Papers Object Localization
“Object Localization” 태그가 달린 논문 726편 · 필터 해제
Learning Open-World Object Proposals without Learning to Classify
Object proposals have become an integral preprocessing steps of many vision pipelines including object detection, weakly supervised detection, object discovery, tracking, etc. Compared to the learning-free methods, learn…
Objectobject-detectionObject DetectionObject Discovery+3Progressive Coordinate Transforms for Monocular 3D Object Detection
Recognizing and localizing objects in the 3D space is a crucial ability for an AI agent to perceive its surrounding environment. While significant progress has been achieved with expensive LiDAR point clouds, it poses a …
3D Object DetectionAI AgentMonocular 3D Object DetectionObject+3Boosting Weakly Supervised Object Detection via Learning Bounding Box Adjusters
Weakly-supervised object detection (WSOD) has emerged as an inspiring recent topic to avoid expensive instance-level object annotations. However, the bounding boxes of most existing WSOD methods are mainly determined by …
Objectobject-detectionObject DetectionObject Localization+2Weakly Supervised Foreground Learning for Weakly Supervised Localization and Detection
Modern deep learning models require large amounts of accurately annotated data, which is often difficult to satisfy. Hence, weakly supervised tasks, including weakly supervised object localization~(WSOL) and detection~(W…
Object LocalizationWeakly-Supervised Object LocalizationShallow Feature Matters for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) aims to localize objects by only utilizing image-level labels. Class activation maps (CAMs) are the commonly used features to achieve WSOL. However, previous CAM-based methods…
ObjectObject LocalizationPseudo LabelWeakly-Supervised Object LocalizationNormalization Matters in Weakly Supervised Object Localization
Weakly-supervised object localization (WSOL) enables finding an object using a dataset without any localization information. By simply training a classification model using only image-level annotations, the feature map o…
ObjectObject LocalizationWeakly-Supervised Object LocalizationTowards Accurate Localization by Instance Search
Visual object localization is the key step in a series of object detection tasks. In the literature, high localization accuracy is achieved with the mainstream strongly supervised frameworks. However, such methods requir…
Few-Shot Object DetectionInstance SearchObjectobject-detection+2Evaluation of Audio-Visual Alignments in Visually Grounded Speech Models
Systems that can find correspondences between multiple modalities, such as between speech and images, have great potential to solve different recognition and data analysis tasks in an unsupervised manner. This work studi…
Cross-Modal RetrievalObject LocalizationRetrievalSemantic RetrievalExploring Depth Contribution for Camouflaged Object Detection
Camouflaged object detection (COD) aims to segment camouflaged objects hiding in the environment, which is challenging due to the similar appearance of camouflaged objects and their surroundings. Research in biology sugg…
Depth EstimationGenerative Adversarial NetworkMonocular Depth EstimationObject+4LayerCAM: Exploring Hierarchical Class Activation Maps for Localization
The class activation maps are generated from the final convolutional layer of CNN. They can highlight discriminative object regions for the class of interest. These discovered object regions have been widely used for wea…
ObjectObject LocalizationSemantic SegmentationWeakly-Supervised Object LocalizationStrengthen Learning Tolerance for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) aims at learning to localize objects of interest by only using the image-level labels as the supervision. While numerous efforts have been made in this field, recent appro…
ObjectObject LocalizationWeakly-Supervised Object LocalizationKeep CALM and Improve Visual Feature Attribution
The class activation mapping, or CAM, has been the cornerstone of feature attribution methods for multiple vision tasks. Its simplicity and effectiveness have led to wide applications in the explanation of visual predict…
Object LocalizationWeakly-Supervised Object LocalizationTeam RUC_AIM3 Technical Report at ActivityNet 2021: Entities Object Localization
Entities Object Localization (EOL) aims to evaluate how grounded or faithful a description is, which consists of caption generation and object grounding. Previous works tackle this problem by jointly training the two mod…
Caption GenerationObjectObject LocalizationDETReg: Unsupervised Pretraining with Region Priors for Object Detection
Recent self-supervised pretraining methods for object detection largely focus on pretraining the backbone of the object detector, neglecting key parts of detection architecture. Instead, we introduce DETReg, a new self-s…
Few-Shot LearningFew-Shot Object DetectionObjectobject-detection+5Dual Normalization Multitasking for Audio-Visual Sounding Object Localization
Although several research works have been reported on audio-visual sound source localization in unconstrained videos, no datasets and metrics have been proposed in the literature to quantitatively evaluate its performanc…
ObjectObject LocalizationSound Source LocalizationDeep Learning for Weakly-Supervised Object Detection and Object Localization: A Survey
Weakly-Supervised Object Detection (WSOD) and Localization (WSOL), i.e., detecting multiple and single instances with bounding boxes in an image using image-level labels, are long-standing and challenging tasks in the CV…
Objectobject-detectionObject DetectionObject Localization+1Improving Few-shot Learning with Weakly-supervised Object Localization
Few-shot learning often involves metric learning-based classifiers, which predict the image label by comparing the distance between the extracted feature vector and class representations. However, applying global pooling…
Few-Shot LearningMetric LearningObject LocalizationWeakly-Supervised Object LocalizationDeep Spiking Convolutional Neural Network for Single Object Localization Based On Deep Continuous Local Learning
With the advent of neuromorphic hardware, spiking neural networks can be a good energy-efficient alternative to artificial neural networks. However, the use of spiking neural networks to perform computer vision tasks rem…
object-detectionObject DetectionObject LocalizationMinMaxCAM: Improving object coverage for CAM-basedWeakly Supervised Object Localization
One of the most common problems of weakly supervised object localization is that of inaccurate object coverage. In the context of state-of-the-art methods based on Class Activation Mapping, this is caused either by local…
ObjectObject LocalizationWeakly-Supervised Object LocalizationHeterogeneous Grid Convolution for Adaptive, Efficient, and Controllable Computation
This paper proposes a novel heterogeneous grid convolution that builds a graph-based image representation by exploiting heterogeneity in the image content, enabling adaptive, efficient, and controllable computations in a…
Clusteringobject-detectionObject DetectionObject Localization+3