activity
20182021
most citedDiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision

5 citations · 8 across the 5 of their papers we have counts for

collaborators

11 papers

cs.CV2021

Learning Realistic Human Reposing using Cyclic Self-Supervision with 3D Shape, Pose, and Appearance Consistency

Soubhik Sanyal, Alex Vorobiov, Timo Bolkart +5

Synthesizing images of a person in novel poses from a single image is a highly ambiguous task. Most existing approaches require paired training images; i.e. images of the same pers…

cs.CV20215 cited

DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision

Shiyi Lan, Zhiding Yu, Christopher Choy +5

We introduce DiscoBox, a novel framework that jointly learns instance segmentation and semantic correspondence using bounding box supervision. Specifically, we propose a self-ensem…

cs.CV2021

VideoLT: Large-scale Long-tailed Video Recognition

Xing Zhang, Zuxuan Wu, Zejia Weng +4

Label distributions in real-world are oftentimes long-tailed and imbalanced, resulting in biased models towards dominant labels. While long-tailed recognition has been extensively…

cs.CV2021

Learned Spatial Representations for Few-shot Talking-Head Synthesis

Moustafa Meshry, Saksham Suri, Larry S. Davis +1

We propose a novel approach for few-shot talking-head synthesis. While recent works in neural talking heads have produced promising results, they can still produce images that do n…

cs.CV2021

M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers

Tianrui Guan, Jun Wang, Shiyi Lan +4

We present a novel architecture for 3D object detection, M3DeTR, which combines different point cloud representations (raw, voxels, bird-eye view) with different feature scales bas…

cs.CV2020

SLADE: A Self-Training Framework For Distance Metric Learning

Jiali Duan, Yen-Liang Lin, Son Tran +2

Most existing distance metric learning approaches use fully labeled data to learn the sample similarities in an embedding space. We present a self-training framework, SLADE, to imp…