Papers Object Localization
“Object Localization” 태그가 달린 논문 726편 · 필터 해제
Dual Attention U-Net with Feature Infusion: Pushing the Boundaries of Multiclass Defect Segmentation
The proposed architecture, Dual Attentive U-Net with Feature Infusion (DAU-FI Net), addresses challenges in semantic segmentation, particularly on multiclass imbalanced datasets with limited samples. DAU-FI Net integrate…
Edge DetectionFeature EngineeringObject LocalizationSegmentation+1Object-Aware Domain Generalization for Object Detection
Single-domain generalization (S-DG) aims to generalize a model to unseen environments with a single-source domain. However, most S-DG approaches have been conducted in the field of classification. When these approaches a…
Autonomous DrivingContrastive LearningData AugmentationDomain Generalization+5Weakly Supervised Open-Vocabulary Object Detection
Despite weakly supervised object detection (WSOD) being a promising step toward evading strong instance-level annotations, its capability is confined to closed-set categories within a single training dataset. In this pap…
AttributeNovel ConceptsObjectobject-detection+6Open3DIS: 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+3Exploring Foveation and Saccade for Improved Weakly-Supervised Localization
Deep neural networks have become the de facto choice as feature extraction engines, ubiquitously used for computer vision tasks. The current approach is to process every input with uniform resolution in a one-shot manner…
Active Object LocalizationFoveationObject LocalizationWeakly-Supervised Object LocalizationMultiscale Vision Transformer With Deep Clustering-Guided Refinement for Weakly Supervised Object Localization
This work addresses the task of weakly-supervised object localization. The goal is to learn object localization using only image-level class labels, which are much easier to obtain compared to bounding box annotations. T…
ClusteringDeep ClusteringObjectObject Localization+1Mono3DVG: 3D Visual Grounding in Monocular Images
We introduce a novel task of 3D visual grounding in monocular RGB images using language descriptions with both appearance and geometry information. Specifically, we build a large-scale dataset, Mono3DRefer, which contain…
3D Object Detection3D visual groundingDecoderMono3DVG+4Boosting Segment Anything Model Towards Open-Vocabulary Learning
The recent Segment Anything Model (SAM) has emerged as a new paradigmatic vision foundation model, showcasing potent zero-shot generalization and flexible prompting. Despite SAM finding applications and adaptations in va…
modelObjectObject LocalizationRegion Proposal+1ZeroReg: Zero-Shot Point Cloud Registration with Foundation Models
State-of-the-art 3D point cloud registration methods rely on labeled 3D datasets for training, which limits their practical applications in real-world scenarios and often hinders generalization to unseen scenes. Leveragi…
DecoderGraph MatchingObjectObject Localization+1BEVNeXt: Reviving Dense BEV Frameworks for 3D Object Detection
Recently, the rise of query-based Transformer decoders is reshaping camera-based 3D object detection. These query-based decoders are surpassing the traditional dense BEV (Bird's Eye View)-based methods. However, we argue…
3D Object DetectionDecoderDepth EstimationObject+3SANeRF-HQ: Segment Anything for NeRF in High Quality
Recently, the Segment Anything Model (SAM) has showcased remarkable capabilities of zero-shot segmentation, while NeRF (Neural Radiance Fields) has gained popularity as a method for various 3D problems beyond novel view …
NeRFNovel View SynthesisObjectObject Localization+3Grounding Everything: Emerging Localization Properties in Vision-Language Transformers
Vision-language foundation models have shown remarkable performance in various zero-shot settings such as image retrieval, classification, or captioning. But so far, those models seem to fall behind when it comes to zero…
Image RetrievalObject LocalizationSemantic SegmentationZero Shot SegmentationUnion-over-Intersections: Object Detection beyond Winner-Takes-All
This paper revisits the problem of predicting box locations in object detection architectures. Typically, each box proposal or box query aims to directly maximize the intersection-over-union score with the ground truth, …
AllInstance SegmentationObjectobject-detection+4Language Embedded 3D Gaussians for Open-Vocabulary Scene Understanding
Open-vocabulary querying in 3D space is challenging but essential for scene understanding tasks such as object localization and segmentation. Language-embedded scene representations have made progress by incorporating la…
GPUInductive BiasNovel View SynthesisObject Localization+2Seeing Beyond Cancer: Multi-Institutional Validation of Object Localization and 3D Semantic Segmentation using Deep Learning for Breast MRI
The clinical management of breast cancer depends on an accurate understanding of the tumor and its anatomical context to adjacent tissues and landmark structures. This context may be provided by semantic segmentation met…
3D Semantic Segmentationobject-detectionObject DetectionObject Localization+3Cooperative Multi-Monostatic Sensing for Object Localization in 6G Networks
Enabling passive sensing of the environment using cellular base stations (BSs) will be one of the disruptive features of the sixth-generation (6G) networks. However, accurate localization and positioning of objects are c…
Object LocalizationPoint, Segment and Count: A Generalized Framework for Object Counting
Class-agnostic object counting aims to count all objects in an image with respect to example boxes or class names, \emph{a.k.a} few-shot and zero-shot counting. In this paper, we propose a generalized framework for both …
Knowledge DistillationObjectObject CountingObject Localization+1DSD-DA: Distillation-based Source Debiasing for Domain Adaptive Object Detection
Though feature-alignment based Domain Adaptive Object Detection (DAOD) methods have achieved remarkable progress, they ignore the source bias issue, i.e., the detector tends to acquire more source-specific knowledge, imp…
Classificationobject-detectionObject DetectionObject LocalizationTowards Learning Monocular 3D Object Localization From 2D Labels using the Physical Laws of Motion
We present a novel method for precise 3D object localization in single images from a single calibrated camera using only 2D labels. No expensive 3D labels are needed. Thus, instead of using 3D labels, our model is traine…
Monocular 3D Object LocalizationObjectObject LocalizationObject Pose Estimation Annotation Pipeline for Multi-view Monocular Camera Systems in Industrial Settings
Object localization, and more specifically object pose estimation, in large industrial spaces such as warehouses and production facilities, is essential for material flow operations. Traditional approaches rely on artifi…
ObjectObject LocalizationPose Estimation