10 papers
Scalable Visual Pretraining for Language Intelligence
Yiming Zhang, Zhonghan Zhao, Wenwei Zhang +14
The rapid progress of large foundation models has been driven predominantly by pretraining on large-scale text corpora. However, many forms of knowledge are conveyed through visual…
SAD-GS: Learning Reliable 3D Semantic Gaussian Fields via Dynamic Geo-Semantic Anchoring
Yufei Zhang, Chenlu Zhan, Gaoang Wang +1
Open-vocabulary 3D semantic Gaussian field learning relies on multi-view 2D supervision, whose semantic targets and spatial assignments are often unreliable. Across varying viewpoi…
: A "Spot the Difference" Challenge for Large Multimodal Models
Kewei Wei, Bocheng Hu, Jie Cao +13
Modern Large Multimodal Models (LMMs) have demonstrated extraordinary ability in static image and single-state spatial-temporal understanding. However, their capacity to comprehend…
Verifier-Free RL for LLMs via Intrinsic Gradient-Norm Reward
Xuexiang Wen, Hang Yu, Linchao Zhu +1
While Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as a promising post-training paradigm for Large Language Models (LLMs), its dependency on the gold…
See, Act, Adapt: Active Perception for Unsupervised Cross-Domain Visual Adaptation via Personalized VLM-Guided Agent
Tianci Tang, Tielong Cai, Hongwei Wang +1
Pre-trained perception models excel in generic image domains but degrade significantly in novel environments like indoor scenes. The conventional remedy is fine-tuning on downstrea…
DynaHOI: Benchmarking Hand-Object Interaction for Dynamic Target
BoCheng Hu, Zhonghan Zhao, Kaiyue Zhou +2
Most existing hand motion generation benchmarks for hand-object interaction (HOI) focus on static objects, leaving dynamic scenarios with moving targets and time-critical coordinat…