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01/19/2023
Multiview Compressive Coding for 3D Reconstruction
A central goal of visual recognition is to understand objects and scenes...
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01/20/2022
MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition
While today's video recognition systems parse snapshots or short clips a...
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01/10/2022
A ConvNet for the 2020s
The "Roaring 20s" of visual recognition began with the introduction of V...
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12/16/2021
Masked Feature Prediction for Self-Supervised Visual Pre-Training
We present Masked Feature Prediction (MaskFeat) for self-supervised pre-...
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12/02/2021
Improved Multiscale Vision Transformers for Classification and Detection
In this paper, we study Multiscale Vision Transformers (MViT) as a unifi...
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06/21/2021
Towards Long-Form Video Understanding
Our world offers a never-ending stream of visual stimuli, yet today's vi...
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10/27/2020
Memory Optimization for Deep Networks
Deep learning is slowly, but steadily, hitting a memory bottleneck. Whil...
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04/06/2020
Lossless Image Compression through Super-Resolution
We introduce a simple and efficient lossless image compression algorithm...
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12/02/2019
A Multigrid Method for Efficiently Training Video Models
Training competitive deep video models is an order of magnitude slower t...
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04/19/2019
Fashion++: Minimal Edits for Outfit Improvement
Given an outfit, what small changes would most improve its fashionabilit...
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12/12/2018
Long-Term Feature Banks for Detailed Video Understanding
To understand the world, we humans constantly need to relate the present...
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04/18/2018
Video Compression through Image Interpolation
An ever increasing amount of our digital communication, media consumptio...
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12/02/2017
Compressed Video Action Recognition
Training robust deep video representations has proven to be much more ch...
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06/23/2017
Sampling Matters in Deep Embedding Learning
Deep embeddings answer one simple question: How similar are two images? ...
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03/31/2017
Spectral Methods for Nonparametric Models
Nonparametric models are versatile, albeit computationally expensive, to...
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12/06/2015