Composing simple elements into complex concepts is crucial yet challengi...
Transformers for graph data are increasingly widely studied and successf...
We revisit the common practice of evaluating adaptation of Online Contin...
Implicit neural representations store videos as neural networks and have...
Implicit neural representations (INR) have gained increasing attention i...
In this paper, we formally address universal object detection, which aim...
Open-vocabulary image segmentation is attracting increasing attention du...
Interactive segmentation enables users to extract masks by providing sim...
Continual Learning (CL) aims to sequentially train models on streams of
...
Advances in the field of visual-language contrastive learning have made ...
Backfilling is the process of re-extracting all gallery embeddings from
...
PyTorch Adapt is a library for domain adaptation, a type of machine lear...
Compression and reconstruction of visual data have been widely studied i...
Modern retrieval system often requires recomputing the representation of...
Paraphrase Identification is a fundamental task in Natural Language
Proc...
Deep learning models for vision tasks are trained on large datasets unde...
We introduce a new approach to image forensics: placing physical refract...
In image classification, a lot of development has happened in detecting
...
This paper compares and ranks 11 UDA validation methods. Validators esti...
Recent progress in vision Transformers exhibits great success in various...
It is now well known that neural networks can be wrong with high confide...
We show that the effectiveness of the well celebrated Mixup [Zhang et al...
We present Spartan, a method for training sparse neural network models w...
Content-based Video Retrieval (CBVR) is used on media-sharing platforms ...
Recent advances in image editing techniques have posed serious challenge...
The current modus operandi in adapting pre-trained models involves updat...
It has been reported that deep learning models are extremely vulnerable ...
Neural network classifiers have become the de-facto choice for current
"...
Training an image captioning model in an unsupervised manner without
uti...
Interest in unsupervised domain adaptation (UDA) has surged in recent ye...
Built on top of self-attention mechanisms, vision transformers have
demo...
Communication requires having a common language, a lingua franca, betwee...
Many widely used datasets for graph machine learning tasks have generall...
Adversarial examples pose a unique challenge for deep learning systems.
...
We propose a novel neural representation for videos (NeRV) which encodes...
We introduce Classification with Alternating Normalization (CAN), a
non-...
Tractably modelling distributions over manifolds has long been an import...
Graphs are a common model for complex relational data such as social net...
Recent advances in using retrieval components over external knowledge so...
Visual engagement in social media platforms comprises interactions with ...
Tasks that rely on multi-modal information typically include a fusion mo...
Much data with graph structures satisfy the principle of homophily, mean...
Many variants of adversarial training have been proposed, with most rese...
This paper studies video inpainting detection, which localizes an inpain...
Self-attention learns pairwise interactions via dot products to model
lo...
With the proliferation of deep learning methods, many computer vision
pr...
An image is worth a thousand words, conveying information that goes beyo...
Graph Neural Networks (GNNs) are the predominant technique for learning ...
The quality of image generation and manipulation is reaching impressive
...
Deep metric learning algorithms have a wide variety of applications, but...