6 papers
Depth Anything at Any Condition
Boyuan Sun, Modi Jin, Bowen Yin +1
We present Depth Anything at Any Condition (DepthAnything-AC), a foundation monocular depth estimation (MDE) model capable of handling diverse environmental conditions. Previous fo…
LLaVA-Scissor: Token Compression with Semantic Connected Components for Video LLMs
Boyuan Sun, Jiaxing Zhao, Xihan Wei +1
In this paper, we present LLaVA-Scissor, a training-free token compression strategy designed for video multimodal large language models. Previous methods mostly attempt to compress…
HumanOmniV2: From Understanding to Omni-Modal Reasoning with Context
Qize Yang, Shimin Yao, Weixuan Chen +7
With the rapid evolution of multimodal large language models, the capacity to deeply understand and interpret human intentions has emerged as a critical capability, which demands d…
HumanOmni: A Large Vision-Speech Language Model for Human-Centric Video Understanding
Jiaxing Zhao, Qize Yang, Yixing Peng +8
In human-centric scenes, the ability to simultaneously understand visual and auditory information is crucial. While recent omni models can process multiple modalities, they general…
Facial Dynamics in Video: Instruction Tuning for Improved Facial Expression Perception and Contextual Awareness
Jiaxing Zhao, Boyuan Sun, Xiang Chen +1
Facial expression captioning has found widespread application across various domains. Recently, the emergence of video Multimodal Large Language Models (MLLMs) has shown promise in…
Towards RAW Object Detection in Diverse Conditions
Zhong-Yu Li, Xin Jin, Boyuan Sun +2
Existing object detection methods often consider sRGB input, which was compressed from RAW data using ISP originally designed for visualization. However, such compression might los…