1 citations · 1 across the 10 of their papers we have counts for
11 papers
CausalSplat: Towards Comprehensive Hierarchical Reasoning in 3D Gaussian Splatting
Jiayu Ding, Meilu Song, Yun Chen +2
While 3D Gaussian Splatting (3DGS) has advanced open vocabulary scene understanding, existing methods remain confined to explicit queries. They struggle to interpret implicit inten…
EFlow: Learning Evidence Flow for Long-Video Reasoning with Adaptive Reflection
Wenhao Zhang, Kuanwei Lin, Xuyi Yang +2
Long-video reasoning is fundamentally constrained by how models acquire and utilize visual evidence. Existing tool-augmented video frameworks often interleave temporal grounding an…
Cognitive-structured Multimodal Agent for Multimodal Understanding, Generation, and Editing
Feng Wang, Canmiao Fu, Zhipeng Huang +3
Recent unified multimodal models show a single architecture can jointly perform vision/language understanding and image generation/editing. However, they repeatedly feed all histor…
VideoRouter: Query-Adaptive Dual Routing for Efficient Long-Video Understanding
Kuanwei Lin, Wenhao Zhang, Ge Li
Video large multimodal models increasingly face a scalability bottleneck: long videos produce excessively long visual-token sequences, which sharply increase memory and latency dur…
3D Instruction Ambiguity Detection
Jiayu Ding, Haoran Tang, Hongbo Jin +2
In safety-critical domains, linguistic ambiguity can have severe consequences; a vague command like "Pass me the vial" in a surgical setting could lead to catastrophic errors. Yet,…
Video Spatial Reasoning with Object-Centric 3D Rollout
Haoran Tang, Meng Cao, Ruyang Liu +4
Recent advances in Multi-modal Large Language Models (MLLMs) have showcased remarkable capabilities in vision-language understanding. However, enabling robust video spatial reasoni…
Flow4Agent: Long-form Video Understanding via Motion Prior from Optical Flow
Ruyang Liu, Shangkun Sun, Haoran Tang +2
Long-form video understanding has always been a challenging problem due to the significant redundancy in both temporal and spatial contents. This challenge is further exacerbated b…
VISTA: Mitigating Semantic Inertia in Video-LLMs via Training-Free Dynamic Chain-of-Thought Routing
Hongbo Jin, Jiayu Ding, Siyi Xie +2
Recent advancements in Large Language Models have successfully transitioned towards System 2 reasoning, yet applying these paradigms to video understanding remains challenging. Whi…
MUSE: Mamba is Efficient Multi-scale Learner for Text-video Retrieval
Haoran Tang, Meng Cao, Jinfa Huang +4
Text-Video Retrieval (TVR) aims to align and associate relevant video content with corresponding natural language queries. Most existing TVR methods are based on large-scale pre-tr…
RAP: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter
Meng Cao, Haoran Tang, Jinfa Huang +7
Text-Video Retrieval (TVR) aims to align relevant video content with natural language queries. To date, most state-of-the-art TVR methods learn image-to-video transfer learning bas…
ST-LLM: Large Language Models Are Effective Temporal Learners
Ruyang Liu, Chen Li, Haoran Tang +3
Large Language Models (LLMs) have showcased impressive capabilities in text comprehension and generation, prompting research efforts towards video LLMs to facilitate human-AI inter…