415 citations · 1.2k across the 53 of their papers we have counts for
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Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image Classification
Jijie Wu, Dongliang Chang, Aneeshan Sain +5
The main challenge for fine-grained few-shot image classification is to learn feature representations with higher inter-class and lower intra-class variations, with a mere few labe…
Learning Invariant Visual Representations for Compositional Zero-Shot Learning
Tian Zhang, Kongming Liang, Ruoyi Du +3
Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions using knowledge learned from seen attribute-object compositions in the training set. Previous works mai…
Multi-View Active Fine-Grained Recognition
Ruoyi Du, Wenqing Yu, Heqing Wang +4
As fine-grained visual classification (FGVC) being developed for decades, great works related have exposed a key direction -- finding discriminative local regions and revealing sub…
Domain Generalization via Frequency-domain-based Feature Disentanglement and Interaction
Jingye Wang, Ruoyi Du, Dongliang Chang +2
Adaptation to out-of-distribution data is a meta-challenge for all statistical learning algorithms that strongly rely on the i.i.d. assumption. It leads to unavoidable labor costs…
Clue Me In: Semi-Supervised FGVC with Out-of-Distribution Data
Ruoyi Du, Dongliang Chang, Zhanyu Ma +2
Despite great strides made on fine-grained visual classification (FGVC), current methods are still heavily reliant on fully-supervised paradigms where ample expert labels are calle…
Making a Bird AI Expert Work for You and Me
Dongliang Chang, Kaiyue Pang, Ruoyi Du +3
As powerful as fine-grained visual classification (FGVC) is, responding your query with a bird name of "Whip-poor-will" or "Mallard" probably does not make much sense. This however…
Cross-layer Navigation Convolutional Neural Network for Fine-grained Visual Classification
Chenyu Guo, Jiyang Xie, Kongming Liang +2
Fine-grained visual classification (FGVC) aims to classify sub-classes of objects in the same super-class (e.g., species of birds, models of cars). For the FGVC tasks, the essentia…
CMF: Cascaded Multi-model Fusion for Referring Image Segmentation
Jianhua Yang, Yan Huang, Zhanyu Ma +1
In this work, we address the task of referring image segmentation (RIS), which aims at predicting a segmentation mask for the object described by a natural language expression. Mos…
Channel DropBlock: An Improved Regularization Method for Fine-Grained Visual Classification
Yifeng Ding, Shuwei Dong, Yujun Tong +3
Classifying the sub-categories of an object from the same super-category (e.g., bird) in a fine-grained visual classification (FGVC) task highly relies on mining multiple discrimin…
Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developments
Xiaoxu Li, Xiaochen Yang, Zhanyu Ma +1
Few-shot image classification is a challenging problem that aims to achieve the human level of recognition based only on a small number of training images. One main solution to few…
DF^2AM: Dual-level Feature Fusion and Affinity Modeling for RGB-Infrared Cross-modality Person Re-identification
Junhui Yin, Zhanyu Ma, Jiyang Xie +3
RGB-infrared person re-identification is a challenging task due to the intra-class variations and cross-modality discrepancy. Existing works mainly focus on learning modality-share…
Unsupervised Person Re-identification via Simultaneous Clustering and Consistency Learning
Junhui Yin, Jiayan Qiu, Siqing Zhang +3
Unsupervised person re-identification (re-ID) has become an important topic due to its potential to resolve the scalability problem of supervised re-ID models. However, existing me…
Fine-Grained Visual Classification via Simultaneously Learning of Multi-regional Multi-grained Features
Dongliang Chang, Yixiao Zheng, Zhanyu Ma +2
Fine-grained visual classification is a challenging task that recognizes the sub-classes belonging to the same meta-class. Large inter-class similarity and intra-class variance is…
Grad-CAM guided channel-spatial attention module for fine-grained visual classification
Shuai Xu, Dongliang Chang, Jiyang Xie +1
Fine-grained visual classification (FGVC) is becoming an important research field, due to its wide applications and the rapid development of computer vision technologies. The curre…
TLRM: Task-level Relation Module for GNN-based Few-Shot Learning
Yurong Guo, Zhanyu Ma, Xiaoxu Li +1
Recently, graph neural networks (GNNs) have shown powerful ability to handle few-shot classification problem, which aims at classifying unseen samples when trained with limited lab…
Progressive Co-Attention Network for Fine-grained Visual Classification
Tian Zhang, Dongliang Chang, Zhanyu Ma +1
Fine-grained visual classification aims to recognize images belonging to multiple sub-categories within a same category. It is a challenging task due to the inherently subtle varia…
Knowledge Transfer Based Fine-grained Visual Classification
Siqing Zhang, Ruoyi Du, Dongliang Chang +2
Fine-grained visual classification (FGVC) aims to distinguish the sub-classes of the same category and its essential solution is to mine the subtle and discriminative regions. Conv…
Dilated-Scale-Aware Attention ConvNet For Multi-Class Object Counting
Wei Xu, Dingkang Liang, Yixiao Zheng +1
Object counting aims to estimate the number of objects in images. The leading counting approaches focus on the single category counting task and achieve impressive performance. Not…
BSNet: Bi-Similarity Network for Few-shot Fine-grained Image Classification
Xiaoxu Li, Jijie Wu, Zhuo Sun +3
Few-shot learning for fine-grained image classification has gained recent attention in computer vision. Among the approaches for few-shot learning, due to the simplicity and effect…
Your "Flamingo" is My "Bird": Fine-Grained, or Not
Dongliang Chang, Kaiyue Pang, Yixiao Zheng +3
Whether what you see in Figure 1 is a "flamingo" or a "bird", is the question we ask in this paper. While fine-grained visual classification (FGVC) strives to arrive at the former,…
Actor and Action Modular Network for Text-based Video Segmentation
Jianhua Yang, Yan Huang, Kai Niu +3
Text-based video segmentation aims to segment an actor in video sequences by specifying the actor and its performing action with a textual query. Previous methods fail to explicitl…
CC-Loss: Channel Correlation Loss For Image Classification
Zeyu Song, Dongliang Chang, Zhanyu Ma +2
The loss function is a key component in deep learning models. A commonly used loss function for classification is the cross entropy loss, which is a simple yet effective applicatio…
GINet: Graph Interaction Network for Scene Parsing
Tianyi Wu, Yu Lu, Yu Zhu +4
Recently, context reasoning using image regions beyond local convolution has shown great potential for scene parsing. In this work, we explore how to incorporate the linguistic kno…
SSKD: Self-Supervised Knowledge Distillation for Cross Domain Adaptive Person Re-Identification
Junhui Yin, Jiayan Qiu, Siqing Zhang +2
Domain adaptive person re-identification (re-ID) is a challenging task due to the large discrepancy between the source domain and the target domain. To reduce the domain discrepanc…