activity
20192024
most citedDeepLab2: A TensorFlow Library for Deep Labeling

34 citations · 59 across the 6 of their papers we have counts for

collaborators

9 papers

cs.CV2024

MaskBit: Embedding-free Image Generation via Bit Tokens

Mark Weber, Lijun Yu, Qihang Yu +4

Masked transformer models for class-conditional image generation have become a compelling alternative to diffusion models. Typically comprising two stages - an initial VQGAN model…

cs.CV2024★ 4 cited

An Image is Worth 32 Tokens for Reconstruction and Generation

Qihang Yu, Mark Weber, Xueqing Deng +3

Recent advancements in generative models have highlighted the crucial role of image tokenization in the efficient synthesis of high-resolution images. Tokenization, which transform…

cs.CV2022★ 8 cited

DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation

Aysim Toker, Lukas Kondmann, Mark Weber +10

Earth observation is a fundamental tool for monitoring the evolution of land use in specific areas of interest. Observing and precisely defining change, in this context, requires b…

cs.CV2021★ 34 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.CV2021

4D Panoptic LiDAR Segmentation

Mehmet Aygün, Aljoša Ošep, Mark Weber +4

Temporal semantic scene understanding is critical for self-driving cars or robots operating in dynamic environments. In this paper, we propose 4D panoptic LiDAR segmentation to ass…

cs.CV2021★ 9 cited

STEP: Segmenting and Tracking Every Pixel

Mark Weber, Jun Xie, Maxwell Collins +10

The task of assigning semantic classes and track identities to every pixel in a video is called video panoptic segmentation. Our work is the first that targets this task in a real-…