activity
20162026
most citedTransferrable Prototypical Networks for Unsupervised Domain Adaptation

50 citations · 256 across the 32 of their papers we have counts for

collaborators
Showing 2019Show all

7 papers · 1 filter

cs.CV2019★ 5 cited

Multi-Source Domain Adaptation and Semi-Supervised Domain Adaptation with Focus on Visual Domain Adaptation Challenge 2019

Yingwei Pan, Yehao Li, Qi Cai +2

This notebook paper presents an overview and comparative analysis of our systems designed for the following two tasks in Visual Domain Adaptation Challenge (VisDA-2019): multi-sour…

cs.CV2019

Hierarchy Parsing for Image Captioning

Ting Yao, Yingwei Pan, Yehao Li +1

It is always well believed that parsing an image into constituent visual patterns would be helpful for understanding and representing an image. Nevertheless, there has not been evi…

cs.CV2019

Deep Metric Learning with Density Adaptivity

Yehao Li, Ting Yao, Yingwei Pan +2

The problem of distance metric learning is mostly considered from the perspective of learning an embedding space, where the distances between pairs of examples are in correspondenc…

cs.CV2019★ 8 cited

Trimmed Action Recognition, Dense-Captioning Events in Videos, and Spatio-temporal Action Localization with Focus on ActivityNet Challenge 2019

Zhaofan Qiu, Dong Li, Yehao Li +3

This notebook paper presents an overview and comparative analysis of our systems designed for the following three tasks in ActivityNet Challenge 2019: trimmed action recognition, d…

cs.CV2019★ 12 cited

Temporal Deformable Convolutional Encoder-Decoder Networks for Video Captioning

Jingwen Chen, Yingwei Pan, Yehao Li +3

It is well believed that video captioning is a fundamental but challenging task in both computer vision and artificial intelligence fields. The prevalent approach is to map an inpu…

cs.CV2019★ 3 cited

Pointing Novel Objects in Image Captioning

Yehao Li, Ting Yao, Yingwei Pan +2

Image captioning has received significant attention with remarkable improvements in recent advances. Nevertheless, images in the wild encapsulate rich knowledge and cannot be suffi…