Vision Transformers for Computer Go
- URL: http://arxiv.org/abs/2309.12675v1
- Date: Fri, 22 Sep 2023 07:35:37 GMT
- Title: Vision Transformers for Computer Go
- Abstract summary: This study focuses on the analysis of the Transformer in Vision.
Through a detailed analysis of numerous points such as prediction accuracy, win rates, memory, speed, size, or even learning rate, we have been able to highlight the substantial role that transformers can play in the game of Go.
- Score: 4.111084095218968
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Motivated by the success of transformers in various fields, such as language
understanding and image analysis, this investigation explores their application
in the context of the game of Go. In particular, our study focuses on the
analysis of the Transformer in Vision. Through a detailed analysis of numerous
points such as prediction accuracy, win rates, memory, speed, size, or even
learning rate, we have been able to highlight the substantial role that
transformers can play in the game of Go. This study was carried out by
comparing them to the usual Residual Networks.
Related papers
- Transformer Interpretability from Perspective of Attention and Gradient [47.816447130827555]
Gradient is widely utilized in Transformer interpretation.<n>We propose a method to achieve it by guiding the gradient direction, or more precisely, the attention direction.
arXiv Detail & Related papers (2026-05-12T01:28:01Z) - Do Vision-Language Transformers Exhibit Visual Commonsense? An Empirical Study of VCR [51.72751335574947]
Visual Commonsense Reasoning (VCR) calls for explanatory reasoning behind question answering over visual scenes.
Progress on the benchmark dataset stems largely from the recent advancement of Vision-Language Transformers (VL Transformers)
This paper posits that the VL Transformers do not exhibit visual commonsense, which is the key to VCR.
arXiv Detail & Related papers (2024-05-27T08:26:58Z) - Advances in Medical Image Analysis with Vision Transformers: A
Comprehensive Review [6.953789750981636]
We provide an encyclopedic review of the applications of Transformers in medical imaging.
Specifically, we present a systematic and thorough review of relevant recent Transformer literature for different medical image analysis tasks.
arXiv Detail & Related papers (2023-01-09T16:56:23Z) - Learning Explicit Object-Centric Representations with Vision
Transformers [81.38804205212425]
We build on the self-supervision task of masked autoencoding and explore its effectiveness for learning object-centric representations with transformers.
We show that the model efficiently learns to decompose simple scenes as measured by segmentation metrics on several multi-object benchmarks.
arXiv Detail & Related papers (2022-10-25T16:39:49Z) - Vision Transformers for Action Recognition: A Survey [41.69370782177517]
Vision transformers are emerging as a powerful tool to solve computer vision problems.
Recent techniques have proven the efficacy of transformers beyond the image domain to solve numerous video-related tasks.
Human action recognition is receiving special attention from the research community due to its widespread applications.
arXiv Detail & Related papers (2022-09-13T02:57:05Z) - 3D Vision with Transformers: A Survey [114.86385193388439]
The success of the transformer architecture in natural language processing has triggered attention in the computer vision field.
We present a systematic and thorough review of more than 100 transformers methods for different 3D vision tasks.
We discuss transformer design in 3D vision, which allows it to process data with various 3D representations.
arXiv Detail & Related papers (2022-08-08T17:59:11Z) - Vision Transformers: State of the Art and Research Challenges [26.462994554165697]
This paper presents a comprehensive overview of the literature on different architecture designs and training tricks for vision transformers.
Our goal is to provide a systematic review with the open research opportunities.
arXiv Detail & Related papers (2022-07-07T02:01:56Z) - Transformers in Time Series: A Survey [66.50847574634726]
We systematically review Transformer schemes for time series modeling by highlighting their strengths as well as limitations.
From the perspective of network structure, we summarize the adaptations and modifications that have been made to Transformers.
From the perspective of applications, we categorize time series Transformers based on common tasks including forecasting, anomaly detection, and classification.
arXiv Detail & Related papers (2022-02-15T01:43:27Z) - Transformers in Vision: A Survey [101.07348618962111]
Transformers enable modeling long dependencies between input sequence elements and support parallel processing of sequence.
Transformers require minimal inductive biases for their design and are naturally suited as set-functions.
This survey aims to provide a comprehensive overview of the Transformer models in the computer vision discipline.
arXiv Detail & Related papers (2021-01-04T18:57:24Z) - A Survey on Visual Transformer [126.56860258176324]
Transformer is a type of deep neural network mainly based on the self-attention mechanism.
In this paper, we review these vision transformer models by categorizing them in different tasks and analyzing their advantages and disadvantages.
arXiv Detail & Related papers (2020-12-23T09:37:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.