2 citations · 3 across the 11 of their papers we have counts for
11 papers
Post-Training VLMs for Video Mistake Detection
Federico Spurio, Olga Zatsarynna, Lars Doorenbos +3
Human mistakes are inevitable when following instructions, yet they can lead to severe consequences. As such, there has been an increased interest in developing methods for detecti…
TRAFA: Anticipating User Actions to Reduce Errors in Procedural Tasks with Predictive Feedback
Sassan Mokhtar, Lars Doorenbos, Fatemeh Jabbari +3
Interactive assistance systems typically provide feedback after an action has been completed, supporting error recovery but not preventing the error itself. We present TRAFA, a rea…
STRIVE: Structured Spatiotemporal Exploration for Reinforcement Learning in Video Question Answering
Emad Bahrami, Olga Zatsarynna, Parth Pathak +3
We introduce STRIVE (SpatioTemporal Reinforcement with Importance-aware Variant Exploration), a structured reinforcement learning framework for video question answering. While grou…
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…
Privacy-Preserving Semantic Segmentation from Ultra-Low-Resolution RGB Inputs
Xuying Huang, Sicong Pan, Olga Zatsarynna +2
RGB-based semantic segmentation has become a mainstream approach for visual perception and is widely applied in a variety of downstream tasks. However, existing methods typically r…
Towards Generalizing Temporal Action Segmentation to Unseen Views
Emad Bahrami, Olga Zatsarynna, Gianpiero Francesca +1
While there has been substantial progress in temporal action segmentation, the challenge to generalize to unseen views remains unaddressed. Hence, we define a protocol for unseen v…
Hierarchical Vector Quantization for Unsupervised Action Segmentation
Federico Spurio, Emad Bahrami, Gianpiero Francesca +1
In this work, we address unsupervised temporal action segmentation, which segments a set of long, untrimmed videos into semantically meaningful segments that are consistent across…
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…
DiffAnt: Diffusion Models for Action Anticipation
Zeyun Zhong, Chengzhi Wu, Manuel Martin +3
Anticipating future actions is inherently uncertain. Given an observed video segment containing ongoing actions, multiple subsequent actions can plausibly follow. This uncertainty…
A Survey on Deep Learning Techniques for Action Anticipation
Zeyun Zhong, Manuel Martin, Michael Voit +2
The ability to anticipate possible future human actions is essential for a wide range of applications, including autonomous driving and human-robot interaction. Consequently, numer…