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Jürgen Gall

6 accepted papers

2022

Adaptive Token Sampling for Efficient Vision Transformers

ECCV 2022poster

"While state-of-the-art vision transformer models achieve promising results in image classification, they are computationally expensive and require many GFLOPs. Although the GFLOPs of a vision transformer can be decreased by reducing the number of tokens in the network, there is no setting that is o…

2022

PoseTrack21: A Dataset for Person Search, Multi-Object Tracking and Multi-Person Pose Tracking

CVPR 2022poster

Current research evaluates person search, multi-object tracking and multi-person pose estimation as separate tasks and on different datasets although these tasks are very akin to each other and comprise similar sub-tasks, e.g. person detection or appearance-based association of detected persons. Con…

Cited by 60PDFcodeScholar
2022

TAVA: Template-Free Animatable Volumetric Actors

ECCV 2022poster

"Coordinate-based volumetric representations have the potential to generate photo-realistic virtual avatars from images. However, virtual avatars need to be controllable and be rendered in novel poses that may not have been observed. Traditional techniques, such as LBS, provide such a controlling fu…

2022

Unified Fully and Timestamp Supervised Temporal Action Segmentation via Sequence to Sequence Translation

ECCV 2022poster

"This paper introduces a unified framework for video action segmentation via sequence to sequence (seq2seq) translation in a fully and timestamp supervised setup. In contrast to current state-of-the-art frame-level prediction methods, we view action segmentation as a seq2seq translation task, i.e.,…

2021

Moving Object Segmentation in 3D LiDAR Data: A Learning-Based Approach Exploiting Sequential Data

RA-L 2021

The ability to detect and segment moving objects in a scene is essential for building consistent maps, making future state predictions, avoiding collisions, and planning. In this letter, we address the problem of moving object segmentation from 3D LiDAR scans. We propose a novel approach that pushes

Cited by 228SourcecodeScholar
2020

Large Scale Holistic Video Understanding

ECCV 2020poster

Video recognition has been advanced in recent years by benchmarks with rich annotations. However, research is still mainly limited to human action or sports recognition - focusing on a highly specific video understanding task and thus leaving a significant gap towards describing the overall content…