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 …
Regionvit: Regional-to-local attention for vision transformers
Vision transformer (ViT) has recently shown its strong capability in achieving comparable
results to convolutional neural networks (CNNs) on image classification. However, vanilla …
results to convolutional neural networks (CNNs) on image classification. However, vanilla …
Delving deep into label smoothing
Label smoothing is an effective regularization tool for deep neural networks (DNNs), which
generates soft labels by applying a weighted average between the uniform distribution and …
generates soft labels by applying a weighted average between the uniform distribution and …
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 …
Accurate fine-grained object recognition with structure-driven relation graph networks
Fine-grained object recognition (FGOR) aims to learn discriminative features that can
identify the subtle distinctions between visually similar objects. However, less effort has …
identify the subtle distinctions between visually similar objects. However, less effort has …
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 …
Vision-based autonomous vehicle recognition: A new challenge for deep learning-based systems
A Boukerche, X Ma - ACM Computing Surveys (CSUR), 2021 - dl.acm.org
Vision-based Automated Vehicle Recognition (VAVR) has attracted considerable attention
recently. Particularly given the reliance on emerging deep learning methods, which have …
recently. Particularly given the reliance on emerging deep learning methods, which have …
Fine-grained recognition with learnable semantic data augmentation
Fine-grained image recognition is a longstanding computer vision challenge that focuses on
differentiating objects belonging to multiple subordinate categories within the same meta …
differentiating objects belonging to multiple subordinate categories within the same meta …
Granularity-aware distillation and structure modeling region proposal network for fine-grained image classification
X Ke, Y Cai, B Chen, H Liu, W Guo - Pattern Recognition, 2023 - Elsevier
Fine-grained visual classification (FGVC) aims to identify objects belonging to multiple sub-
categories of the same super-category. The key to solving fine-grained classification …
categories of the same super-category. The key to solving fine-grained classification …
Weakly supervised fine-grained image classification via guassian mixture model oriented discriminative learning
Existing weakly supervised fine-grained image recognition (WFGIR) methods usually pick
out the discriminative regions from the high-level feature maps directly. We discover that due …
out the discriminative regions from the high-level feature maps directly. We discover that due …