35 citations · 40 across the 9 of their papers we have counts for
9 papers
DailyBench: A Unified Benchmark for AI-Generated and Manipulated Images from Modern Generative Models
Xin Jiang, Hao Tang, Junyao Gao +7
Recent advances in generative models have shifted AI-generated image detection from identifying easily distinguishable, fully synthetic images to identifying highly realistic conte…
See More, Match Better: Multi-Source Feature Fusion for Two-View Correspondence Learning
Xiaojie Li, Xin Jiang, Luanyuan Dai +3
Two-view correspondence learning aims to distinguish true correspondences (inliers) from false ones (outliers) in image pairs by leveraging their underlying differences. Existing m…
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.…
Why pre-training is beneficial for downstream classification tasks?
Xin Jiang, Xu Cheng, Zechao Li
Pre-training has exhibited notable benefits to downstream tasks by boosting accuracy and speeding up convergence, but the exact reasons for these benefits still remain unclear. To…
IMAGDressing-v1: Customizable Virtual Dressing
Fei Shen, Xin Jiang, Xin He +5
Latest advances have achieved realistic virtual try-on (VTON) through localized garment inpainting using latent diffusion models, significantly enhancing consumers' online shopping…
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 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…