29 citations · 47 across the 6 of their papers we have counts for
6 papers
MMViT: Multiscale Multiview Vision Transformers
Yuchen Liu, Natasha Ong, Kaiyan Peng +8
We present Multiscale Multiview Vision Transformers (MMViT), which introduces multiscale feature maps and multiview encodings to transformer models. Our model encodes different vie…
Reversible Vision Transformers
Karttikeya Mangalam, Haoqi Fan, Yanghao Li +4
We present Reversible Vision Transformers, a memory efficient architecture design for visual recognition. By decoupling the GPU memory requirement from the depth of the model, Reve…
Normalized Contrastive Learning for Text-Video Retrieval
Yookoon Park, Mahmoud Azab, Bo Xiong +4
Cross-modal contrastive learning has led the recent advances in multimodal retrieval with its simplicity and effectiveness. In this work, however, we reveal that cross-modal contra…
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…
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
Yanghao Li, Chao-Yuan Wu, Haoqi Fan +4
In this paper, we study Multiscale Vision Transformers (MViTv2) as a unified architecture for image and video classification, as well as object detection. We present an improved ve…
PyTorchVideo: A Deep Learning Library for Video Understanding
Haoqi Fan, Tullie Murrell, Heng Wang +13
We introduce PyTorchVideo, an open-source deep-learning library that provides a rich set of modular, efficient, and reproducible components for a variety of video understanding tas…