Papers Object Localization
“Object Localization” 태그가 달린 논문 726편 · 필터 해제
Improving Weakly-supervised Object Localization via Causal Intervention
The recent emerged weakly supervised object localization (WSOL) methods can learn to localize an object in the image only using image-level labels. Previous works endeavor to perceive the interval objects from the small …
ObjectObject LocalizationWeakly-Supervised Object LocalizationWeakly Supervised Object Localization and Detection: A Survey
As an emerging and challenging problem in the computer vision community, weakly supervised object localization and detection plays an important role for developing new generation computer vision systems and has received …
ObjectObject LocalizationSurveyWeakly-Supervised Object LocalizationFine-Grained Attention for Weakly Supervised Object Localization
Although recent advances in deep learning accelerated an improvement in a weakly supervised object localization (WSOL) task, there are still challenges to identify the entire body of an object, rather than only discrimin…
ObjectObject LocalizationWeakly-Supervised Object LocalizationWeakly-supervised Instance Segmentation via Class-agnostic Learning with Salient Images
Humans have a strong class-agnostic object segmentation ability and can outline boundaries of unknown objects precisely, which motivates us to propose a box-supervised class-agnostic object segmentation (BoxCaseg) based …
Box-supervised Instance SegmentationInstance SegmentationMulti-Task LearningObject+4DAP: Detection-Aware Pre-training with Weak Supervision
This paper presents a detection-aware pre-training (DAP) approach, which leverages only weakly-labeled classification-style datasets (e.g., ImageNet) for pre-training, but is specifically tailored to benefit object detec…
ClassificationGeneral Classificationimage-classificationImage Classification+4TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) is a challenging problem when given image category labels but requires to learn object localization models. Optimizing a convolutional neural network (CNN) for classification …
ObjectObject LocalizationWeakly-Supervised Object LocalizationFew-shot Weakly-Supervised Object Detection via Directional Statistics
Detecting novel objects from few examples has become an emerging topic in computer vision recently. However, these methods need fully annotated training images to learn new object categories which limits their applicabil…
Multiple Instance LearningObjectobject-detectionObject Detection+2Object Localization Through a Single Multiple-Model Convolutional Neural Network with a Specific Training Approach
Object localization has a vital role in any object detector, and therefore, has been the focus of attention by many researchers. In this article, a special training approach is proposed for a light convolutional neural n…
ObjectObject LocalizationMonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation
Object localization in 3D space is a challenging aspect in monocular 3D object detection. Recent advances in 6DoF pose estimation have shown that predicting dense 2D-3D correspondence maps between image and object 3D mod…
3D Object DetectionMonocular 3D Object DetectionObjectobject-detection+3Meta-DETR: Image-Level Few-Shot Object Detection with Inter-Class Correlation Exploitation
Few-shot object detection has been extensively investigated by incorporating meta-learning into region-based detection frameworks. Despite its success, the said paradigm is constrained by several factors, such as (i) low…
Few-Shot Object DetectionMeta-Learningobject-detectionObject Detection+1Unveiling the Potential of Structure Preserving for Weakly Supervised Object Localization
Weakly supervised object localization(WSOL) remains an open problem given the deficiency of finding object extent information using a classification network. Although prior works struggled to localize objects through var…
ClassificationGeneral ClassificationObjectObject Localization+2Learning from Counting: Leveraging Temporal Classification for Weakly Supervised Object Localization and Detection
This paper reports a new solution of leveraging temporal classification to support weakly supervised object detection (WSOD). Specifically, we introduce raster scan-order techniques to serialize 2D images into 1D sequenc…
General ClassificationObjectobject-detectionObject Detection+3InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring
Compared with the visual grounding on 2D images, the natural-language-guided 3D object localization on point clouds is more challenging. In this paper, we propose a new model, named InstanceRefer, to achieve a superior 3…
3D visual groundingAttributeObject LocalizationPanoptic Segmentation+1Weakly-supervised multi-class object localization using only object counts as labels
We demonstrate the use of an extensive deep neural network to localize instances of objects in images. The EDNN is naturally able to accurately perform multi-class counting using only ground truth count values as labels.…
ObjectObject LocalizationImproved Semantic Segmentation of Tuberculosis-consistent findings in Chest X-rays Using Augmented Training of Modality-specific U-Net Models with Weak Localizations
Deep learning (DL) has drawn tremendous attention in object localization and recognition for both natural and medical images. U-Net segmentation models have demonstrated superior performance compared to conventional hand…
Decision MakingObject LocalizationSegmentationSemantic Segmentation+1Integrated Grad-CAM: Sensitivity-Aware Visual Explanation of Deep Convolutional Networks via Integrated Gradient-Based Scoring
Visualizing the features captured by Convolutional Neural Networks (CNNs) is one of the conventional approaches to interpret the predictions made by these models in numerous image recognition applications. Grad-CAM is a …
Object LocalizationSensitivityLearning task-agnostic representation via toddler-inspired learning
One of the inherent limitations of current AI systems, stemming from the passive learning mechanisms (e.g., supervised learning), is that they perform well on labeled datasets but cannot deduce knowledge on their own. To…
image-classificationImage ClassificationObject LocalizationPersonal Fixations-Based Object Segmentation with Object Localization and Boundary Preservation
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…
Image SegmentationObjectObject LocalizationSemantic SegmentationFocal and Efficient IOU Loss for Accurate Bounding Box Regression
In object detection, bounding box regression (BBR) is a crucial step that determines the object localization performance. However, we find that most previous loss functions for BBR have two main drawbacks: (i) Both $\ell…
object-detectionObject DetectionObject LocalizationregressionKimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs
Humans are able to form a complex mental model of the environment they move in. This mental model captures geometric and semantic aspects of the scene, describes the environment at multiple levels of abstractions (e.g., …
3D ReconstructionObject LocalizationScene ParsingVisual Localization