Semantic-Based Active Perception for Humanoid Visual Tasks with Foveal Sensors

• URL: http://arxiv.org/abs/2404.10836v1
• Date: Tue, 16 Apr 2024 18:15:57 GMT
• Title: Semantic-Based Active Perception for Humanoid Visual Tasks with Foveal Sensors
• Authors: João Luzio, Alexandre Bernardino, Plinio Moreno,
• Abstract summary: The aim of this work is to establish how accurately a recent semantic-based active perception model is able to complete visual tasks that are regularly performed by humans. This model exploits the ability of current object detectors to localize and classify a large number of object classes and to update a semantic description of a scene across multiple fixations. In the task of scene exploration, the semantic-based method demonstrates superior performance compared to the traditional saliency-based model.
• Score: 49.99728312519117
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The aim of this work is to establish how accurately a recent semantic-based foveal active perception model is able to complete visual tasks that are regularly performed by humans, namely, scene exploration and visual search. This model exploits the ability of current object detectors to localize and classify a large number of object classes and to update a semantic description of a scene across multiple fixations. It has been used previously in scene exploration tasks. In this paper, we revisit the model and extend its application to visual search tasks. To illustrate the benefits of using semantic information in scene exploration and visual search tasks, we compare its performance against traditional saliency-based models. In the task of scene exploration, the semantic-based method demonstrates superior performance compared to the traditional saliency-based model in accurately representing the semantic information present in the visual scene. In visual search experiments, searching for instances of a target class in a visual field containing multiple distractors shows superior performance compared to the saliency-driven model and a random gaze selection algorithm. Our results demonstrate that semantic information, from the top-down, influences visual exploration and search tasks significantly, suggesting a potential area of research for integrating it with traditional bottom-up cues.

Related papers

• Teaching VLMs to Localize Specific Objects from In-context Examples [56.797110842152]
  Vision-Language Models (VLMs) have shown remarkable capabilities across diverse visual tasks. Current VLMs lack a fundamental cognitive ability: learning to localize objects in a scene by taking into account the context. This work is the first to explore and benchmark personalized few-shot localization for VLMs.
  arXiv  Detail & Related papers  (2024-11-20T13:34:22Z)
• Zero-Shot Object-Centric Representation Learning [72.43369950684057]
  We study current object-centric methods through the lens of zero-shot generalization. We introduce a benchmark comprising eight different synthetic and real-world datasets. We find that training on diverse real-world images improves transferability to unseen scenarios.
  arXiv  Detail & Related papers  (2024-08-17T10:37:07Z)
• Labeling Indoor Scenes with Fusion of Out-of-the-Box Perception Models [4.157013247909771]
  We propose to leverage the recent advancements in state-of-the-art models for bottom-up segmentation (SAM), object detection (Detic), and semantic segmentation (MaskFormer) We aim to develop a cost-effective labeling approach to obtain pseudo-labels for semantic segmentation and object instance detection in indoor environments. We demonstrate the effectiveness of the proposed approach on the Active Vision dataset and the ADE20K dataset.
  arXiv  Detail & Related papers  (2023-11-17T21:58:26Z)
• Selective Visual Representations Improve Convergence and Generalization for Embodied AI [44.33711781750707]
  Embodied AI models often employ off the shelf vision backbones like CLIP to encode their visual observations. This introduces noise within the learning process and distracts the agent's focus from task-relevant visual cues. Inspired by selective attention in humans-the process through which people filter their perception based on their experiences, knowledge, and the task at hand-we introduce a parameter-efficient approach to filter visual stimuli for embodied AI.
  arXiv  Detail & Related papers  (2023-11-07T18:34:02Z)
• Embodied Learning for Lifelong Visual Perception [33.02424587900808]
  We study lifelong visual perception in an embodied setup, where we develop new models and compare various agents that navigate in buildings. The purpose of the agents is to recognize objects and other semantic classes in the whole building at the end of a process that combines exploration and active visual learning.
  arXiv  Detail & Related papers  (2021-12-28T10:47:13Z)
• Glimpse-Attend-and-Explore: Self-Attention for Active Visual Exploration [47.01485765231528]
  Active visual exploration aims to assist an agent with a limited field of view to understand its environment based on partial observations. We propose the Glimpse-Attend-and-Explore model which employs self-attention to guide the visual exploration instead of task-specific uncertainty maps. Our model provides encouraging results while being less dependent on dataset bias in driving the exploration.
  arXiv  Detail & Related papers  (2021-08-26T11:41:03Z)
• Embodied Visual Active Learning for Semantic Segmentation [33.02424587900808]
  We study the task of embodied visual active learning, where an agent is set to explore a 3d environment with the goal to acquire visual scene understanding. We develop a battery of agents - both learnt and pre-specified - and with different levels of knowledge of the environment. We extensively evaluate the proposed models using the Matterport3D simulator and show that a fully learnt method outperforms comparable pre-specified counterparts.
  arXiv  Detail & Related papers  (2020-12-17T11:02:34Z)
• Synthesizing the Unseen for Zero-shot Object Detection [72.38031440014463]
  We propose to synthesize visual features for unseen classes, so that the model learns both seen and unseen objects in the visual domain. We use a novel generative model that uses class-semantics to not only generate the features but also to discriminatively separate them.
  arXiv  Detail & Related papers  (2020-10-19T12:36:11Z)
• What Can You Learn from Your Muscles? Learning Visual Representation from Human Interactions [50.435861435121915]
  We use human interaction and attention cues to investigate whether we can learn better representations compared to visual-only representations. Our experiments show that our "muscly-supervised" representation outperforms a visual-only state-of-the-art method MoCo.
  arXiv  Detail & Related papers  (2020-10-16T17:46:53Z)
• Spatio-Temporal Graph for Video Captioning with Knowledge Distillation [50.034189314258356]
  We propose a graph model for video captioning that exploits object interactions in space and time. Our model builds interpretable links and is able to provide explicit visual grounding. To avoid correlations caused by the variable number of objects, we propose an object-aware knowledge distillation mechanism.
  arXiv  Detail & Related papers  (2020-03-31T03:58:11Z)

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.