3 citations · 7 across the 9 of their papers we have counts for
12 papers
ChronoVision: Temporal Reasoning via Latent State Reconstruction
Yifan Shen, Jian Xu, Boyi Li +6
Multimodal large language models excel at passive perception but struggle with complex visual cognitive tasks requiring multi-step temporal reasoning. This degradation largely stem…
Decoding Children's Gait Behavior
Yifan Shen, Boyi Li, Meihuan Huang +12
We introduce a new problem domain for human action recognition: the fine-grained analysis of children's gait behaviors from standard RGB video. We specifically target the ambulator…
The 1st AI Children Challenge
Boyi Li, Yifan Shen, Houze Yang +7
The First AI Children Challenge aims to advance real-world applications of computer vision and AI in child healthcare, child education, and pediatrics. The 2026 CV4CHL edition feat…
: Unifying Generation and Self-Verification for Parallel Reasoners
Harman Singh, Xiuyu Li, Kusha Sareen +14
Test-time scaling for complex reasoning tasks shows that leveraging inference-time compute, by methods such as independently sampling and aggregating multiple solutions, results in…
DexImit: Learning Bimanual Dexterous Manipulation from Monocular Human Videos
Juncheng Mu, Sizhe Yang, Yiming Bao +6
Data scarcity fundamentally limits the generalization of bimanual dexterous manipulation, as real-world data collection for dexterous hands is expensive and labor-intensive. Human…
TADS: Task-Aware Data Selection for Multi-Task Multimodal Pre-Training
Guanjie Cheng, Boyi Li, Lingyu Sun +4
Large-scale multimodal pre-trained models like CLIP rely heavily on high-quality training data, yet raw web-crawled datasets are often noisy, misaligned, and redundant, leading to…
Describe Anything: Detailed Localized Image and Video Captioning
Long Lian, Yifan Ding, Yunhao Ge +8
Generating detailed and accurate descriptions for specific regions in images and videos remains a fundamental challenge for vision-language models. We introduce the Describe Anythi…
Atlas: Multi-Scale Attention Improves Long Context Image Modeling
Kumar Krishna Agrawal, Long Lian, Longchao Liu +6
Efficiently modeling massive images is a long-standing challenge in machine learning. To this end, we introduce Multi-Scale Attention (MSA). MSA relies on two key ideas, (i) multi-…
Scaling Vision Pre-Training to 4K Resolution
Baifeng Shi, Boyi Li, Han Cai +8
High-resolution perception of visual details is crucial for daily tasks. Current vision pre-training, however, is still limited to low resolutions (e.g., 378 x 378 pixels) due to t…
Self-correcting LLM-controlled Diffusion Models
Tsung-Han Wu, Long Lian, Joseph E. Gonzalez +2
Text-to-image generation has witnessed significant progress with the advent of diffusion models. Despite the ability to generate photorealistic images, current text-to-image diffus…
From Wrong To Right: A Recursive Approach Towards Vision-Language Explanation
Jiaxin Ge, Sanjay Subramanian, Trevor Darrell +1
Addressing the challenge of adapting pre-trained vision-language models for generating insightful explanations for visual reasoning tasks with limited annotations, we present ReVis…
LLM-grounded Video Diffusion Models
Long Lian, Baifeng Shi, Adam Yala +2
Text-conditioned diffusion models have emerged as a promising tool for neural video generation. However, current models still struggle with intricate spatiotemporal prompts and oft…