Zobrazeno 1 - 10
of 121
pro vyhledávání: '"Chin Poo Lee"'
Publikováno v:
Automatika, Vol 65, Iss 3, Pp 1113-1126 (2024)
This paper introduces Knowledge Distillation of Vision Transformer (KDViT), a novel approach for medical image classification. The Vision Transformer architecture incorporates a self-attention mechanism to autonomously learn image structure. The inpu
Externí odkaz:
https://doaj.org/article/20fe7d43898d4e94ba3e07eb66f52636
Autor:
Qi Zhi Lim, Chin Poo Lee, Kian Ming Lim, Jing Xiang Ng, Eric Khang Heng Ooi, Nicole Kai Ning Loh
Publikováno v:
Emerging Science Journal, Vol 8, Iss 2, Pp 675-686 (2024)
In natural language processing (NLP), a Question Answering System (QAS) refers to a system or model that is designed to understand and respond to user queries in natural language. As we navigate through the recent advancements in QAS, it can be obser
Externí odkaz:
https://doaj.org/article/bbd416ec23544b148f8158712193b63a
Publikováno v:
IEEE Access, Vol 12, Pp 108573-108583 (2024)
Individual deep learning models showcase impressive performance; however, the capacity of a single model might fall short in capturing the full spectrum of intricate patterns present in the input data. Thus, relying solely on a single model may hampe
Externí odkaz:
https://doaj.org/article/9969fcb20ab740fb92063157b7e7f124
Publikováno v:
IEEE Access, Vol 12, Pp 86690-86703 (2024)
The few-shot learning paradigm aims to generalize to unseen tasks with limited samples. However, a focus solely on class-level discrimination may fall short of achieving robust generalization, especially when neglecting instance diversity and discrim
Externí odkaz:
https://doaj.org/article/1b1e2b1f1c2d47068e0194e39ab9e5e9
Publikováno v:
IEEE Access, Vol 12, Pp 79327-79341 (2024)
Facial Expression Recognition (FER) is a machine learning problem that deals with recognizing human facial expressions. While existing work has achieved performance improvements in recent years, FER in the wild and under challenging conditions remain
Externí odkaz:
https://doaj.org/article/97158384496449a9946f95f7fec3263e
Publikováno v:
IEEE Access, Vol 12, Pp 74069-74080 (2024)
Sentiment analysis, a pivotal task in natural language processing, aims to discern opinions and emotions expressed in text. However, existing methods for sentiment analysis face various challenges such as data scarcity, complex language patterns, and
Externí odkaz:
https://doaj.org/article/58afcf743e104c8e8fc4930619508020
Publikováno v:
IEEE Access, Vol 12, Pp 74872-74887 (2024)
Knowledge Graph Question Answering (KGQA) plays a crucial role in extracting valuable insights from interconnected information. Existing methods, while commendable, face challenges such as contextual ambiguity and limited adaptability to diverse know
Externí odkaz:
https://doaj.org/article/025c720740ad4fe4a01155944ad87409
Publikováno v:
IEEE Access, Vol 12, Pp 71505-71519 (2024)
Multimodal Question Answering (MMQA) has emerged as a challenging frontier at the intersection of natural language processing (NLP) and computer vision, demanding the integration of diverse modalities for effective comprehension and response. While p
Externí odkaz:
https://doaj.org/article/f296d12e92a143cd950ca500367b3712
Publikováno v:
IEEE Access, Vol 12, Pp 58586-58596 (2024)
Few-shot image classification remains a persistent challenge due to the intrinsic difficulty faced by visual recognition models in achieving generalization with limited training data. Existing methods primarily focus on exploiting marginal distributi
Externí odkaz:
https://doaj.org/article/9c1c0053ba0e4006a02ddb70decc6a98
Publikováno v:
IEEE Access, Vol 12, Pp 52867-52877 (2024)
Few-shot learning continues to pose a challenge as it is inherently difficult for visual recognition models to generalize with limited labeled examples. When the training data is limited, the process of training and fine-tuning the model will be unst
Externí odkaz:
https://doaj.org/article/fcdc5b5adb714b9c9e1d3d9f195a18f6