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of 1 004
pro vyhledávání: '"I.5"'
Autor:
Sifnaios, Savvas, Arvanitakis, George, Konstantinidis, Fotios K., Tsimiklis, Georgios, Amditis, Angelos, Frangos, Panayiotis
Recent advancements in computer vision, particularly in detection, segmentation, and classification, have significantly impacted various domains. However, these advancements are tied to RGB-based systems, which are insufficient for applications in in
Externí odkaz:
http://arxiv.org/abs/2409.13498
This study investigates the efficacy of Low-Rank Adaptation (LoRA) in fine-tuning Earth Observation (EO) foundation models for flood segmentation. We hypothesize that LoRA, a parameter-efficient technique, can significantly accelerate the adaptation
Externí odkaz:
http://arxiv.org/abs/2409.09907
We take the perspective in which we want to design a downstream task (such as estimating vegetation coverage) on a certain area of interest (AOI) with a limited labeling budget. By leveraging an existing Foundation Model (FM) we must decide whether w
Externí odkaz:
http://arxiv.org/abs/2409.08744
The global increase in observed forest dieback, characterised by the death of tree foliage, heralds widespread decline in forest ecosystems. This degradation causes significant changes to ecosystem services and functions, including habitat provision
Externí odkaz:
http://arxiv.org/abs/2409.08171
Coffee is one of the most valuable primary commodities. Despite this, the common selection technique of green coffee beans relies on personnel visual inspection, which is labor-intensive and subjective. Therefore, an efficient way to evaluate the qua
Externí odkaz:
http://arxiv.org/abs/2409.04068
Autor:
Carloni, Gianluca, Colantonio, Sara
The aim of this paper is threefold. We inform the AI practitioner about the human visual system with an extensive literature review; we propose a novel biologically motivated neural network for image classification; and, finally, we present a new plu
Externí odkaz:
http://arxiv.org/abs/2409.04360
Autor:
Lafargue, Raphael, Smith, Luke, Vermet, Franck, Löwe, Mathias, Reid, Ian, Gripon, Vincent, Valmadre, Jack
The predominant method for computing confidence intervals (CI) in few-shot learning (FSL) is based on sampling the tasks with replacement, i.e.\ allowing the same samples to appear in multiple tasks. This makes the CI misleading in that it takes into
Externí odkaz:
http://arxiv.org/abs/2409.02850
Autor:
Sugiyama, Kosuke, Uchida, Masato
While precise data observation is essential for the learning processes of predictive models, it can be challenging owing to factors such as insufficient observation accuracy, high collection costs, and privacy constraints. In this paper, we examines
Externí odkaz:
http://arxiv.org/abs/2408.14788
Autor:
Chudasama, Vishal, Sarkar, Hiran, Wasnik, Pankaj, Balasubramanian, Vineeth N, Kalla, Jayateja
Object detection is a critical field in computer vision focusing on accurately identifying and locating specific objects in images or videos. Traditional methods for object detection rely on large labeled training datasets for each object category, w
Externí odkaz:
http://arxiv.org/abs/2408.14249
Autor:
Stripelis, Dimitris, Hu, Zijian, Zhang, Jipeng, Xu, Zhaozhuo, Shah, Alay Dilipbhai, Jin, Han, Yao, Yuhang, Avestimehr, Salman, He, Chaoyang
With the rapid growth of Large Language Models (LLMs) across various domains, numerous new LLMs have emerged, each possessing domain-specific expertise. This proliferation has highlighted the need for quick, high-quality, and cost-effective LLM query
Externí odkaz:
http://arxiv.org/abs/2408.12320