20 citations · 29 across the 15 of their papers we have counts for
11 papers · 1 filter
CoVisco: Codec-Native Vision Encoder with Native Token Compression for Unified Image-Video Understanding
Yulong Liu, Xiaotian Han, Junyuan Shang +6
Vision-language models face a fundamental scaling bottleneck: the number of visual tokens grows with both temporal duration and spatial resolution, making long-video understanding…
RESUME: Recurrent State Updates from Motion and Residual Signals for Efficient Video Language Modeling
Can Zhang, Xiaotian Han, Junyuan Shang +5
Existing video language models encode sampled RGB frames independently, so a long video must either exhaust the token budget or drop the changes between sampled frames. Codec-aware…
From Intent to Evidence: Policy-Steered Multi-Strategy Retrieval for Long-Video Agents
Can Zhang, Baofeng Zhang, Xiaotian Han +5
Existing long-video agents acquire evidence through one uniform behavior, ignoring whether the required evidence is concentrated, requires broad occurrence coverage, or must discri…
Aggregating Visual Information with Optimal Transport for VideoLM Token Compression
Wenti Yin, Xiaotian Han, Junyuan Shang +5
Video language models process videos as dense visual-token sequences with substantial representational redundancy. Compressing these sequences is therefore essential for reducing t…
DreamClear: High-Capacity Real-World Image Restoration with Privacy-Safe Dataset Curation
Yuang Ai, Xiaoqiang Zhou, Huaibo Huang +4
Image restoration (IR) in real-world scenarios presents significant challenges due to the lack of high-capacity models and comprehensive datasets. To tackle these issues, we presen…
InfiMM-WebMath-40B: Advancing Multimodal Pre-Training for Enhanced Mathematical Reasoning
Xiaotian Han, Yiren Jian, Xuefeng Hu +8
Pre-training on large-scale, high-quality datasets is crucial for enhancing the reasoning capabilities of Large Language Models (LLMs), especially in specialized domains such as ma…
Visual Anchors Are Strong Information Aggregators For Multimodal Large Language Model
Haogeng Liu, Quanzeng You, Xiaotian Han +4
In the realm of Multimodal Large Language Models (MLLMs), vision-language connector plays a crucial role to link the pre-trained vision encoders with Large Language Models (LLMs).…
ViTAR: Vision Transformer with Any Resolution
Qihang Fan, Quanzeng You, Xiaotian Han +5
This paper tackles a significant challenge faced by Vision Transformers (ViTs): their constrained scalability across different image resolutions. Typically, ViTs experience a perfo…
InfiMM-HD: A Leap Forward in High-Resolution Multimodal Understanding
Haogeng Liu, Quanzeng You, Xiaotian Han +7
Multimodal Large Language Models (MLLMs) have experienced significant advancements recently. Nevertheless, challenges persist in the accurate recognition and comprehension of intri…
COCO is "ALL'' You Need for Visual Instruction Fine-tuning
Xiaotian Han, Yiqi Wang, Bohan Zhai +2
Multi-modal Large Language Models (MLLMs) are increasingly prominent in the field of artificial intelligence. Visual instruction fine-tuning (IFT) is a vital process for aligning M…
InfiMM-Eval: Complex Open-Ended Reasoning Evaluation For Multi-Modal Large Language Models
Xiaotian Han, Quanzeng You, Yongfei Liu +9
Multi-modal Large Language Models (MLLMs) are increasingly prominent in the field of artificial intelligence. These models not only excel in traditional vision-language tasks but a…