We study a challenging problem of unsupervised discovery of object landm...
Deep neural networks (DNNs) have made great strides in pushing the
state...
Diabetic Retinopathy (DR), a leading cause of vision impairment, require...
We contribute to the sparsely populated area of unsupervised deep graph
...
Albeit achieving high predictive accuracy across many challenging comput...
Deep neural networks (DNNs) have enabled astounding progress in several
...
With the rapid growth of social media platforms, users are sharing billi...
FSS(Few-shot segmentation) aims to segment a target class with a small n...
The increasing use of deep neural networks in safety-critical applicatio...
Recent years have seen an increased interest in establishing association...
In recent past, several domain generalization (DG) methods have been
pro...
State-of-the-art transformer-based video instance segmentation (VIS)
app...
We study few-shot semantic segmentation that aims to segment a target ob...
Video anomaly detection is well investigated in weakly-supervised and
on...
Following unprecedented success on the natural language tasks, Transform...
We study the problem of learning association between face and voice, whi...
Accurate and robust visual object tracking is one of the most challengin...
We study adapting trained object detectors to unseen domains manifesting...
Human learning benefits from multi-modal inputs that often appear as ric...
Human-object interaction detection is an important and relatively new cl...
Pedestrian detection relying on deep convolution neural networks has mad...
Being heavily reliant on animals, it is our ethical obligation to improv...
Visual and audiovisual speech recognition are witnessing a renaissance w...