Enhancing Saliency Prediction in Monitoring Tasks: The Role of Visual Highlights

• URL: http://arxiv.org/abs/2405.09695v1
• Date: Wed, 15 May 2024 20:43:57 GMT
• Title: Enhancing Saliency Prediction in Monitoring Tasks: The Role of Visual Highlights
• Authors: Zekun Wu, Anna Maria Feit,
• Abstract summary: We develop a new saliency model to infer the visual attention change in the highlight condition. Our findings show the effectiveness of visual highlights in enhancing user attention and demonstrate the potential of incorporating these cues into saliency prediction models.
• Score: 4.0361765428523135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study examines the role of visual highlights in guiding user attention in drone monitoring tasks, employing a simulated interface for observation. The experiment results show that such highlights can significantly expedite the visual attention on the corresponding area. Based on this observation, we leverage both the temporal and spatial information in the highlight to develop a new saliency model: the highlight-informed saliency model (HISM), to infer the visual attention change in the highlight condition. Our findings show the effectiveness of visual highlights in enhancing user attention and demonstrate the potential of incorporating these cues into saliency prediction models.

Related papers

• Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performance [61.06245197347139]
  We propose a novel approach to explain the behavior of a black-box model under feature shifts. We refer to our method that combines concepts from Optimal Transport and Shapley Values as Explanatory Performance Estimation.
  arXiv  Detail & Related papers  (2024-08-24T18:28:19Z)
• Shifting Focus with HCEye: Exploring the Dynamics of Visual Highlighting and Cognitive Load on User Attention and Saliency Prediction [3.2873782624127834]
  This paper examines the joint impact of visual highlighting (permanent and dynamic) and dual-task-induced cognitive load on gaze behaviour. We show that state-of-the-art saliency models increase their performance when accounting for different cognitive loads.
  arXiv  Detail & Related papers  (2024-04-22T14:45:30Z)
• Semantic-Based Active Perception for Humanoid Visual Tasks with Foveal Sensors [49.99728312519117]
  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.
  arXiv  Detail & Related papers  (2024-04-16T18:15:57Z)
• Emotic Masked Autoencoder with Attention Fusion for Facial Expression Recognition [1.4374467687356276]
  This paper presents an innovative approach integrating the MAE-Face self-supervised learning (SSL) method and multi-view Fusion Attention mechanism for expression classification. We suggest easy-to-implement and no-training frameworks aimed at highlighting key facial features to determine if such features can serve as guides for the model. The efficacy of this method is validated by improvements in model performance on the Aff-wild2 dataset.
  arXiv  Detail & Related papers  (2024-03-19T16:21:47Z)
• GazeFusion: Saliency-guided Image Generation [50.37783903347613]
  Diffusion models offer unprecedented image generation capabilities given just a text prompt. We present a saliency-guided framework to incorporate the data priors of human visual attention into the generation process.
  arXiv  Detail & Related papers  (2024-03-16T21:01:35Z)
• 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)
• GAMR: A Guided Attention Model for (visual) Reasoning [7.919213739992465]
  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.
  arXiv  Detail & Related papers  (2022-06-10T07:52:06Z)
• 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)
• Heterogeneous Contrastive Learning: Encoding Spatial Information for Compact Visual Representations [183.03278932562438]
  This paper presents an effective approach that adds spatial information to the encoding stage to alleviate the learning inconsistency between the contrastive objective and strong data augmentation operations. We show that our approach achieves higher efficiency in visual representations and thus delivers a key message to inspire the future research of self-supervised visual representation learning.
  arXiv  Detail & Related papers  (2020-11-19T16:26:25Z)
• Visual Interest Prediction with Attentive Multi-Task Transfer Learning [6.177155931162925]
  We propose a transfer learning and attention mechanism based neural network model to predict visual interest & affective dimensions in digital photos. Evaluation of our model on the benchmark dataset shows large improvement over current state-of-the-art systems.
  arXiv  Detail & Related papers  (2020-05-26T14:49:34Z)

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.