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 …
Attribute-aware deep hashing with self-consistency 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 …
An Asymmetric Augmented Self-Supervised Learning Method for Unsupervised Fine-Grained Image Hashing
Unsupervised fine-grained image hashing aims to learn compact binary hash codes in
unsupervised settings addressing challenges posed by large-scale datasets and …
unsupervised settings addressing challenges posed by large-scale datasets and …
Learning mutually exclusive part representations for fine-grained image classification
Fine-grained image classification (FGIC) aims to separate different subcategories from one
general superclass, which requires the classification model to extract distinctive …
general superclass, which requires the classification model to extract distinctive …
Automatic check-out via prototype-based classifier learning from single-product exemplars
Abstract Automatic Check-Out (ACO) aims to accurately predict the presence and count of
each category of products in check-out images, where a major challenge is the significant …
each category of products in check-out images, where a major challenge is the significant …
MECOM: A Meta-Completion Network for Fine-Grained Recognition with Incomplete Multi-Modalities
XS Wei, HT Yu, A Xu, F Zhang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Our work focuses on tackling the problem of fine-grained recognition with incomplete multi-
modal data, which is overlooked by previous work in the literature. It is desirable to not only …
modal data, which is overlooked by previous work in the literature. It is desirable to not only …
Automatic identification of conodont species using fine-grained convolutional neural networks
X Duan - Frontiers in Earth Science, 2023 - frontiersin.org
Conodonts are jawless vertebrates deposited in marine strata from the Cambrian to the
Triassic that play an important role in geoscience research. The accurate identification of …
Triassic that play an important role in geoscience research. The accurate identification of …
Logit Variated Product Quantization Based on Parts Interaction and Metric Learning With Knowledge Distillation for Fine-Grained Image Retrieval
Image retrieval with fine-grained categories is an extremely challenging task due to the high
intraclass variance and low interclass variance. Most previous works have focused on …
intraclass variance and low interclass variance. Most previous works have focused on …
FOF: a fine-grained object detection and feature extraction end-to-end network
W Shen, J Chen, J Shao - International Journal of Multimedia Information …, 2023 - Springer
Currently, widely used object detection can predict targets present in the training set.
However, in fine-grained object detection tasks, such as commodity detection, the …
However, in fine-grained object detection tasks, such as commodity detection, the …
Prototype Learning for Automatic Check-Out
H Chen, XS Wei, L Xiao - IEEE Transactions on Multimedia, 2023 - ieeexplore.ieee.org
The basic goal of Automatic Check-Out (ACO) task is to accurately predict the categories
and quantities of products selected by customers in the check-out images. However, there is …
and quantities of products selected by customers in the check-out images. However, there is …