10 papers
AeroGround: A Comprehensive Benchmark for Aerial-Ground Collaborative Reasoning
Shenghong Yi, Lin Zhang, Muzian Li +6
Vision-language models (VLMs) have been widely employed in understanding and reasoning tasks for unmanned aerial vehicles (UAVs). Existing UAV benchmarks primarily focus on aerial-…
Advancing MLLM-based UAV Image Understanding and Reasoning: A Benchmark and a Training-Free Multi-Agent System
Haoyu Zhang, Shuoxun Zhang, Peng Ye +5
Multimodal Large Language Model (MLLM)-based UAV aerial image understanding and reasoning is essential for aerial intelligence yet poses distinct challenges arising from extreme sc…
EvLIR: Learning Illumination Residuals from Ordered Events for Low-Light Image Enhancement
Haoxian Zhou, Chuanzhi Xu, Langyi Chen +5
Low-light image enhancement is severely ill-posed when the input frame contains missing structure, saturated noise, and weak local contrast. Event cameras provide asynchronous brig…
Position Rebinding Cache Reuse: Replay-Free Visual Revisiting for Interleaved Multimodal Reasoning
Mengzhao Wang, Yanli Ji, Wangmeng Zuo +2
Interleaved multimodal reasoning improves visual grounding by revisiting visual evidence during multi-step generation, yet existing methods typically rely on token replay, repeated…
FracEvent: Event-Camera Simulation via Fractional-Relaxation Pixel Dynamics
Langyi Chen, Chuanzhi Xu, Haoxian Zhou +6
Event cameras asynchronously report brightness changes with microsecond-level temporal resolution, but real event data remain difficult to collect at scale because specialized sens…
MLG-Stereo: ViT Based Stereo Matching with Multi-Stage Local-Global Enhancement
Haoyu Zhang, Jingyi Zhou, Peng Ye +4
With the development of deep learning, ViT-based stereo matching methods have made significant progress due to their remarkable robustness and zero-shot ability. However, due to th…
FreshMem: Brain-Inspired Frequency-Space Hybrid Memory for Streaming Video Understanding
Kangcong Li, Peng Ye, Lin Zhang +4
Transitioning Multimodal Large Language Models (MLLMs) from offline to online streaming video understanding is essential for continuous perception. However, existing methods lack f…
LSTM-MAS: A Long Short-Term Memory Inspired Multi-Agent System for Long-Context Understanding
Yichen Jiang, Jiakang Yuan, Chongjun Tu +2
Effectively processing long contexts remains a fundamental yet unsolved challenge for large language models (LLMs). Existing single-LLM-based methods primarily reduce the context w…
Sequential Token Merging: Revisiting Hidden States
Yan Wen, Peng Ye, Lin Zhang +4
Vision Mambas (ViMs) achieve remarkable success with sub-quadratic complexity, but their efficiency remains constrained by quadratic token scaling with image resolution. While exis…
Consistency-aware Self-Training for Iterative-based Stereo Matching
Jingyi Zhou, Peng Ye, Haoyu Zhang +6
Iterative-based methods have become mainstream in stereo matching due to their high performance. However, these methods heavily rely on labeled data and face challenges with unlabe…