Deep audio-visual learning: A survey
Audio-visual learning, aimed at exploiting the relationship between audio and visual
modalities, has drawn considerable attention since deep learning started to be used …
modalities, has drawn considerable attention since deep learning started to be used …
A comprehensive survey on deep gait recognition: algorithms, datasets and challenges
Gait recognition aims at identifying a person at a distance through visual cameras. With the
emergence of deep learning, significant advancements in gait recognition have achieved …
emergence of deep learning, significant advancements in gait recognition have achieved …
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 …
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 …
CABNet: Category attention block for imbalanced diabetic retinopathy grading
Diabetic Retinopathy (DR) grading is challenging due to the presence of intra-class
variations, small lesions and imbalanced data distributions. The key for solving fine-grained …
variations, small lesions and imbalanced data distributions. The key for solving fine-grained …
A spatial feature-enhanced attention neural network with high-order pooling representation for application in pest and disease recognition
With the development of advanced information and intelligence technologies, precision
agriculture has become an effective solution to monitor and prevent crop pests and …
agriculture has become an effective solution to monitor and prevent crop pests and …
Boosting few-shot fine-grained recognition with background suppression and foreground alignment
Few-shot fine-grained recognition (FS-FGR) aims to recognize novel fine-grained categories
with the help of limited available samples. Undoubtedly, this task inherits the main …
with the help of limited available samples. Undoubtedly, this task inherits the main …
TransIFC: invariant cues-aware feature concentration learning for efficient fine-grained bird image classification
Fine-grained bird image classification (FBIC) is not only meaningful for endangered bird
observation and protection but also a prevalent task for image classification in multimedia …
observation and protection but also a prevalent task for image classification in multimedia …
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 …
Aa-trans: Core attention aggregating transformer with information entropy selector for fine-grained visual classification
The task of fine-grained visual classification (FGVC) is to distinguish targets from
subordinate classifications. Since fine-grained images have the inherent characteristic of …
subordinate classifications. Since fine-grained images have the inherent characteristic of …