Fine-grained visual classification (FGVC) involves categorizing fine
sub...
In recent years, the development of instance segmentation has garnered
s...
The In-Context Learning (ICL) is to understand a new task via a few
demo...
Knowledge distillation-based anomaly detection methods generate same out...
Reconstruction method based on the memory module for visual anomaly dete...
Unsupervised anomaly detection is a challenging task in industrial
appli...
Weakly supervised Referring Expression Grounding (REG) aims to ground a
...
Freezing the pre-trained backbone has become a standard paradigm to avoi...
The pixel-wise dense prediction tasks based on weakly supervisions curre...
Contextual information has been shown to be powerful for semantic
segmen...
Retinal images have been widely used by clinicians for early diagnosis o...
Constructing fine-grained image datasets typically requires domain-speci...
State-of-the-art face super-resolution methods employ deep convolutional...
Hashing has been widely applied to multimodal retrieval on large-scale
m...
In this paper, we propose a general dual convolutional neural network
(D...
Hashing has attracted increasing research attentions in recent years due...
Image retagging aims to improve tag quality of social images by refining...
The Guided Filter (GF) is well-known for its linear complexity. However,...
Videos are inherently multimodal. This paper studies the problem of how ...
Age progression is defined as aesthetically re-rendering the aging face ...
Recently, Long Short-Term Memory (LSTM) has become a popular choice to m...