15 citations · 39 across the 14 of their papers we have counts for
15 papers
Looking into the Unknown: Exploring Action Discovery for Segmentation of Known and Unknown Actions
Federico Spurio, Emad Bahrami, Olga Zatsarynna +3
We introduce Action Discovery, a novel setup within Temporal Action Segmentation that addresses the challenge of defining and annotating ambiguous actions and incomplete annotation…
MANTA: Diffusion Mamba for Efficient and Effective Stochastic Long-Term Dense Anticipation
Olga Zatsarynna, Emad Bahrami, Yazan Abu Farha +2
Long-term dense action anticipation is very challenging since it requires predicting actions and their durations several minutes into the future based on provided video observation…
Gated Temporal Diffusion for Stochastic Long-Term Dense Anticipation
Olga Zatsarynna, Emad Bahrami, Yazan Abu Farha +2
Long-term action anticipation has become an important task for many applications such as autonomous driving and human-robot interaction. Unlike short-term anticipation, predicting…
Rethinking temporal self-similarity for repetitive action counting
Yanan Luo, Jinhui Yi, Yazan Abu Farha +2
Counting repetitive actions in long untrimmed videos is a challenging task that has many applications such as rehabilitation. State-of-the-art methods predict action counts by firs…
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…
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…
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…
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…
Pose Refinement Graph Convolutional Network for Skeleton-based Action Recognition
Shijie Li, Jinhui Yi, Yazan Abu Farha +1
With the advances in capturing 2D or 3D skeleton data, skeleton-based action recognition has received an increasing interest over the last years. As skeleton data is commonly repre…
Long-Term Anticipation of Activities with Cycle Consistency
Yazan Abu Farha, Qiuhong Ke, Bernt Schiele +1
With the success of deep learning methods in analyzing activities in videos, more attention has recently been focused towards anticipating future activities. However, most of the w…
MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation
Shijie Li, Yazan Abu Farha, Yun Liu +2
With the success of deep learning in classifying short trimmed videos, more attention has been focused on temporally segmenting and classifying activities in long untrimmed videos.…
Uncertainty-Aware Anticipation of Activities
Yazan Abu Farha, Juergen Gall
Anticipating future activities in video is a task with many practical applications. While earlier approaches are limited to just a few seconds in the future, the prediction time ho…
MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation
Yazan Abu Farha, Juergen Gall
Temporally locating and classifying action segments in long untrimmed videos is of particular interest to many applications like surveillance and robotics. While traditional approa…
When will you do what? - Anticipating Temporal Occurrences of Activities
Yazan Abu Farha, Alexander Richard, Juergen Gall
Analyzing human actions in videos has gained increased attention recently. While most works focus on classifying and labeling observed video frames or anticipating the very recent…