Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition
Our work focuses on tackling the challenging but natural visual recognition task of long-
tailed data distribution (ie, a few classes occupy most of the data, while most classes have …
tailed data distribution (ie, a few classes occupy most of the data, while most classes have …
Bag of tricks for long-tailed visual recognition with deep convolutional neural networks
In recent years, visual recognition on challenging long-tailed distributions, where classes
often exhibit extremely imbalanced frequencies, has made great progress mostly based on …
often exhibit extremely imbalanced frequencies, has made great progress mostly based on …
Your" flamingo" is my" bird": Fine-grained, or not
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, for the …
paper. While fine-grained visual classification (FGVC) strives to arrive at the former, for the …
Isia food-500: A dataset for large-scale food recognition via stacked global-local attention network
Food recognition has received more and more attention in the multimedia community for its
various real-world applications, such as diet management and self-service restaurants. A …
various real-world applications, such as diet management and self-service restaurants. A …
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 …
Not all negatives are equal: Label-aware contrastive loss for fine-grained text classification
Fine-grained classification involves dealing with datasets with larger number of classes with
subtle differences between them. Guiding the model to focus on differentiating dimensions …
subtle differences between them. Guiding the model to focus on differentiating dimensions …
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 …
A-Net: Learning Attribute-Aware Hash Codes for Large-Scale Fine-Grained Image Retrieval
Our work focuses on tackling large-scale fine-grained image retrieval as ranking the images
depicting the concept of interests (ie, the same sub-category labels) highest based on the …
depicting the concept of interests (ie, the same sub-category labels) highest based on the …
Meta attention-generation network for cross-granularity few-shot learning
Fine-grained classification with few labeled samples has urgent needs in practice since fine-
grained samples are more difficult and expensive to collect and annotate. Standard few-shot …
grained samples are more difficult and expensive to collect and annotate. Standard few-shot …
A survey of recent advances in CNN-based fine-grained visual categorization
C Qiu, W Zhou - 2020 IEEE 20th International Conference on …, 2020 - ieeexplore.ieee.org
Fine-grained visual classification (FGVC) is an important task in the field of computer vision
(CV), which aims to classify sub-categories that are hard to distinguish (eg, identifying …
(CV), which aims to classify sub-categories that are hard to distinguish (eg, identifying …