9 papers · 1 filter
Not All Error Yields to Scale: Where Scaling Stops in Vision-Language Inference
Xinye Zhao, Yunkai Dang, Yunchen Wu +1
Vision-language models (VLMs) face a fixed-budget trade-off between processing more visual information for fine-grained perception and using a larger language backbone for complex…
Understanding Dynamic Scenes at Gigapixel Scale: Wide-Area Spatio-Temporal Perception from UAVs
Yuhang Zhu, Meiyi Zhu, Yunkai Dang +4
UAV-borne imaging has advanced from megapixel to gigapixel sensors, shifting aerial perception from recognizing individual targets to understanding entire dynamic scenes. We charac…
BUS: Brain-Inspired Unsupervised Self-Reflection via Backward Prediction for Multimodal Reasoning
Jiacheng Yang, Tongying Xiao, Yunkai Dang +7
Current Vision-Language Models (VLMs) often struggle to handle complex visual tasks that require consistent and fine-grained reasoning. Recent methods aim to train models to facili…
Instinct vs. Reflection: Unifying Token and Verbalized Confidence in Multimodal Large Models
Yunkai Dang, Yifan Jiang, Yizhu Jiang +3
Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in various perception and reasoning tasks. Despite this success, ensuring their reliability in p…
UHR-BAT: Budget-Aware Token Compression Vision-Language model for Ultra-High-Resolution Remote Sensing
Yunkai Dang, Minxin Dai, Yuekun Yang +4
Ultra-high-resolution (UHR) remote sensing imagery couples kilometer-scale context with query-critical evidence that may occupy only a few pixels. Such vast spatial scale leads to…
CLASP: Class-Adaptive Layer Fusion and Dual-Stage Pruning for Multimodal Large Language Models
Yunkai Dang, Yizhu Jiang, Yifan Jiang +4
Multimodal Large Language Models (MLLMs) suffer from substantial computational overhead due to the high redundancy in visual token sequences. Existing approaches typically address…
HART: High-Resolution Annotation-Free Reasoning Technique through a Closed-loop Framework
Jiacheng Yang, Anqi Chen, Yunkai Dang +5
Current Large Multimodal Models (LMMs) struggle with high-resolution visual inputs during the reasoning process, as the number of image tokens increases quadratically with resoluti…
FUSE-RSVLM: Feature Fusion Vision-Language Model for Remote Sensing
Yunkai Dang, Donghao Wang, Jiacheng Yang +7
Large vision-language models (VLMs) exhibit strong performance across various tasks. However, these VLMs encounter significant challenges when applied to the remote sensing domain…
A Benchmark for Ultra-High-Resolution Remote Sensing MLLMs
Yunkai Dang, Meiyi Zhu, Donghao Wang +7
Multimodal large language models (MLLMs) demonstrate strong perception and reasoning performance on existing remote sensing (RS) benchmarks. However, most prior benchmarks rely on…