35 citations · 39 across the 7 of their papers we have counts for
7 papers
Fine-grained Image Retrieval via Dual-Vision Adaptation
Xin Jiang, Meiqi Cao, Hao Tang +2
Fine-Grained Image Retrieval~(FGIR) faces challenges in learning discriminative visual representations to retrieve images with similar fine-grained features. Current leading FGIR s…
Rethinking Vision Transformer for Large-Scale Fine-Grained Image Retrieval
Xin Jiang, Hao Tang, Yonghua Pan +1
Large-scale fine-grained image retrieval (FGIR) aims to retrieve images belonging to the same subcategory as a given query by capturing subtle differences in a large-scale setting.…
OT-DETECTOR: Delving into Optimal Transport for Zero-shot Out-of-Distribution Detection
Yu Liu, Hao Tang, Haiqi Zhang +2
Out-of-distribution (OOD) detection is crucial for ensuring the reliability and safety of machine learning models in real-world applications. While zero-shot OOD detection, which r…
DVF: Advancing Robust and Accurate Fine-Grained Image Retrieval with Retrieval Guidelines
Xin Jiang, Hao Tang, Rui Yan +2
Fine-grained image retrieval (FGIR) is to learn visual representations that distinguish visually similar objects while maintaining generalization. Existing methods propose to gener…
Learning with Unreliability: Fast Few-shot Voxel Radiance Fields with Relative Geometric Consistency
Yingjie Xu, Bangzhen Liu, Hao Tang +2
We propose a voxel-based optimization framework, ReVoRF, for few-shot radiance fields that strategically address the unreliability in pseudo novel view synthesis. Our method pivots…
Learning Contrastive Self-Distillation for Ultra-Fine-Grained Visual Categorization Targeting Limited Samples
Ziye Fang, Xin Jiang, Hao Tang +1
In the field of intelligent multimedia analysis, ultra-fine-grained visual categorization (Ultra-FGVC) plays a vital role in distinguishing intricate subcategories within broader c…
Delving into Multimodal Prompting for Fine-grained Visual Classification
Xin Jiang, Hao Tang, Junyao Gao +3
Fine-grained visual classification (FGVC) involves categorizing fine subdivisions within a broader category, which poses challenges due to subtle inter-class discrepancies and larg…