85 citations · 118 across the 12 of their papers we have counts for
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A Compositional Feature Embedding and Similarity Metric for Ultra-Fine-Grained Visual Categorization
Yajie Sun, Miaohua Zhang, Xiaohan Yu +2
Fine-grained visual categorization (FGVC), which aims at classifying objects with small inter-class variances, has been significantly advanced in recent years. However, ultra-fine-…
Mask-Guided Feature Extraction and Augmentation for Ultra-Fine-Grained Visual Categorization
Zicheng Pan, Xiaohan Yu, Miaohua Zhang +1
While the fine-grained visual categorization (FGVC) problems have been greatly developed in the past years, the Ultra-fine-grained visual categorization (Ultra-FGVC) problems have…
PGTRNet: Two-phase Weakly Supervised Object Detection with Pseudo Ground Truth Refinement
Jun Wang, Hefeng Zhou, Xiaohan Yu
Current state-of-the-art weakly supervised object detection (WSOD) studies mainly follow a two-stage training strategy which integrates a fully supervised detector (FSD) with a pur…
Feature Fusion Vision Transformer for Fine-Grained Visual Categorization
Jun Wang, Xiaohan Yu, Yongsheng Gao
The core for tackling the fine-grained visual categorization (FGVC) is to learn subtle yet discriminative features. Most previous works achieve this by explicitly selecting the dis…
EAR-NET: Error Attention Refining Network For Retinal Vessel Segmentation
Jun Wang, Yang Zhao, Linglong Qian +2
The precise detection of blood vessels in retinal images is crucial to the early diagnosis of the retinal vascular diseases, e.g., diabetic, hypertensive and solar retinopathies. E…
Mask Guided Attention For Fine-Grained Patchy Image Classification
Jun Wang, Xiaohan Yu, Yongsheng Gao
In this work, we present a novel mask guided attention (MGA) method for fine-grained patchy image classification. The key challenge of fine-grained patchy image classification lies…