5 papers
DailyBench: A Unified Benchmark for AI-Generated and Manipulated Images from Modern Generative Models
Xin Jiang, Hao Tang, Junyao Gao +4
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…