activity
20192022
most citedChasing Sparsity in Vision Transformers: An End-to-End Exploration

85 citations · 230 across the 7 of their papers we have counts for

collaborators

12 papers

cs.CV202138 cited

VALUE: A Multi-Task Benchmark for Video-and-Language Understanding Evaluation

Linjie Li, Jie Lei, Zhe Gan +12

Most existing video-and-language (VidL) research focuses on a single dataset, or multiple datasets of a single task. In reality, a truly useful VidL system is expected to be easily…

cs.CV202185 cited

Chasing Sparsity in Vision Transformers: An End-to-End Exploration

Tianlong Chen, Yu Cheng, Zhe Gan +3

Vision transformers (ViTs) have recently received explosive popularity, but their enormous model sizes and training costs remain daunting. Conventional post-training pruning often…

cs.CV20217 cited

CUPID: Adaptive Curation of Pre-training Data for Video-and-Language Representation Learning

Luowei Zhou, Jingjing Liu, Yu Cheng +2

This work concerns video-language pre-training and representation learning. In this now ubiquitous training scheme, a model first performs pre-training on paired videos and text (e…

cs.CV20217 cited

UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training

Mingyang Zhou, Luowei Zhou, Shuohang Wang +4

Vision-and-language pre-training has achieved impressive success in learning multimodal representations between vision and language. To generalize this success to non-English langu…

cs.LG20219 cited

Adversarial Feature Augmentation and Normalization for Visual Recognition

Tianlong Chen, Yu Cheng, Zhe Gan +4

Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective…

cs.CV2021

The Elastic Lottery Ticket Hypothesis

Xiaohan Chen, Yu Cheng, Shuohang Wang +3

Lottery Ticket Hypothesis (LTH) raises keen attention to identifying sparse trainable subnetworks, or winning tickets, which can be trained in isolation to achieve similar or even…