Area of interest adaption using feature importance

• URL: http://arxiv.org/abs/2303.12744v1
• Date: Fri, 3 Mar 2023 07:49:10 GMT
• Title: Area of interest adaption using feature importance
• Authors: Wolfgang Fuhl and Susanne Zabel and Theresa Harbig and Julia Astrid Moldt and Teresa Festl Wiete and Anne Herrmann Werner and Kay Nieselt
• Abstract summary: We present two approaches and algorithms that adapt areas of interest (AOI) or regions of interest (ROI) to the eye tracking data quality and classification task. The first approach uses feature importance in a greedy way and grows or shrinks AOIs in all directions. The second approach is an extension of the first approach, which divides the AOIs into areas and calculates a direction of growth.
• Score: 1.4469849628263638
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present two approaches and algorithms that adapt areas of interest (AOI) or regions of interest (ROI), respectively, to the eye tracking data quality and classification task. The first approach uses feature importance in a greedy way and grows or shrinks AOIs in all directions. The second approach is an extension of the first approach, which divides the AOIs into areas and calculates a direction of growth, i.e. a gradient. Both approaches improve the classification results considerably in the case of generalized AOIs, but can also be used for qualitative analysis. In qualitative analysis, the algorithms presented allow the AOIs to be adapted to the data, which means that errors and inaccuracies in eye tracking data can be better compensated for. A good application example is abstract art, where manual AOIs annotation is hardly possible, and data-driven approaches are mainly used for initial AOIs. Link: https://es-cloud.cs.uni-tuebingen.de/d/8e2ab8c3fdd444e1a135/?p=%2FAOIGradient&mode=list

Related papers

• DRL4AOI: A DRL Framework for Semantic-aware AOI Segmentation in Location-Based Services [30.648182879899434]
  A fundamental task is segmenting Areas of Interest (AOIs) in Location-Based Services (LBS) In this paper, we present a novel DRL-based framework called DRL4AOI. We also present a representative implementation of DRL4AOI for AOI segmentation in the logistics service.
  arXiv  Detail & Related papers  (2024-12-06T21:45:27Z)
• Multimodal Urban Areas of Interest Generation via Remote Sensing Imagery and Geographical Prior [9.85003064364004]
  Urban area-of-interest (AOI) refers to an integrated urban functional zone with defined polygonal boundaries. We propose a comprehensive end-to-end multimodal deep learning framework designed for simultaneously detecting accurate AOI boundaries and validating the reliability of AOI.
  arXiv  Detail & Related papers  (2024-01-12T12:54:30Z)
• Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks [51.8723187709964]
  We study the OOD generalization of neural algorithmic reasoning tasks. The goal is to learn an algorithm from input-output pairs using deep neural networks.
  arXiv  Detail & Related papers  (2022-11-01T18:33:20Z)
• Deep Active Ensemble Sampling For Image Classification [8.31483061185317]
  Active learning frameworks aim to reduce the cost of data annotation by actively requesting the labeling for the most informative data points. Some proposed approaches include uncertainty-based techniques, geometric methods, implicit combination of uncertainty-based and geometric approaches. We present an innovative integration of recent progress in both uncertainty-based and geometric frameworks to enable an efficient exploration/exploitation trade-off in sample selection strategy. Our framework provides two advantages: (1) accurate posterior estimation, and (2) tune-able trade-off between computational overhead and higher accuracy.
  arXiv  Detail & Related papers  (2022-10-11T20:20:20Z)
• Bi-level Alignment for Cross-Domain Crowd Counting [113.78303285148041]
  Current methods rely on external data for training an auxiliary task or apply an expensive coarse-to-fine estimation. We develop a new adversarial learning based method, which is simple and efficient to apply. We evaluate our approach on five real-world crowd counting benchmarks, where we outperform existing approaches by a large margin.
  arXiv  Detail & Related papers  (2022-05-12T02:23:25Z)
• Unbiased Directed Object Attention Graph for Object Navigation [4.037009782513272]
  We propose a directed object attention (DOA) graph to guide the agent in explicitly learning the attention relationships between objects. To distinguish features in different branches, a concise adaptive branch energy distribution (ABED) method is proposed. Compared with the state-of-the-art (SOTA) method, our method reports 7.4%, 8.1% and 17.6% increase in success rate (SR), success weighted by path length (SPL) and success weighted by action efficiency (SAE)
  arXiv  Detail & Related papers  (2022-04-09T08:13:05Z)
• Weakly Supervised Change Detection Using Guided Anisotropic Difusion [97.43170678509478]
  We propose original ideas that help us to leverage such datasets in the context of change detection. First, we propose the guided anisotropic diffusion (GAD) algorithm, which improves semantic segmentation results. We then show its potential in two weakly-supervised learning strategies tailored for change detection.
  arXiv  Detail & Related papers  (2021-12-31T10:03:47Z)
• UMAD: Universal Model Adaptation under Domain and Category Shift [138.12678159620248]
  Universal Model ADaptation (UMAD) framework handles both UDA scenarios without access to source data. We develop an informative consistency score to help distinguish unknown samples from known samples. Experiments on open-set and open-partial-set UDA scenarios demonstrate that UMAD exhibits comparable, if not superior, performance to state-of-the-art data-dependent methods.
  arXiv  Detail & Related papers  (2021-12-16T01:22:59Z)
• Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchmark and Adversarial Graph Learning [85.6386289476598]
  We develop a novel adversarial graph representation adaptation (AGRA) framework for cross-domain holistic-local feature co-adaptation. We conduct extensive and fair evaluations on several popular benchmarks and show that the proposed AGRA framework outperforms previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-08-03T15:00:31Z)
• Supervised Domain Adaptation using Graph Embedding [86.3361797111839]
  Domain adaptation methods assume that distributions between the two domains are shifted and attempt to realign them. We propose a generic framework based on graph embedding. We show that the proposed approach leads to a powerful Domain Adaptation framework.
  arXiv  Detail & Related papers  (2020-03-09T12:25:13Z)

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.