1 citations · 1 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…
T-MOR: Learning Motion-Aware Skeleton Representations for Human Action Recognition
Di Yang, Mahmoud Ali, Quan Kong +2
Vision-language models such as CLIP have recently achieved strong performance on a wide range of visual understanding tasks. However, most existing models rely primarily on appeara…
The Unreasonable Effectiveness of VLMs for Zero-shot Procedural Mistake Detection
Serdar Ozsoy, Lars Doorenbos, Federico Spurio +2
Procedural mistake detection is important for quality control and user assistance across many disciplines. Recent work in this field has achieved significant gains by using the rea…
SnapPose3D: Diffusion-Based Single-Frame 2D-to-3D Lifting of Human Poses
Alessandro Simoni, Riccardo Catalini, Davide Di Nucci +6
Depth ambiguity and joint uncertainty are the two main obstacles in obtaining accurate human pose predictions by 2D-to-3D lifting methods proposed in the literature. In particular,…
Sequence-Adaptive Video Prediction in Continuous Streams using Diffusion Noise Optimization
Sina Mokhtarzadeh Azar, Emad Bahrami, Enrico Pallotta +3
In this work, we investigate diffusion-based video prediction models, which forecast future video frames, for continuous video streams. In this context, the models observe continuo…
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…
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…
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…