17 citations · 53 across the 29 of their papers we have counts for
9 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…
Distributed Zero-Shot Learning for Visual Recognition
Zhi Chen, Yadan Luo, Zi Huang +3
In this paper, we propose a Distributed Zero-Shot Learning (DistZSL) framework that can fully exploit decentralized data to learn an effective model for unseen classes. Considering…
Latent Refinement via Flow Matching for Training-free Linear Inverse Problem Solving
Hossein Askari, Yadan Luo, Hongfu Sun +1
Recent advances in inverse problem solving have increasingly adopted flow priors over diffusion models due to their ability to construct straight probability paths from noise to da…
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…
OraPO: Oracle-educated Reinforcement Learning for Data-efficient and Factual Radiology Report Generation
Zhuoxiao Chen, Hongyang Yu, Ying Xu +3
Radiology report generation (RRG) aims to automatically produce clinically faithful reports from chest X-ray images. Prevailing work typically follows a scale-driven paradigm, by m…
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…
Local Information Matters: Inference Acceleration For Grounded Conversation Generation Models Through Adaptive Local-Aware Token Pruning
Bizhe Bai, Jianjian Cao, Yadan Luo +1
Grounded Conversation Generation (GCG) is an emerging vision-language task that requires models to generate natural language responses seamlessly intertwined with corresponding obj…
SCORE: Soft Label Compression-Centric Dataset Condensation via Coding Rate Optimization
Bowen Yuan, Yuxia Fu, Zijian Wang +2
Dataset Condensation (DC) aims to obtain a condensed dataset that allows models trained on the condensed dataset to achieve performance comparable to those trained on the full data…