Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Deep neural models, in recent years, have been successful in almost every field, even
solving the most complex problem statements. However, these models are huge in size with …

We introduce EgoSchema, a very long-form video question-answering dataset, and
benchmark to evaluate long video understanding capabilities of modern vision and …

Test-time training adapts to a new test distribution on the fly by optimizing a model for each
test input using self-supervision. In this paper, we use masked autoencoders for this one …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

In this paper, we propose Test-Time Training, a general approach for improving the
performance of predictive models when training and test data come from different …

Self-attention performs well in long context but has quadratic complexity. Existing RNN
layers have linear complexity, but their performance in long context is limited by the …

This paper proposes a simple self-supervised approach for learning a representation for
visual correspondence from raw video. We cast correspondence as prediction of links in a …

Recommending cold items in recommendation systems is a longstanding challenge due to
the inherent differences between warm items, which are recommended based on user …

Stereo-based 3D detection aims at detecting 3D object bounding boxes from stereo images
using intermediate depth maps or implicit 3D geometry representations, which provides a …