Tool Use in Agentic Video Reinforcement Learning

Zuhao Yang, Kaichen Zhang, Sudong Wang +7

Training large multimodal models (LMMs) via reinforcement learning (RL) to natively invoke video-processing tools (e.g., cropping) has become a promising route to long-video unders…

cs.CV2026

Script-a-Video: Deep Structured Audio-visual Captions via Factorized Streams and Relational Grounding

Tencent Hunyuan Team

Advances in Multimodal Large Language Models (MLLMs) are transforming video captioning from a descriptive endpoint into a semantic interface for both video understanding and genera…

cs.CV2026

SVAgent: Storyline-Guided Long Video Understanding via Cross-Modal Multi-Agent Collaboration

Zhongyu Yang, Zuhao Yang, Shuo Zhan +3

Video question answering (VideoQA) is a challenging task that requires integrating spatial, temporal, and semantic information to capture the complex dynamics of video sequences. A…

cs.CV2026

Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling

Keming Wu, Zuhao Yang, Kaichen Zhang +24

Recent visual generation models have made major progress in photorealism, typography, instruction following, and interactive editing, yet they still struggle with spatial reasoning…

cs.CV2026

MultiHaystack: Benchmarking Multimodal Retrieval and Reasoning over 40K Images, Videos, and Documents

Dannong Xu, Zhongyu Yang, Jun Chen +6

Multimodal large language models (MLLMs) achieve strong performance on benchmarks that evaluate text, image, or video understanding separately. However, these settings do not asses…

cs.CV2025

InEx: Hallucination Mitigation via Introspection and Cross-Modal Multi-Agent Collaboration

Zhongyu Yang, Yingfang Yuan, Xuanming Jiang +2

Hallucination remains a critical challenge in large language models (LLMs), hindering the development of reliable multimodal LLMs (MLLMs). Existing solutions often rely on human in…

cs.CV2025

Script: Graph-Structured and Query-Conditioned Semantic Token Pruning for Multimodal Large Language Models

Zhongyu Yang, Dannong Xu, Wei Pang +1

The rapid growth of visual tokens in multimodal large language models (MLLMs) leads to excessive memory consumption and inference latency, especially when handling high-resolution…

cs.CV2025

WikiAutoGen: Towards Multi-Modal Wikipedia-Style Article Generation

Zhongyu Yang, Jun Chen, Dannong Xu +5

Knowledge discovery and collection are intelligence-intensive tasks that traditionally require significant human effort to ensure high-quality outputs. Recent research has explored…