361 citations · 1.3k across the 81 of their papers we have counts for
14 papers · 1 filter
Keypoint Message Passing for Video-based Person Re-Identification
Di Chen, Andreas Doering, Shanshan Zhang +3
Video-based person re-identification (re-ID) is an important technique in visual surveillance systems which aims to match video snippets of people captured by different cameras. Ex…
Adaptive Token Sampling For Efficient Vision Transformers
Mohsen Fayyaz, Soroush Abbasi Koohpayegani, Farnoush Rezaei Jafari +5
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…
TaylorSwiftNet: Taylor Driven Temporal Modeling for Swift Future Frame Prediction
Saber Pourheydari, Emad Bahrami, Mohsen Fayyaz +3
While recurrent neural networks (RNNs) demonstrate outstanding capabilities for future video frame prediction, they model dynamics in a discrete time space, i.e., they predict the…
Long Short View Feature Decomposition via Contrastive Video Representation Learning
Nadine Behrmann, Mohsen Fayyaz, Juergen Gall +1
Self-supervised video representation methods typically focus on the representation of temporal attributes in videos. However, the role of stationary versus non-stationary attribute…
FIFA: Fast Inference Approximation for Action Segmentation
Yaser Souri, Yazan Abu Farha, Fabien Despinoy +2
We introduce FIFA, a fast approximate inference method for action segmentation and alignment. Unlike previous approaches, FIFA does not rely on expensive dynamic programming for in…
Multi-Modal Temporal Convolutional Network for Anticipating Actions in Egocentric Videos
Olga Zatsarynna, Yazan Abu Farha, Juergen Gall
Anticipating human actions is an important task that needs to be addressed for the development of reliable intelligent agents, such as self-driving cars or robot assistants. While…
Towards Better Adversarial Synthesis of Human Images from Text
Rania Briq, Pratika Kochar, Juergen Gall
This paper proposes an approach that generates multiple 3D human meshes from text. The human shapes are represented by 3D meshes based on the SMPL model. The model's performance is…
Using Visual Anomaly Detection for Task Execution Monitoring
Santosh Thoduka, Juergen Gall, Paul G. Plöger
Execution monitoring is essential for robots to detect and respond to failures. Since it is impossible to enumerate all failures for a given task, we learn from successful executio…
Learning to Generate Novel Scene Compositions from Single Images and Videos
Vadim Sushko, Juergen Gall, Anna Khoreva
Training GANs in low-data regimes remains a challenge, as overfitting often leads to memorization or training divergence. In this work, we introduce One-Shot GAN that can learn to…
Temporal Action Segmentation from Timestamp Supervision
Zhe Li, Yazan Abu Farha, Juergen Gall
Temporal action segmentation approaches have been very successful recently. However, annotating videos with frame-wise labels to train such models is very expensive and time consum…
Generating Novel Scene Compositions from Single Images and Videos
Vadim Sushko, Dan Zhang, Juergen Gall +1
Given a large dataset for training, generative adversarial networks (GANs) can achieve remarkable performance for the image synthesis task. However, training GANs in extremely low…
Iterative Greedy Matching for 3D Human Pose Tracking from Multiple Views
Julian Tanke, Juergen Gall
In this work we propose an approach for estimating 3D human poses of multiple people from a set of calibrated cameras. Estimating 3D human poses from multiple views has several com…
Hierarchical Graph-RNNs for Action Detection of Multiple Activities
Sovan Biswas, Yaser Souri, Juergen Gall
In this paper, we propose an approach that spatially localizes the activities in a video frame where each person can perform multiple activities at the same time. Our approach take…
Discovering Multi-Label Actor-Action Association in a Weakly Supervised Setting
Sovan Biswas, Juergen Gall
Since collecting and annotating data for spatio-temporal action detection is very expensive, there is a need to learn approaches with less supervision. Weakly supervised approaches…