Essam Sleiman — accepted AI-conference papers

2024

Goldfish: Vision-Language Understanding of Arbitrarily Long Videos

Kirolos Ataallah*, Xiaoqian shen, Eslam mohamed abdelrahman*, Essam Sleiman, Mingchen Zhuge, Jian Ding +3

ECCV 2024poster

"Most current LLM-based models for video understanding can process videos within minutes. However, they struggle with lengthy videos due to challenges such as “noise and redundancy”, as well as “memory and computation” constraints. In this paper, we present , a methodology tailored for comprehending…

Cited by 15 Source ↗PDF ↗Scholar ↗

2024

SlowFormer: Adversarial Attack on Compute and Energy Consumption of Efficient Vision Transformers

K L Navaneet, Soroush Abbasi Koohpayegani, Essam Sleiman, Hamed Pirsiavash

CVPR 2024poster

Recently there has been a lot of progress in reducing the computation of deep models at inference time. These methods can reduce both the computational needs and power usage of deep models. Some of these approaches adaptively scale the compute based on the input instance. We show that such models ca…

Cited by 3 Source ↗PDF ↗code ↗Scholar ↗

2023

Mitigating Negative Transfer in Multi-Task Learning with Exponential Moving Average Loss Weighting Strategies (Student Abstract)

Anish Lakkapragada, Essam Sleiman, Saimourya Surabhi, Dennis P. Wall

AAAI 2023technical

Multi-Task Learning (MTL) is a growing subject of interest in deep learning, due to its ability to train models more efficiently on multiple tasks compared to using a group of conventional single-task models. However, MTL can be impractical as certain tasks can dominate training and hurt performance…

Cited by 11 Source ↗PDF ↗Scholar ↗