The SnakeCLEF2023 competition aims to the development of advanced algori...
We propose Equiangular Basis Vectors (EBVs) for classification tasks. In...
Simplicity Bias (SB) is a phenomenon that deep neural networks tend to r...
In this paper, we propose Suppression-Enhancing Mask based attention and...
Semi-supervised few-shot learning consists in training a classifier to a...
Long-tailed instance segmentation is a challenging task due to the extre...
Fine-grained image analysis (FGIA) is a longstanding and fundamental pro...
Learning from the web can ease the extreme dependence of deep learning o...
One single instance could possess multiple portraits and reveal diverse
...
In this paper, we tackle the long-tailed visual recognition problem from...
WebFG 2020 is an international challenge hosted by Nanjing University of...
Due to the existence of label noise in web images and the high memorizat...
Retrieving content relevant images from a large-scale fine-grained datas...
State-of-the-art two-stage object detectors apply a classifier to a spar...
We target at providing a computational cheap yet effective approach for
...
Despite significant progress of applying deep learning methods to the fi...
In this paper, we tackle the domain adaptive object detection problem, w...
Our work focuses on tackling the challenging but natural visual recognit...
Computer vision (CV) is the process of using machines to understand and
...
The task of multi-label image recognition is to predict a set of object
...
Over recent years, emerging interest has occurred in integrating compute...
Vehicle re-identification is an important problem and becomes desirable ...
Humans are capable of learning a new fine-grained concept with very litt...
Landmark/pose estimation in single monocular images have received much e...
Reusable model design becomes desirable with the rapid expansion of comp...
Reusable model design becomes desirable with the rapid expansion of mach...
Fine-grained image recognition is a challenging computer vision problem,...
Deep convolutional neural network models pre-trained for the ImageNet
cl...
In this paper we show that by carefully making good choices for various
...
In this paper, we categorize fine-grained images without using any objec...