306 citations · 386 across the 87 of their papers we have counts for
8 papers · 1 filter
Homer: Understanding Long-form Videos with Hierarchical Memory and Agentic Reasoning
Yixin Ji, Fanghua Ye, Juntao Li +5
Multimodal large language models excel on short clips but struggle on hour-long videos in an online setting, where frames are processed incrementally under limited memory. Existing…
MMLongEmbed: Benchmarking Multimodal Embedding Models in Long-Context Scenarios
Haitian Wang, Ruoxi Sun, Quantong Qiu +5
Recent advancements have significantly expanded the theoretical context windows of Multimodal Embedding Models (MEMs). However, larger context windows do not necessarily translate…
OmniSelect: Dynamic Modality-Aware Token Compression for Efficient Omni-modal Large Language Models
Morunliu Yang, Ruotao Xu, Le Li +6
Omnimodal large language models (OmniLLMs) have recently gained increasing attention for unified audio-video understanding. However, processing long multimodal token sequences intr…
MMLongCite: A Benchmark for Evaluating Faithfulness of Long-Context Vision-Language Models
Keyan Zhou, Zecheng Tang, Lingfeng Ming +9
The rapid advancement of long-context vision language models (LCVLMs) has led to a significant expansion of their context windows. However, an extended context window does not guar…
Learning Active Perception via Self-Evolving Preference Optimization for GUI Grounding
Wanfu Wang, Qipeng Huang, Guangquan Xue +2
Vision Language Models (VLMs) have recently achieved significant progress in bridging visual perception and linguistic reasoning. Recently, OpenAI o3 model introduced a zoom-in sea…
OpenThinkIMG: Learning to Think with Images via Visual Tool Reinforcement Learning
Zhaochen Su, Linjie Li, Mingyang Song +8
While humans can flexibly leverage interactive visual cognition for complex problem-solving, enabling Large Vision-Language Models (LVLMs) to learn similarly adaptive behaviors wit…
A Survey: Spatiotemporal Consistency in Video Generation
Zhiyu Yin, Kehai Chen, Xuefeng Bai +7
Video generation aims to produce temporally coherent sequences of visual frames, representing a pivotal advancement in Artificial Intelligence Generated Content (AIGC). Compared to…
StrokeNUWA: Tokenizing Strokes for Vector Graphic Synthesis
Zecheng Tang, Chenfei Wu, Zekai Zhang +8
To leverage LLMs for visual synthesis, traditional methods convert raster image information into discrete grid tokens through specialized visual modules, while disrupting the model…