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20112023
most citedLLM as A Robotic Brain: Unifying Egocentric Memory and Control

10 citations · 53 across the 17 of their papers we have counts for

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8 papers · 1 filter

cs.CV20235 cited

FreeDoM: Training-Free Energy-Guided Conditional Diffusion Model

Jiwen Yu, Yinhuai Wang, Chen Zhao +2

Recently, conditional diffusion models have gained popularity in numerous applications due to their exceptional generation ability. However, many existing methods are training-requ…

cs.CV2023

Re-ReND: Real-time Rendering of NeRFs across Devices

Sara Rojas, Jesus Zarzar, Juan Camilo Perez +4

This paper proposes a novel approach for rendering a pre-trained Neural Radiance Field (NeRF) in real-time on resource-constrained devices. We introduce Re-ReND, a method enabling…

cs.CV20233 cited

Look, Listen, and Attack: Backdoor Attacks Against Video Action Recognition

Hasan Abed Al Kader Hammoud, Shuming Liu, Mohammed Alkhrashi +2

Deep neural networks (DNNs) are vulnerable to a class of attacks called "backdoor attacks", which create an association between a backdoor trigger and a target label the attacker i…

cs.CV20224 cited

Negative Frames Matter in Egocentric Visual Query 2D Localization

Mengmeng Xu, Cheng-Yang Fu, Yanghao Li +3

The recently released Ego4D dataset and benchmark significantly scales and diversifies the first-person visual perception data. In Ego4D, the Visual Queries 2D Localization task ai…

cs.CV2022

Egocentric Video-Language Pretraining @ Ego4D Challenge 2022

Kevin Qinghong Lin, Alex Jinpeng Wang, Mattia Soldan +13

In this report, we propose a video-language pretraining (VLP) based solution \cite{kevin2022egovlp} for four Ego4D challenge tasks, including Natural Language Query (NLQ), Moment Q…

cs.CV20221 cited

vCLIMB: A Novel Video Class Incremental Learning Benchmark

Andrés Villa, Kumail Alhamoud, Juan León Alcázar +3

Continual learning (CL) is under-explored in the video domain. The few existing works contain splits with imbalanced class distributions over the tasks, or study the problem in uns…