Personal Fixations-Based Object Segmentation with Object Localization and Boundary Preservation

• URL: http://arxiv.org/abs/2101.09014v1
• Date: Fri, 22 Jan 2021 09:20:47 GMT
• Title: Personal Fixations-Based Object Segmentation with Object Localization and Boundary Preservation
• Authors: Gongyang Li and Zhi Liu and Ran Shi and Zheng Hu and Weijie Wei and Yong Wu and Mengke Huang and Haibin Ling
• Abstract summary: We focus on Personal Fixations-based Object (PFOS) to address issues in previous studies. We propose a novel network based on Object Localization and Boundary Preservation (OLBP) to segment the gazed objects. OLBP is organized in the mixed bottom-up and top-down manner with multiple types of deep supervision.
• Score: 60.41628937597989
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As a natural way for human-computer interaction, fixation provides a promising solution for interactive image segmentation. In this paper, we focus on Personal Fixations-based Object Segmentation (PFOS) to address issues in previous studies, such as the lack of appropriate dataset and the ambiguity in fixations-based interaction. In particular, we first construct a new PFOS dataset by carefully collecting pixel-level binary annotation data over an existing fixation prediction dataset, such dataset is expected to greatly facilitate the study along the line. Then, considering characteristics of personal fixations, we propose a novel network based on Object Localization and Boundary Preservation (OLBP) to segment the gazed objects. Specifically, the OLBP network utilizes an Object Localization Module (OLM) to analyze personal fixations and locates the gazed objects based on the interpretation. Then, a Boundary Preservation Module (BPM) is designed to introduce additional boundary information to guard the completeness of the gazed objects. Moreover, OLBP is organized in the mixed bottom-up and top-down manner with multiple types of deep supervision. Extensive experiments on the constructed PFOS dataset show the superiority of the proposed OLBP network over 17 state-of-the-art methods, and demonstrate the effectiveness of the proposed OLM and BPM components. The constructed PFOS dataset and the proposed OLBP network are available at https://github.com/MathLee/OLBPNet4PFOS.

Related papers

• Boosting Gaze Object Prediction via Pixel-level Supervision from Vision Foundation Model [19.800353299691277]
  This paper presents a more challenging gaze object segmentation (GOS) task, which involves inferring the pixel-level mask corresponding to the object captured by human gaze behavior. We propose to automatically obtain head features from scene features to ensure the model's inference efficiency and flexibility in the real world.
  arXiv  Detail & Related papers  (2024-08-02T06:32:45Z)
• Weakly Supervised Open-Vocabulary Object Detection [31.605276665964787]
  We propose a novel weakly supervised open-vocabulary object detection framework, namely WSOVOD, to extend traditional WSOD. To achieve this, we explore three vital strategies, including dataset-level feature adaptation, image-level salient object localization, and region-level vision-language alignment.
  arXiv  Detail & Related papers  (2023-12-19T18:59:53Z)
• Open-Vocabulary Camouflaged Object Segmentation [66.94945066779988]
  We introduce a new task, open-vocabulary camouflaged object segmentation (OVCOS) We construct a large-scale complex scene dataset (textbfOVCamo) containing 11,483 hand-selected images with fine annotations and corresponding object classes. By integrating the guidance of class semantic knowledge and the supplement of visual structure cues from the edge and depth information, the proposed method can efficiently capture camouflaged objects.
  arXiv  Detail & Related papers  (2023-11-19T06:00:39Z)
• Background Activation Suppression for Weakly Supervised Object Localization and Semantic Segmentation [84.62067728093358]
  Weakly supervised object localization and semantic segmentation aim to localize objects using only image-level labels. New paradigm has emerged by generating a foreground prediction map to achieve pixel-level localization. This paper presents two astonishing experimental observations on the object localization learning process.
  arXiv  Detail & Related papers  (2023-09-22T15:44:10Z)
• Weakly-supervised Contrastive Learning for Unsupervised Object Discovery [52.696041556640516]
  Unsupervised object discovery is promising due to its ability to discover objects in a generic manner. We design a semantic-guided self-supervised learning model to extract high-level semantic features from images. We introduce Principal Component Analysis (PCA) to localize object regions.
  arXiv  Detail & Related papers  (2023-07-07T04:03:48Z)
• Sharp Eyes: A Salient Object Detector Working The Same Way as Human Visual Characteristics [3.222802562733787]
  We propose a sharp eyes network (SENet) that first seperates the object from scene, and then finely segments it. The proposed method aims to utilize the expanded objects to guide the network obtain complete prediction.
  arXiv  Detail & Related papers  (2023-01-18T11:00:45Z)
• Self-Supervised Video Object Segmentation via Cutout Prediction and Tagging [117.73967303377381]
  We propose a novel self-supervised Video Object (VOS) approach that strives to achieve better object-background discriminability. Our approach is based on a discriminative learning loss formulation that takes into account both object and background information. Our proposed approach, CT-VOS, achieves state-of-the-art results on two challenging benchmarks: DAVIS-2017 and Youtube-VOS.
  arXiv  Detail & Related papers  (2022-04-22T17:53:27Z)
• Unveiling the Potential of Structure-Preserving for Weakly Supervised Object Localization [71.79436685992128]
  We propose a two-stage approach, termed structure-preserving activation (SPA), towards fully leveraging the structure information incorporated in convolutional features for WSOL. In the first stage, a restricted activation module (RAM) is designed to alleviate the structure-missing issue caused by the classification network. In the second stage, we propose a post-process approach, termed self-correlation map generating (SCG) module to obtain structure-preserving localization maps.
  arXiv  Detail & Related papers  (2021-03-08T03:04:14Z)

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.