Transfg: A transformer architecture for fine-grained recognition
Fine-grained visual classification (FGVC) which aims at recognizing objects from
subcategories is a very challenging task due to the inherently subtle inter-class differences …
subcategories is a very challenging task due to the inherently subtle inter-class differences …
Metaformer: A unified meta framework for fine-grained recognition
Fine-Grained Visual Classification (FGVC) is the task that requires recognizing the objects
belonging to multiple subordinate categories of a super-category. Recent state-of-the-art …
belonging to multiple subordinate categories of a super-category. Recent state-of-the-art …
Progressive learning of category-consistent multi-granularity features for fine-grained visual classification
Fine-grained visual classification (FGVC) is much more challenging than traditional
classification tasks due to the inherently subtle intra-class object variations. Recent works …
classification tasks due to the inherently subtle intra-class object variations. Recent works …
Part-guided relational transformers for fine-grained visual recognition
Fine-grained visual recognition is to classify objects with visually similar appearances into
subcategories, which has made great progress with the development of deep CNNs …
subcategories, which has made great progress with the development of deep CNNs …
Learning rich part hierarchies with progressive attention networks for fine-grained image recognition
We investigate the localization of subtle yet discriminative parts for fine-grained image
recognition. Based on the observation that such parts typically exist within a hierarchical …
recognition. Based on the observation that such parts typically exist within a hierarchical …
Improving fine-grained visual recognition in low data regimes via self-boosting attention mechanism
The challenge of fine-grained visual recognition often lies in discovering the key
discriminative regions. While such regions can be automatically identified from a large-scale …
discriminative regions. While such regions can be automatically identified from a large-scale …
Transformer with peak suppression and knowledge guidance for fine-grained image recognition
Fine-grained image recognition is challenging because discriminative clues are usually
fragmented, whether from a single image or multiple images. Despite their significant …
fragmented, whether from a single image or multiple images. Despite their significant …
Cross-x learning for fine-grained visual categorization
Recognizing objects from subcategories with very subtle differences remains a challenging
task due to the large intra-class and small inter-class variation. Recent work tackles this …
task due to the large intra-class and small inter-class variation. Recent work tackles this …
Stochastic partial swap: Enhanced model generalization and interpretability for fine-grained recognition
Learning mid-level representation for fine-grained recognition is easily dominated by a
limited number of highly discriminative patterns, degrading its robustness and generalization …
limited number of highly discriminative patterns, degrading its robustness and generalization …
Context-aware attentional pooling (cap) for fine-grained visual classification
Deep convolutional neural networks (CNNs) have shown a strong ability in mining
discriminative object pose and parts information for image recognition. For fine-grained …
discriminative object pose and parts information for image recognition. For fine-grained …