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Object Discovery

Object Discovery is the task of identifying previously unseen objects.

Source: Unsupervised Object Discovery and Segmentation of RGBD-images

Papers

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Showing 101–150 of 210 papers

TitleStatusHype
Out of Sight, Still in Mind: Reasoning and Planning about Unobserved Objects with Video Tracking Enabled Memory Models—0
MoDA: Leveraging Motion Priors from Videos for Advancing Unsupervised Domain Adaptation in Semantic SegmentationCode0
Box-based Refinement for Weakly Supervised and Unsupervised Localization TasksCode0
Enhancing Interpretable Object Abstraction via Clustering-based Slot Initialization—0
LOCATE: Self-supervised Object Discovery via Flow-guided Graph-cut and Bootstrapped Self-trainingCode0
Semantics Meets Temporal Correspondence: Self-supervised Object-centric Learning in VideosCode0
Multi-Label Self-Supervised Learning with Scene Images—0
Grounded Object Centric Learning—0
Multi-Object Discovery by Low-Dimensional Object Motion—0
FODVid: Flow-guided Object Discovery in Videos—0
Weakly-supervised Contrastive Learning for Unsupervised Object DiscoveryCode0
Coarse Is Better? A New Pipeline Towards Self-Supervised Learning with Uncurated Images—0
Cycle Consistency Driven Object Discovery—0
Contrastive Training of Complex-Valued Autoencoders for Object DiscoveryCode0
SlotDiffusion: Object-Centric Generative Modeling with Diffusion Models—0
AutoRecon: Automated 3D Object Discovery and Reconstruction—0
MOST: Multiple Object localization with Self-supervised Transformers for object discovery—0
Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots—0
DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discovery—0
Adversarial Normalization: I Can Visualize Everything (ICE)Code0
Image Segmentation-based Unsupervised Multiple Objects Discovery—0
Improving Unsupervised Video Object Segmentation with Motion-Appearance Synergy—0
Boosting Object Representation Learning via Motion and Object ContinuityCode0
Foreground Guidance and Multi-Layer Feature Fusion for Unsupervised Object Discovery with TransformersCode0
Object-Category Aware Reinforcement Learning—0
Differentiable Mathematical Programming for Object-Centric Representation Learning—0
K-means for unsupervised instance segmentation using a self-supervised transformer—0
Motion-inductive Self-supervised Object Discovery in Videos—0
Sample, Crop, Track: Self-Supervised Mobile 3D Object Detection for Urban Driving LiDAR—0
TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut—0
Objects Can Move: 3D Change Detection by Geometric Transformation ConstistencyCode0
Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions—0
SCIM: Simultaneous Clustering, Inference, and Mapping for Open-World Semantic Scene UnderstandingCode0
TUSK: Task-Agnostic Unsupervised Keypoints—0
Object discovery and representation networksCode0
Towards Self-Supervised Learning of Global and Object-Centric RepresentationsCode0
Unsupervised Image Decomposition with Phase-Correlation NetworksCode0
SCOD: Active Object Detection for Embodied Agents using Sensory Commutativity of Action Sequences—0
Exploring Depth Contribution for Camouflaged Object Detection—0
PatchNet: Unsupervised Object Discovery based on Patch Embedding—0
Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation—0
Track, Check, Repeat: An EM Approach to Unsupervised Tracking—0
Knowledge-Guided Object Discovery with Acquired Deep ImpressionsCode0
Self-Supervision by Prediction for Object Discovery in Videos—0
Inducing Meaningful Units from Character Sequences with Dynamic Capacity Slot Attention—0
GO-Finder: A Registration-Free Wearable System for Assisting Users in Finding Lost Objects via Hand-Held Object Discovery—0
Unsupervised Discovery of 3D Physical Objects—0
Language-Mediated, Object-Centric Representation Learning—0
Iterative Knowledge Exchange Between Deep Learning and Space-Time Spectral Clustering for Unsupervised Segmentation in Videos—0
Chair Segments: A Compact Benchmark for the Study of Object SegmentationCode0
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