Papers Unsupervised Object Detection
“Unsupervised Object Detection” 태그가 달린 논문 19편 · 필터 해제
Scene-Centric Unsupervised Panoptic Segmentation
Unsupervised panoptic segmentation aims to partition an image into semantically meaningful regions and distinct object instances without training on manually annotated data. In contrast to prior work on unsupervised pano…
Instance SegmentationPanoptic SegmentationPseudo LabelScene Understanding+5Learning to Detect Objects from Multi-Agent LiDAR Scans without Manual Labels
Unsupervised 3D object detection serves as an important solution for offline 3D object annotation. However, due to the data sparsity and limited views, the clustering-based label fitting in unsupervised object detection …
3D Object DetectionObjectobject-detectionObject Detection+1Leveraging Color Channel Independence for Improved Unsupervised Object Detection
Object-centric architectures can learn to extract distinct object representations from visual scenes, enabling downstream applications on the object level. Similarly to autoencoder-based image models, object-centric appr…
DisentanglementObjectobject-detectionObject Detection+2Vision-Language Guidance for LiDAR-based Unsupervised 3D Object Detection
Accurate 3D object detection in LiDAR point clouds is crucial for autonomous driving systems. To achieve state-of-the-art performance, the supervised training of detectors requires large amounts of human-annotated data, …
3D Object DetectionAutonomous DrivingObjectobject-detection+2Approaching Outside: Scaling Unsupervised 3D Object Detection from 2D Scene
The unsupervised 3D object detection is to accurately detect objects in unstructured environments with no explicit supervisory signals. This task, given sparse LiDAR point clouds, often results in compromised performance…
3D Object Detectionobject-detectionObject DetectionPseudo Label+1Unsupervised Object Detection with Theoretical Guarantees
Unsupervised object detection using deep neural networks is typically a difficult problem with few to no guarantees about the learned representation. In this work we present the first unsupervised object detection method…
DecoderObjectobject-detectionObject Detection+1UNION: Unsupervised 3D Object Detection using Object Appearance-based Pseudo-Classes
Unsupervised 3D object detection methods have emerged to leverage vast amounts of data without requiring manual labels for training. Recent approaches rely on dynamic objects for learning to detect mobile objects but pen…
3D Object DetectionObject DetectionObject DiscoveryUnsupervised Object DetectionMOD-UV: Learning Mobile Object Detectors from Unlabeled Videos
Embodied agents must detect and localize objects of interest, e.g. traffic participants for self-driving cars. Supervision in the form of bounding boxes for this task is extremely expensive. As such, prior work has looke…
Motion SegmentationObjectobject-detectionObject Detection+5Unsupervised learning based object detection using Contrastive Learning
Training image-based object detectors presents formidable challenges, as it entails not only the complexities of object detection but also the added intricacies of precisely localizing objects within potentially diverse …
Contrastive LearningObjectobject-detectionObject Detection+2Towards Unsupervised Object Detection From LiDAR Point Clouds
In this paper, we study the problem of unsupervised object detection from 3D point clouds in self-driving scenes. We present a simple yet effective method that exploits (i) point clustering in near-range areas where the …
Objectobject-detectionObject DetectionObject Discovery+1Towards Self-Adaptive Machine Learning-Enabled Systems Through QoS-Aware Model Switching
Machine Learning (ML), particularly deep learning, has seen vast advancements, leading to the rise of Machine Learning-Enabled Systems (MLS). However, numerous software engineering challenges persist in propelling these …
object-detectionObject DetectionSelf Adaptive SystemUnsupervised Object DetectionCut and Learn for Unsupervised Object Detection and Instance Segmentation
We propose Cut-and-LEaRn (CutLER), a simple approach for training unsupervised object detection and segmentation models. We leverage the property of self-supervised models to 'discover' objects without supervision and am…
Instance Segmentationobject-detectionObject DetectionSemantic Segmentation+4FreeSOLO: Learning to Segment Objects without Annotations
Instance segmentation is a fundamental vision task that aims to recognize and segment each object in an image. However, it requires costly annotations such as bounding boxes and segmentation masks for learning. In this w…
Instance Segmentationobject-detectionObject DetectionSegmentation+2ProposalCLIP: Unsupervised Open-Category Object Proposal Generation via Exploiting CLIP Cues
Object proposal generation is an important and fundamental task in computer vision. In this paper, we propose ProposalCLIP, a method towards unsupervised open-category object proposal generation. Unlike previous works wh…
Objectobject-detectionObject DetectionObject Proposal Generation+1Class-aware Sounding Objects Localization via Audiovisual Correspondence
Audiovisual scenes are pervasive in our daily life. It is commonplace for humans to discriminatively localize different sounding objects but quite challenging for machines to achieve class-aware sounding objects localiza…
Objectobject-detectionObject DetectionObject Localization+1GMAIR: Unsupervised Object Detection Based on Spatial Attention and Gaussian Mixture
Recent studies on unsupervised object detection based on spatial attention have achieved promising results. Models, such as AIR and SPAIR, output "what" and "where" latent variables that represent the attributes and loca…
ClusteringObjectobject-detectionObject Detection+2Unsupervised Object Detection with LiDAR Clues
Despite the importance of unsupervised object detection, to the best of our knowledge, there is no previous work addressing this problem. One main issue, widely known to the community, is that object boundaries derived o…
Objectobject-detectionObject DetectionUnsupervised Object DetectionA Metamodel and Framework for AGI
Can artificial intelligence systems exhibit superhuman performance, but in critical ways, lack the intelligence of even a single-celled organism? The answer is clearly 'yes' for narrow AI systems. Animals, plants, and ev…
Federated Learningobject-detectionObject DetectionUnsupervised Object DetectionAn Explicit Local and Global Representation Disentanglement Framework with Applications in Deep Clustering and Unsupervised Object Detection
Visual data can be understood at different levels of granularity, where global features correspond to semantic-level information and local features correspond to texture patterns. In this work, we propose a framework, ca…
ClusteringDeep ClusteringDisentanglementobject-detection+4