activity
20172021
most citedRethinking Atrous Convolution for Semantic Image Segmentation

7.5k citations · 7.9k across the 11 of their papers we have counts for

collaborators

21 papers

cs.CV202134 cited

DeepLab2: A TensorFlow Library for Deep Labeling

Mark Weber, Huiyu Wang, Siyuan Qiao +12

DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a state-of-the-art and easy-to-use TensorFlow codebase for general dense pixel prediction problems in computer…

cs.CV20207 cited

ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation

Siyuan Qiao, Yukun Zhu, Hartwig Adam +2

In this paper, we present ViP-DeepLab, a unified model attempting to tackle the long-standing and challenging inverse projection problem in vision, which we model as restoring the…

cs.CV2020

MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers

Huiyu Wang, Yukun Zhu, Hartwig Adam +2

We present MaX-DeepLab, the first end-to-end model for panoptic segmentation. Our approach simplifies the current pipeline that depends heavily on surrogate sub-tasks and hand-desi…

cs.CV202028 cited

Scaling Wide Residual Networks for Panoptic Segmentation

Liang-Chieh Chen, Huiyu Wang, Siyuan Qiao

The Wide Residual Networks (Wide-ResNets), a shallow but wide model variant of the Residual Networks (ResNets) by stacking a small number of residual blocks with large channel size…

cs.CV2020

DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution

Siyuan Qiao, Liang-Chieh Chen, Alan Yuille

Many modern object detectors demonstrate outstanding performances by using the mechanism of looking and thinking twice. In this paper, we explore this mechanism in the backbone des…

cs.CV202012 cited

Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation

Liang-Chieh Chen, Raphael Gontijo Lopes, Bowen Cheng +5

Supervised learning in large discriminative models is a mainstay for modern computer vision. Such an approach necessitates investing in large-scale human-annotated datasets for ach…