activity
20172022
most citedA Hybrid RNN-HMM Approach for Weakly Supervised Temporal Action Segmentation

99 citations · 253 across the 33 of their papers we have counts for

collaborators

60 papers

cs.CV20223 cited

Robust Action Segmentation from Timestamp Supervision

Yaser Souri, Yazan Abu Farha, Emad Bahrami +2

Action segmentation is the task of predicting an action label for each frame of an untrimmed video. As obtaining annotations to train an approach for action segmentation in a fully…

cs.CV20222 cited

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

Nadine Behrmann, S. Alireza Golestaneh, Zico Kolter +2

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 cur…

cs.CV20225 cited

Dual Pyramid Generative Adversarial Networks for Semantic Image Synthesis

Shijie Li, Ming-Ming Cheng, Juergen Gall

The goal of semantic image synthesis is to generate photo-realistic images from semantic label maps. It is highly relevant for tasks like content generation and image editing. Curr…

cs.CV2022

Self-supervised Learning for Unintentional Action Prediction

Olga Zatsarynna, Yazan Abu Farha, Juergen Gall

Distinguishing if an action is performed as intended or if an intended action fails is an important skill that not only humans have, but that is also important for intelligent syst…

cs.CV20221 cited

One-Shot Synthesis of Images and Segmentation Masks

Vadim Sushko, Dan Zhang, Juergen Gall +1

Joint synthesis of images and segmentation masks with generative adversarial networks (GANs) is promising to reduce the effort needed for collecting image data with pixel-wise anno…

cs.CV20223 cited

Ranking Info Noise Contrastive Estimation: Boosting Contrastive Learning via Ranked Positives

David T. Hoffmann, Nadine Behrmann, Juergen Gall +2

This paper introduces Ranking Info Noise Contrastive Estimation (RINCE), a new member in the family of InfoNCE losses that preserves a ranked ordering of positive samples. In contr…