6 papers
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…
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…
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…
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…