Towards Instance Segmentation with Object Priority: Prominent Object Detection and Recognition
This manuscript introduces the problem of prominent object detection and recognition inspired by the fact that human seems to priorities perception of scene elements. The problem deals with finding the most important region of interest, segmenting the relevant item/object in that area, and assigning it an object class label. In other words, we are solving the three problems of saliency modeling, saliency detection, and object recognition under one umbrella. The motivation behind such a problem formulation is (1) the benefits to the knowledge representation-based vision pipelines, and (2) the potential improvements in emulating bio-inspired vision systems by solving these three problems together. We are foreseeing extending this problem formulation to fully semantically segmented scenes with instance object priority for high-level inferences in various applications including assistive vision. Along with a new problem definition, we also propose a method to achieve such a task. The proposed model predicts the most important area in the image, segments the associated objects, and labels them. The proposed problem and method are evaluated against human fixations, annotated segmentation masks, and object class categories. We define a chance level for each of the evaluation criterion to compare the proposed algorithm with. Despite the good performance of the proposed baseline, the overall evaluations indicate that the problem of prominent object detection and recognition is a challenging task that is still worth investigating further.
Code (0)
등록된 구현이 없습니다.
Tasks
Instance SegmentationObjectobject-detectionObject DetectionObject RecognitionSaliency DetectionSemantic SegmentationSimilar Papers 제목 키워드 기반
Open3DIS: Open-Vocabulary 3D Instance Segmentation with 2D Mask Guidance
We introduce Open3DIS, a novel solution designed to tackle the problem of Open-Vocabulary Instance Segmentation within 3D scenes. Objects within 3D environments exhibit diverse shapes, scales, and colors, making precise …
3D Instance Segmentation3D Open-Vocabulary Instance SegmentationInstance SegmentationObject Localization+3Contextual Guided Segmentation Framework for Semi-supervised Video Instance Segmentation
In this paper, we propose Contextual Guided Segmentation (CGS) framework for video instance segmentation in three passes. In the first pass, i.e., preview segmentation, we propose Instance Re-Identification Flow to estim…
Human-Object Interaction DetectionInstance SegmentationSegmentationSemantic Segmentation+1A Spacecraft Dataset for Detection, Segmentation and Parts Recognition
Virtually all aspects of modern life depend on space technology. Thanks to the great advancement of computer vision in general and deep learning-based techniques in particular, over the decades, the world witnessed the g…
Deep LearningInstance SegmentationObjectobject-detection+3FoodSAM: Any Food Segmentation
In this paper, we explore the zero-shot capability of the Segment Anything Model (SAM) for food image segmentation. To address the lack of class-specific information in SAM-generated masks, we propose a novel framework, …
Image SegmentationInstance SegmentationPanoptic SegmentationSegmentation+1SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmark
We propose SiM3D, the first benchmark considering the integration of multiview and multimodal information for comprehensive 3D anomaly detection and segmentation (ADS), where the task is to produce a voxel-based Anomaly …
3D Anomaly Detection3D Anomaly Detection and SegmentationAnomaly Detection