Computer Vision74 [2023-2] 백승우 - AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to rep arxiv.org 0. Abstract 트랜스포머 아키텍처는 자연어 처리 작업의 사실상의 표준이 되었지만, 컴퓨터 비전.. 2024. 1. 30. [2023-2] 주서영 - EEG2IMAGE: Image Reconstruction from EEG Brain Signals EEG2IMAGE: Image Reconstruction from EEG Brain Signals Reconstructing images using brain signals of imagined visuals may provide an augmented vision to the disabled, leading to the advancement of Brain-Computer Interface (BCI) technology. The recent progress in deep learning has boosted the study area of synth arxiv.org GitHub - prajwalsingh/EEG2Image: EEG2IMAGE: Image Reconstruction from EEG Br.. 2024. 1. 24. [2023-2] 염제원 - TASK2VEC: Task Embedding for Meta-Learning https://arxiv.org/abs/1902.03545 Task2Vec: Task Embedding for Meta-Learning We introduce a method to provide vectorial representations of visual classification tasks which can be used to reason about the nature of those tasks and their relations. Given a dataset with ground-truth labels and a loss function defined over those label arxiv.org Abstract Visual Classification Task에서 Task를 Vector로 표현하.. 2024. 1. 7. [2023-2] 주서영 - Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks Neural network based computer vision systems are typically built on a backbone, a pretrained or randomly initialized feature extractor. Several years ago, the default option was an ImageNet-trained convolutional neural network. However, the recent past has arxiv.org Abstract neural network 기반의 com.. 2024. 1. 2. 이전 1 ··· 14 15 16 17 18 19 다음
