24 citations · 32 across the 16 of their papers we have counts for
8 papers · 1 filter
Logit-Attention Divergence: Mitigating Position Bias in Multi-Image Retrieval via Attention-Guided Calibration
Mingtao Xian, Yifeng Yang, Qinying Gu +2
Multimodal Large Language Models (MLLMs) have shown strong performance in multi-image cross-modal retrieval, yet suffer from severe position bias, where predictions are dominated b…
TINS: Test-time ID-prototype-separated Negative Semantics Learning for OOD Detection
Yifeng Yang, Jubo Feng, Jing Xu +3
Vision-language models enable OOD detection by comparing image alignment with ID labels and negative semantics. Existing negative-label-based methods mainly rely on static negative…
: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization
Lin Zhu, Yifeng Yang, Xinbing Wang +2
Recent approaches for vision-language models (VLMs) have shown remarkable success in achieving fast downstream adaptation. When applied to real-world downstream tasks, VLMs inevita…
OODD: Test-time Out-of-Distribution Detection with Dynamic Dictionary
Yifeng Yang, Lin Zhu, Zewen Sun +3
Out-of-distribution (OOD) detection remains challenging for deep learning models, particularly when test-time OOD samples differ significantly from training outliers. We propose OO…
ConcealGS: Concealing Invisible Copyright Information in 3D Gaussian Splatting
Yifeng Yang, Hengyu Liu, Chenxin Li +6
With the rapid development of 3D reconstruction technology, the widespread distribution of 3D data has become a future trend. While traditional visual data (such as images and vide…
CRoFT: Robust Fine-Tuning with Concurrent Optimization for OOD Generalization and Open-Set OOD Detection
Lin Zhu, Yifeng Yang, Qinying Gu +3
Recent vision-language pre-trained models (VL-PTMs) have shown remarkable success in open-vocabulary tasks. However, downstream use cases often involve further fine-tuning of VL-PT…
PNAS-MOT: Multi-Modal Object Tracking with Pareto Neural Architecture Search
Chensheng Peng, Zhaoyu Zeng, Jinling Gao +5
Multiple object tracking is a critical task in autonomous driving. Existing works primarily focus on the heuristic design of neural networks to obtain high accuracy. As tracking ac…
G-NAS: Generalizable Neural Architecture Search for Single Domain Generalization Object Detection
Fan Wu, Jinling Gao, Lanqing Hong +3
In this paper, we focus on a realistic yet challenging task, Single Domain Generalization Object Detection (S-DGOD), where only one source domain's data can be used for training ob…