3 citations · 5 across the 9 of their papers we have counts for
6 papers · 1 filter
Action-Guided Attention for Video Action Anticipation
Tsung-Ming Tai, Sofia Casarin, Andrea Pilzer +2
Anticipating future actions in videos is challenging, as the observed frames provide only evidence of past activities, requiring the inference of latent intentions to predict upcom…
Robust Feature Learning Against Noisy Labels
Tsung-Ming Tai, Yun-Jie Jhang, Wen-Jyi Hwang
Supervised learning of deep neural networks heavily relies on large-scale datasets annotated by high-quality labels. In contrast, mislabeled samples can significantly degrade the g…
Inductive Attention for Video Action Anticipation
Tsung-Ming Tai, Giuseppe Fiameni, Cheng-Kuang Lee +2
Anticipating future actions based on spatiotemporal observations is essential in video understanding and predictive computer vision. Moreover, a model capable of anticipating the f…
NVIDIA-UNIBZ Submission for EPIC-KITCHENS-100 Action Anticipation Challenge 2022
Tsung-Ming Tai, Oswald Lanz, Giuseppe Fiameni +5
In this report, we describe the technical details of our submission for the EPIC-Kitchen-100 action anticipation challenge. Our modelings, the higher-order recurrent space-time tra…
Unified Recurrence Modeling for Video Action Anticipation
Tsung-Ming Tai, Giuseppe Fiameni, Cheng-Kuang Lee +2
Forecasting future events based on evidence of current conditions is an innate skill of human beings, and key for predicting the outcome of any decision making. In artificial visio…
Higher Order Recurrent Space-Time Transformer for Video Action Prediction
Tsung-Ming Tai, Giuseppe Fiameni, Cheng-Kuang Lee +1
Endowing visual agents with predictive capability is a key step towards video intelligence at scale. The predominant modeling paradigm for this is sequence learning, mostly impleme…