GAMR: A Guided Attention Model for (visual) Reasoning

• URL: http://arxiv.org/abs/2206.04928v5
• Date: Tue, 21 Mar 2023 15:35:50 GMT
• Title: GAMR: A Guided Attention Model for (visual) Reasoning
• Authors: Mohit Vaishnav, Thomas Serre
• Abstract summary: Humans continue to outperform modern AI systems in their ability to flexibly parse and understand complex visual scenes. We present a novel module for visual reasoning, the Guided Attention Model for (visual) Reasoning (GAMR) GAMR posits that the brain solves complex visual reasoning problems dynamically via sequences of attention shifts to select and route task-relevant visual information into memory.
• Score: 7.919213739992465
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Humans continue to outperform modern AI systems in their ability to flexibly parse and understand complex visual scenes. Here, we present a novel module for visual reasoning, the Guided Attention Model for (visual) Reasoning (GAMR), which instantiates an active vision theory -- positing that the brain solves complex visual reasoning problems dynamically -- via sequences of attention shifts to select and route task-relevant visual information into memory. Experiments on an array of visual reasoning tasks and datasets demonstrate GAMR's ability to learn visual routines in a robust and sample-efficient manner. In addition, GAMR is shown to be capable of zero-shot generalization on completely novel reasoning tasks. Overall, our work provides computational support for cognitive theories that postulate the need for a critical interplay between attention and memory to dynamically maintain and manipulate task-relevant visual information to solve complex visual reasoning tasks.

Related papers

• VipAct: Visual-Perception Enhancement via Specialized VLM Agent Collaboration and Tool-use [74.39058448757645]
  We present VipAct, an agent framework that enhances vision-language models (VLMs) VipAct consists of an orchestrator agent, which manages task requirement analysis, planning, and coordination, along with specialized agents that handle specific tasks. We evaluate VipAct on benchmarks featuring a diverse set of visual perception tasks, with experimental results demonstrating significant performance improvements.
  arXiv  Detail & Related papers  (2024-10-21T18:10:26Z)
• Mind the GAP: Glimpse-based Active Perception improves generalization and sample efficiency of visual reasoning [0.7999703756441756]
  Human capabilities in understanding visual relations are far superior to those of AI systems. We develop a system equipped with a novel Glimpse-based Active Perception (GAP) The results suggest that the GAP is essential for extracting visual relations that go beyond the immediate visual content.
  arXiv  Detail & Related papers  (2024-09-30T11:48:11Z)
• InsightSee: Advancing Multi-agent Vision-Language Models for Enhanced Visual Understanding [12.082379948480257]
  This paper proposes InsightSee, a multi-agent framework to enhance vision-language models' capabilities in handling complex visual understanding scenarios. The framework comprises a description agent, two reasoning agents, and a decision agent, which are integrated to refine the process of visual information interpretation. The proposed framework outperforms state-of-the-art algorithms in 6 out of 9 benchmark tests, with a substantial advancement in multimodal understanding.
  arXiv  Detail & Related papers  (2024-05-31T13:56:55Z)
• PhD Thesis: Exploring the role of (self-)attention in cognitive and computer vision architecture [0.0]
  We analyze Transformer-based self-attention as a model and extend it with memory. We propose GAMR, a cognitive architecture combining attention and memory, inspired by active vision theory.
  arXiv  Detail & Related papers  (2023-06-26T12:40:12Z)
• Top-Down Visual Attention from Analysis by Synthesis [87.47527557366593]
  We consider top-down attention from a classic Analysis-by-Synthesis (AbS) perspective of vision. We propose Analysis-by-Synthesis Vision Transformer (AbSViT), which is a top-down modulated ViT model that variationally approximates AbS, and controllable achieves top-down attention.
  arXiv  Detail & Related papers  (2023-03-23T05:17:05Z)
• BI AVAN: Brain inspired Adversarial Visual Attention Network [67.05560966998559]
  We propose a brain-inspired adversarial visual attention network (BI-AVAN) to characterize human visual attention directly from functional brain activity. Our model imitates the biased competition process between attention-related/neglected objects to identify and locate the visual objects in a movie frame the human brain focuses on in an unsupervised manner.
  arXiv  Detail & Related papers  (2022-10-27T22:20:36Z)
• Attention Mechanisms in Computer Vision: A Survey [75.6074182122423]
  We provide a comprehensive review of various attention mechanisms in computer vision. We categorize them according to approach, such as channel attention, spatial attention, temporal attention and branch attention. We suggest future directions for attention mechanism research.
  arXiv  Detail & Related papers  (2021-11-15T09:18:40Z)
• Understanding the computational demands underlying visual reasoning [10.308647202215708]
  We systematically assess the ability of modern deep convolutional neural networks to learn to solve visual reasoning problems. Our analysis leads to a novel taxonomy of visual reasoning tasks, which can be primarily explained by the type of relations and the number of relations used to compose the underlying rules.
  arXiv  Detail & Related papers  (2021-08-08T10:46:53Z)
• Deep Learning for Embodied Vision Navigation: A Survey [108.13766213265069]
  "Embodied visual navigation" problem requires an agent to navigate in a 3D environment mainly rely on its first-person observation. This paper attempts to establish an outline of the current works in the field of embodied visual navigation by providing a comprehensive literature survey.
  arXiv  Detail & Related papers  (2021-07-07T12:09:04Z)
• Understanding top-down attention using task-oriented ablation design [0.22940141855172028]
  Top-down attention allows neural networks, both artificial and biological, to focus on the information most relevant for a given task. We aim to answer this with a computational experiment based on a general framework called task-oriented ablation design. We compare the performance of two neural networks, one with top-down attention and one without.
  arXiv  Detail & Related papers  (2021-06-08T21:01:47Z)
• Self-supervised Learning from a Multi-view Perspective [121.63655399591681]
  We show that self-supervised representations can extract task-relevant information and discard task-irrelevant information. Our theoretical framework paves the way to a larger space of self-supervised learning objective design.
  arXiv  Detail & Related papers  (2020-06-10T00:21:35Z)

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.