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Deep Variational Bayesian Modeling of Haze Degradation Process

Autor: Im, Eun Woo, Shin, Junsung, Baik, Sungyong, Kim, Tae Hyun

Publikováno v: In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management 2023 Oct 21 (pp. 895-904)

Relying on the representation power of neural networks, most recent works have often neglected several factors involved in haze degradation, such as transmission (the amount of light reaching an observer from a scene over distance) and atmospheric li

Externí odkaz: http://arxiv.org/abs/2412.03745

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VidHalluc: Evaluating Temporal Hallucinations in Multimodal Large Language Models for Video Understanding

Autor: Li, Chaoyu, Im, Eun Woo, Fazli, Pooyan

Multimodal large language models (MLLMs) have recently shown significant advancements in video understanding, excelling in content reasoning and instruction-following tasks. However, the problem of hallucination, where models generate inaccurate or m

Externí odkaz: http://arxiv.org/abs/2412.03735

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Harnessing Meta-Learning for Improving Full-Frame Video Stabilization

Autor: Ali, Muhammad Kashif, Im, Eun Woo, Kim, Dongjin, Kim, Tae Hyun

Video stabilization is a longstanding computer vision problem, particularly pixel-level synthesis solutions for video stabilization which synthesize full frames add to the complexity of this task. These techniques aim to stabilize videos by synthesiz

Externí odkaz: http://arxiv.org/abs/2403.03662

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