activity
20152022
most citedLong-Term Feature Banks for Detailed Video Understanding

33 citations · 86 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2022★ 9 cited

MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition

Chao-Yuan Wu, Yanghao Li, Karttikeya Mangalam +4

While today's video recognition systems parse snapshots or short clips accurately, they cannot connect the dots and reason across a longer range of time yet. Most existing video ar…

cs.CV2019

Fashion++: Minimal Edits for Outfit Improvement

Wei-Lin Hsiao, Isay Katsman, Chao-Yuan Wu +2

Given an outfit, what small changes would most improve its fashionability? This question presents an intriguing new vision challenge. We introduce Fashion++, an approach that propo…

cs.CV2018★ 33 cited

Long-Term Feature Banks for Detailed Video Understanding

Chao-Yuan Wu, Christoph Feichtenhofer, Haoqi Fan +3

To understand the world, we humans constantly need to relate the present to the past, and put events in context. In this paper, we enable existing video models to do the same. We p…

cs.CV2018★ 20 cited

Video Compression through Image Interpolation

Chao-Yuan Wu, Nayan Singhal, Philipp Krähenbühl

An ever increasing amount of our digital communication, media consumption, and content creation revolves around videos. We share, watch, and archive many aspects of our lives throu…

cs.CV2017★ 20 cited

Compressed Video Action Recognition

Chao-Yuan Wu, Manzil Zaheer, Hexiang Hu +3

Training robust deep video representations has proven to be much more challenging than learning deep image representations. This is in part due to the enormous size of raw video st…

cs.LG2017★ 3 cited

Spectral Methods for Nonparametric Models

Hsiao-Yu Fish Tung, Chao-Yuan Wu, Manzil Zaheer +1

Nonparametric models are versatile, albeit computationally expensive, tool for modeling mixture models. In this paper, we introduce spectral methods for the two most popular nonpar…

cs.LG2015★ 1 cited

Explaining reviews and ratings with PACO: Poisson Additive Co-Clustering

Chao-Yuan Wu, Alex Beutel, Amr Ahmed +1

Understanding a user's motivations provides valuable information beyond the ability to recommend items. Quite often this can be accomplished by perusing both ratings and review tex…