paper-with-me

Papers

From Cognition to Computation: A Comparative Review of Human Attention and Transformer Architectures

2024-04-25 · Minglu Zhao, Dehong Xu, Tao Gao

Attention is a cornerstone of human cognition that facilitates the efficient extraction of information in everyday life. Recent developments in artificial intelligence like the Transformer architecture also incorporate the idea of attention in model designs. However, despite the shared fundamental principle of selectively attending to information, human attention and the Transformer model display notable differences, particularly in their capacity constraints, attention pathways, and intentional mechanisms. Our review aims to provide a comparative analysis of these mechanisms from a cognitive-functional perspective, thereby shedding light on several open research questions. The exploration encourages interdisciplinary efforts to derive insights from human attention mechanisms in the pursuit of developing more generalized artificial intelligence.

📄 PDF Abstract BibTeX arXiv:2407.01548

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

Attention 설명 없음
Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.
Multi-Head Attention 설명 없음
Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…
BPE Byte Pair Encoding, or BPE, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word…
Layer Normalization Unlike batch normalization, Layer Normalization directly estimates the normalization statistics from the summed inputs…
Label Smoothing Label Smoothing is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the…
Absolute Position Encodings Absolute Position Encodings are a type of position embeddings for [Transformer-based models] where positional encodings are…

Similar Papers 제목 키워드 기반

An Analysis of Deep Neural Networks with Attention for Action Recognition from a Neurophysiological Perspective

2019-07-02 · Swathikiran Sudhakaran, Oswald Lanz

We review three recent deep learning based methods for action recognition and present a brief comparative analysis of the methods from a neurophyisiological point of view. We posit that there are some analogy between the…

Action RecognitionDeep Learning

Human Action Recognition from Various Data Modalities: A Review

2020-12-22 · Zehua Sun, Qiuhong Ke, Hossein Rahmani, Mohammed Bennamoun 외

Human Action Recognition (HAR) aims to understand human behavior and assign a label to each action. It has a wide range of applications, and therefore has been attracting increasing attention in the field of computer vis…

Action RecognitionTemporal Action Localization

Comparing Facial Expression Recognition in Humans and Machines: Using CAM, GradCAM, and Extremal Perturbation

2021-10-09 · Serin Park, Christian Wallraven

Facial expression recognition (FER) is a topic attracting significant research in both psychology and machine learning with a wide range of applications. Despite a wealth of research on human FER and considerable progres…

Facial Expression RecognitionFacial Expression Recognition (FER)

Comparative Opinion Mining: A Review

2017-12-24 · Kasturi Dewi Varathan, Anastasia Giachanou, Fabio Crestani

Opinion mining refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in textual material. Opinion mining, also known as sentiment ana…

Opinion MiningSentiment Analysis

Facial gesture interfaces for expression and communication

2020-10-04 · Michael J. Lyons

Considerable effort has been devoted to the automatic extraction of information about action of the face from image sequences. Within the context of human-computer interaction (HCI) we may distinguish systems that allow …