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…
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…
MixANT: Observation-dependent Memory Propagation for Stochastic Dense Action Anticipation
Syed Talal Wasim, Hamid Suleman, Olga Zatsarynna +2
We present MixANT, a novel architecture for stochastic long-term dense anticipation of human activities. While recent State Space Models (SSMs) like Mamba have shown promise throug…
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…