Vision transformers (ViTs) have significantly changed the computer visio...
Label-efficient and reliable semantic segmentation is essential for many...
Modern deep neural networks tend to be evaluated on static test sets. On...
In this paper, we provide a deep analysis of temporal modeling for actio...
The modern open internet contains billions of public images of human fac...
Most machine learning models are validated and tested on fixed datasets....
Less than 35
leads to increased soil and sea pollution and is one of the...
The black-box nature of neural networks limits model decision
interpreta...
In this work, we develop efficient disruptions of black-box image transl...
Deep learning is being adopted in settings where accurate and justifiabl...
Conventionally, AI models are thought to trade off explainability for lo...
In this work, we address the problem of learning an ensemble of speciali...
Deep models are state-of-the-art for many computer vision tasks includin...
We propose Guided Zoom, an approach that utilizes spatial grounding to m...
We propose a guided dropout regularizer for deep networks based on the
e...
Binary vector embeddings enable fast nearest neighbor retrieval in large...
We present the Moments in Time Dataset, a large-scale human-annotated
co...
Deep models are state-of-the-art for many vision tasks including video a...
Hashing, or learning binary embeddings of data, is frequently used in ne...
Learning-based hashing methods are widely used for nearest neighbor
retr...
Recently, attempts have been made to collect millions of videos to train...
Supervised hashing methods are widely-used for nearest neighbor search i...