Fine-grained image analysis with deep learning: A survey
Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer
vision and pattern recognition, and underpins a diverse set of real-world applications. The …
vision and pattern recognition, and underpins a diverse set of real-world applications. The …
Dual cross-attention learning for fine-grained visual categorization and object re-identification
Recently, self-attention mechanisms have shown impressive performance in various NLP
and CV tasks, which can help capture sequential characteristics and derive global …
and CV tasks, which can help capture sequential characteristics and derive global …
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 …
Learning attention-guided pyramidal features for few-shot fine-grained recognition
Few-shot fine-grained recognition (FS-FGR) aims to distinguish several highly similar
objects from different sub-categories with limited supervision. However, traditional few-shot …
objects from different sub-categories with limited supervision. However, traditional few-shot …
Fine-grained visual classification via progressive multi-granularity training of jigsaw patches
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 …
Feature fusion vision transformer for fine-grained visual categorization
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 …
discriminative features. Most previous works achieve this by explicitly selecting the …
Fine-grained generalized zero-shot learning via dense attribute-based attention
D Huynh, E Elhamifar - … of the IEEE/CVF conference on …, 2020 - openaccess.thecvf.com
We address the problem of fine-grained generalized zero-shot recognition of visually similar
classes without training images for some classes. We propose a dense attribute-based …
classes without training images for some classes. We propose a dense attribute-based …
Sim-trans: Structure information modeling transformer for fine-grained visual categorization
Fine-grained visual categorization (FGVC) aims at recognizing objects from similar
subordinate categories, which is challenging and practical for human's accurate automatic …
subordinate categories, which is challenging and practical for human's accurate automatic …
Rams-trans: Recurrent attention multi-scale transformer for fine-grained image recognition
In fine-grained image recognition (FGIR), the localization and amplification of region
attention is an important factor, which has been explored extensively convolutional neural …
attention is an important factor, which has been explored extensively convolutional neural …
Snapmix: Semantically proportional mixing for augmenting fine-grained data
Data mixing augmentation has proved effective in training deep models. Recent methods
mix labels mainly according to the mixture proportion of image pixels. Due to the major …
mix labels mainly according to the mixture proportion of image pixels. Due to the major …