activity
20172022
most citedOpen-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity

4 citations · 6 across the 5 of their papers we have counts for

collaborators

14 papers

cs.CV20224 cited

Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity

Weiyao Wang, Matt Feiszli, Heng Wang +2

Open-world instance segmentation is the task of grouping pixels into object instances without any pre-determined taxonomy. This is challenging, as state-of-the-art methods rely on…

cs.CV2021

Searching for Two-Stream Models in Multivariate Space for Video Recognition

Xinyu Gong, Heng Wang, Zheng Shou +3

Conventional video models rely on a single stream to capture the complex spatial-temporal features. Recent work on two-stream video models, such as SlowFast network and AssembleNet…

cs.CV2021

Unidentified Video Objects: A Benchmark for Dense, Open-World Segmentation

Weiyao Wang, Matt Feiszli, Heng Wang +1

Current state-of-the-art object detection and segmentation methods work well under the closed-world assumption. This closed-world setting assumes that the list of object categories…

cs.CV2021

Generic Event Boundary Detection: A Benchmark for Event Segmentation

Mike Zheng Shou, Stan Weixian Lei, Weiyao Wang +2

This paper presents a novel task together with a new benchmark for detecting generic, taxonomy-free event boundaries that segment a whole video into chunks. Conventional work in te…

cs.CV20202 cited

FP-NAS: Fast Probabilistic Neural Architecture Search

Zhicheng Yan, Xiaoliang Dai, Peizhao Zhang +3

Differential Neural Architecture Search (NAS) requires all layer choices to be held in memory simultaneously; this limits the size of both search space and final architecture. In c…

cs.CV2020

SF-Net: Single-Frame Supervision for Temporal Action Localization

Fan Ma, Linchao Zhu, Yi Yang +4

In this paper, we study an intermediate form of supervision, i.e., single-frame supervision, for temporal action localization (TAL). To obtain the single-frame supervision, the ann…