activity
20152023
most citedLearning to Propagate Labels: Transductive Propagation Network for Few-shot Learning

517 citations · 1.7k across the 49 of their papers we have counts for

collaborators
Showing 2020Show all

10 papers · 1 filter

cs.CV2020★ 1 cited

ActBERT: Learning Global-Local Video-Text Representations

Linchao Zhu, Yi Yang

In this paper, we introduce ActBERT for self-supervised learning of joint video-text representations from unlabeled data. First, we leverage global action information to catalyze t…

cs.CV2020

Describing Unseen Videos via Multi-Modal Cooperative Dialog Agents

Ye Zhu, Yu Wu, Yi Yang +1

With the arising concerns for the AI systems provided with direct access to abundant sensitive information, researchers seek to develop more reliable AI with implicit information s…

cs.CV2020★ 76 cited

Parameter-Efficient Person Re-identification in the 3D Space

Zhedong Zheng, Nenggan Zheng, Yi Yang

People live in a 3D world. However, existing works on person re-identification (re-id) mostly consider the semantic representation learning in a 2D space, intrinsically limiting th…

stat.ML2020★ 4 cited

Feature Robust Optimal Transport for High-dimensional Data

Mathis Petrovich, Chao Liang, Ryoma Sato +6

Optimal transport is a machine learning problem with applications including distribution comparison, feature selection, and generative adversarial networks. In this paper, we propo…

cs.CV2020★ 6 cited

OpenMix: Reviving Known Knowledge for Discovering Novel Visual Categories in An Open World

Zhun Zhong, Linchao Zhu, Zhiming Luo +3

In this paper, we tackle the problem of discovering new classes in unlabeled visual data given labeled data from disjoint classes. Existing methods typically first pre-train a mode…

cs.CV2020★ 6 cited

Motion-Excited Sampler: Video Adversarial Attack with Sparked Prior

Hu Zhang, Linchao Zhu, Yi Zhu +1

Deep neural networks are known to be susceptible to adversarial noise, which are tiny and imperceptible perturbations. Most of previous work on adversarial attack mainly focus on i…