85 citations · 118 across the 12 of their papers we have counts for
5 papers · 2 filters
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…
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…