8 citations · 13 across the 4 of their papers we have counts for
4 papers · 1 filter
PromptonomyViT: Multi-Task Prompt Learning Improves Video Transformers using Synthetic Scene Data
Roei Herzig, Ofir Abramovich, Elad Ben-Avraham +5
Action recognition models have achieved impressive results by incorporating scene-level annotations, such as objects, their relations, 3D structure, and more. However, obtaining an…
Structured Video Tokens @ Ego4D PNR Temporal Localization Challenge 2022
Elad Ben-Avraham, Roei Herzig, Karttikeya Mangalam +5
This technical report describes the SViT approach for the Ego4D Point of No Return (PNR) Temporal Localization Challenge. We propose a learning framework StructureViT (SViT for sho…
Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens
Elad Ben-Avraham, Roei Herzig, Karttikeya Mangalam +5
Recent action recognition models have achieved impressive results by integrating objects, their locations and interactions. However, obtaining dense structured annotations for each…
Object-Region Video Transformers
Roei Herzig, Elad Ben-Avraham, Karttikeya Mangalam +5
Recently, video transformers have shown great success in video understanding, exceeding CNN performance; yet existing video transformer models do not explicitly model objects, alth…