Papers Object Localization
“Object Localization” 태그가 달린 논문 726편 · 필터 해제
On Label Granularity and Object Localization
Weakly supervised object localization (WSOL) aims to learn representations that encode object location using only image-level category labels. However, many objects can be labeled at different levels of granularity. Is i…
ObjectObject LocalizationWeakly-Supervised Object LocalizationBagging Regional Classification Activation Maps for Weakly Supervised Object Localization
Classification activation map (CAM), utilizing the classification structure to generate pixel-wise localization maps, is a crucial mechanism for weakly supervised object localization (WSOL). However, CAM directly uses th…
ObjectObject LocalizationWeakly-Supervised Object LocalizationFinding Fallen Objects Via Asynchronous Audio-Visual Integration
The way an object looks and sounds provide complementary reflections of its physical properties. In many settings cues from vision and audition arrive asynchronously but must be integrated, as when we hear an object drop…
Imitation LearningObjectObject LocalizationReal-time Full-stack Traffic Scene Perception for Autonomous Driving with Roadside Cameras
We propose a novel and pragmatic framework for traffic scene perception with roadside cameras. The proposed framework covers a full-stack of roadside perception pipeline for infrastructure-assisted autonomous driving, in…
Autonomous DrivingEdge-computingObjectobject-detection+3Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks
We propose Unified-IO, a model that performs a large variety of AI tasks spanning classical computer vision tasks, including pose estimation, object detection, depth estimation and image generation, vision-and-language t…
Depth EstimationImage GenerationKeypoint EstimationObject Categorization+10Spiking Neural Networks for Frame-based and Event-based Single Object Localization
Spiking neural networks have shown much promise as an energy-efficient alternative to artificial neural networks. However, understanding the impacts of sensor noises and input encodings on the network activity and perfor…
Object LocalizationCREAM: Weakly Supervised Object Localization via Class RE-Activation Mapping
Weakly Supervised Object Localization (WSOL) aims to localize objects with image-level supervision. Existing works mainly rely on Class Activation Mapping (CAM) derived from a classification model. However, CAM-based met…
ClusteringObjectObject Localizationparameter estimation+1Location-free Human Pose Estimation
Human pose estimation (HPE) usually requires large-scale training data to reach high performance. However, it is rather time-consuming to collect high-quality and fine-grained annotations for human body. To alleviate thi…
Object LocalizationPose EstimationWeakly-Supervised Object LocalizationBeyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam Search
To track the target in a video, current visual trackers usually adopt greedy search for target object localization in each frame, that is, the candidate region with the maximum response score will be selected as the trac…
Decision MakingImage CaptioningMulti-agent Reinforcement LearningObject Localization+3Visuomotor Control in Multi-Object Scenes Using Object-Aware Representations
Perceptual understanding of the scene and the relationship between its different components is important for successful completion of robotic tasks. Representation learning has been shown to be a powerful technique for t…
ObjectObject LocalizationRepresentation LearningSelf-Supervised LearningLearning 6-DoF Object Poses to Grasp Category-level Objects by Language Instructions
This paper studies the task of any objects grasping from the known categories by free-form language instructions. This task demands the technique in computer vision, natural language processing, and robotics. We bring th…
ObjectObject LocalizationRobotic GraspingGRIT: General Robust Image Task Benchmark
Computer vision models excel at making predictions when the test distribution closely resembles the training distribution. Such models have yet to match the ability of biological vision to learn from multiple sources and…
Instance SegmentationKeypoint DetectionObject CategorizationObject Localization+6Diverse Instance Discovery: Vision-Transformer for Instance-Aware Multi-Label Image Recognition
Previous works on multi-label image recognition (MLIR) usually use CNNs as a starting point for research. In this paper, we take pure Vision Transformer (ViT) as the research base and make full use of the advantages of T…
Multi-Label Image RecognitionObject LocalizationWeakly-Supervised Object LocalizationViTOL: Vision Transformer for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) aims at predicting object locations in an image using only image-level category labels. Common challenges that image classification models encounter when localizing objects ar…
image-classificationImage ClassificationObjectObject Localization+1Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization
Obtaining accurate pixel-level localization from class labels is a crucial process in weakly supervised semantic segmentation and object localization. Attribution maps from a trained classifier are widely used to provide…
Adversarial AttackObjectObject LocalizationSemantic Segmentation+4Total Variation Optimization Layers for Computer Vision
Optimization within a layer of a deep-net has emerged as a new direction for deep-net layer design. However, there are two main challenges when applying these layers to computer vision tasks: (a) which optimization probl…
DenoisingEdge DetectionGPUimage-classification+5Bridging the Gap between Classification and Localization for Weakly Supervised Object Localization
Weakly supervised object localization aims to find a target object region in a given image with only weak supervision, such as image-level labels. Most existing methods use a class activation map (CAM) to generate a loca…
ObjectObject LocalizationWeakly-Supervised Object LocalizationHINT: Hierarchical Neuron Concept Explainer
To interpret deep networks, one main approach is to associate neurons with human-understandable concepts. However, existing methods often ignore the inherent relationships of different concepts (e.g., dog and cat both be…
Object LocalizationWeakly-Supervised Object LocalizationContrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation
While class activation map (CAM) generated by image classification network has been widely used for weakly supervised object localization (WSOL) and semantic segmentation (WSSS), such classifiers usually focus on discrim…
Contrastive Learningimage-classificationImage ClassificationObject+3CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation
For robots to be generally useful, they must be able to find arbitrary objects described by people (i.e., be language-driven) even without expensive navigation training on in-domain data (i.e., perform zero-shot inferenc…
image-classificationImage ClassificationObject LocalizationPhilosophy