3 citations · 7 across the 19 of their papers we have counts for
7 papers · 1 filter
Language-driven Fine-grained Retrieval
Shijie Wang, Xin Yu, Yadan Luo +3
Existing fine-grained image retrieval (FGIR) methods learn discriminative embeddings by adopting semantically sparse one-hot labels derived from category names as supervision. Whil…
TALO: Pushing 3D Vision Foundation Models Towards Globally Consistent Online Reconstruction
Fengyi Zhang, Tianjun Zhang, Kasra Khosoussi +3
3D vision foundation models have shown strong generalization in reconstructing key 3D attributes from uncalibrated images through a single feed-forward pass. However, when deployed…
MergeVLA: Cross-Skill Model Merging Toward a Generalist Vision-Language-Action Agent
Yuxia Fu, Zhizhen Zhang, Yuqi Zhang +3
Recent Vision-Language-Action (VLA) models reformulate vision-language models by tuning them with millions of robotic demonstrations. While they perform well when fine-tuned for a…
Box-QAymo: Box-Referring VQA Dataset for Autonomous Driving
Djamahl Etchegaray, Yuxia Fu, Zi Huang +1
Interpretable communication is essential for safe and trustworthy autonomous driving, yet current vision-language models (VLMs) often operate under idealized assumptions and strugg…
CodeMerge: Codebook-Guided Model Merging for Robust Test-Time Adaptation in Autonomous Driving
Huitong Yang, Zhuoxiao Chen, Fengyi Zhang +2
Maintaining robust 3D perception under dynamic and unpredictable test-time conditions remains a critical challenge for autonomous driving systems. Existing test-time adaptation (TT…
Test-Time 3D Occupancy Prediction
Fengyi Zhang, Xiangyu Sun, Huitong Yang +3
Self-supervised 3D occupancy prediction offers a promising solution for understanding complex driving scenes without requiring costly 3D annotations. However, training dense occupa…
Provable Ordering and Continuity in Vision-Language Pretraining for Generalizable Embodied Agents
Zhizhen Zhang, Lei Zhu, Zhen Fang +2
Pre-training vision-language representations on human action videos has emerged as a promising approach to reduce reliance on large-scale expert demonstrations for training embodie…