49 skills found · Page 1 of 2
RayYoh / OCRM SurveyA Survey of Embodied Learning for Object-Centric Robotic Manipulation
evelinehong / Slot Attention PytorchPytorch Implementation of paper "Object-Centric Learning with Slot Attention"
Wuziyi616 / SlotDiffusionCode release for NeurIPS 2023 paper SlotDiffusion: Object-centric Learning with Diffusion Models
MICV-yonsei / EAGLE[CVPR 2024 Highlight✨] Official Pytorch Code for EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation
amazon-science / Object Centric Learning FrameworkNo description available
taldatech / Lpwm[ICLR 2026 Oral] Latent Particle World Models official repository
gkakogeorgiou / Spot[CVPR 2024 Highlight] :dog: SPOT: Self-Training with Patch-Order Permutation for Object-Centric Learning with Autoregressive Transformers
VersesTech / AxiomImplementation and evaluation of the AXIOM architecture from the preprint "AXIOM: Learning to Play Games in Minutes with Expanding Object Centric Models"
amazon-science / AdaSlotOfficial implementation of the CVPR'24 paper [Adaptive Slot Attention: Object Discovery with Dynamic Slot Number]
YuLiu-LY / BO QSAThis repository is the official implementation of Improving Object-centric Learning With Query Optimization
martius-lab / VideosaurRepository for our paper "Object-Centric Learning for Real-World Videos by Predicting Temporal Feature Similarities"
gorkaydemir / SOLV[NeurIPS 2023] Self-supervised Object-Centric Learning for Videos
DanHrmti / ECRLOfficial PyTorch implementation of "Entity-Centric Reinforcement Learning for Object Manipulation from Pixels", Haramati et al., ICLR 2024
sayantann11 / Clustering Modelsfor MLlustering in Machine Learning Introduction to Clustering It is basically a type of unsupervised learning method . An unsupervised learning method is a method in which we draw references from datasets consisting of input data without labelled responses. Generally, it is used as a process to find meaningful structure, explanatory underlying processes, generative features, and groupings inherent in a set of examples. Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them. For ex– The data points in the graph below clustered together can be classified into one single group. We can distinguish the clusters, and we can identify that there are 3 clusters in the below picture. It is not necessary for clusters to be a spherical. Such as : DBSCAN: Density-based Spatial Clustering of Applications with Noise These data points are clustered by using the basic concept that the data point lies within the given constraint from the cluster centre. Various distance methods and techniques are used for calculation of the outliers. Why Clustering ? Clustering is very much important as it determines the intrinsic grouping among the unlabeled data present. There are no criteria for a good clustering. It depends on the user, what is the criteria they may use which satisfy their need. For instance, we could be interested in finding representatives for homogeneous groups (data reduction), in finding “natural clusters” and describe their unknown properties (“natural” data types), in finding useful and suitable groupings (“useful” data classes) or in finding unusual data objects (outlier detection). This algorithm must make some assumptions which constitute the similarity of points and each assumption make different and equally valid clusters. Clustering Methods : Density-Based Methods : These methods consider the clusters as the dense region having some similarity and different from the lower dense region of the space. These methods have good accuracy and ability to merge two clusters.Example DBSCAN (Density-Based Spatial Clustering of Applications with Noise) , OPTICS (Ordering Points to Identify Clustering Structure) etc. Hierarchical Based Methods : The clusters formed in this method forms a tree-type structure based on the hierarchy. New clusters are formed using the previously formed one. It is divided into two category Agglomerative (bottom up approach) Divisive (top down approach) examples CURE (Clustering Using Representatives), BIRCH (Balanced Iterative Reducing Clustering and using Hierarchies) etc. Partitioning Methods : These methods partition the objects into k clusters and each partition forms one cluster. This method is used to optimize an objective criterion similarity function such as when the distance is a major parameter example K-means, CLARANS (Clustering Large Applications based upon Randomized Search) etc. Grid-based Methods : In this method the data space is formulated into a finite number of cells that form a grid-like structure. All the clustering operation done on these grids are fast and independent of the number of data objects example STING (Statistical Information Grid), wave cluster, CLIQUE (CLustering In Quest) etc. Clustering Algorithms : K-means clustering algorithm – It is the simplest unsupervised learning algorithm that solves clustering problem.K-means algorithm partition n observations into k clusters where each observation belongs to the cluster with the nearest mean serving as a prototype of the cluster . Applications of Clustering in different fields Marketing : It can be used to characterize & discover customer segments for marketing purposes. Biology : It can be used for classification among different species of plants and animals. Libraries : It is used in clustering different books on the basis of topics and information. Insurance : It is used to acknowledge the customers, their policies and identifying the frauds. City Planning: It is used to make groups of houses and to study their values based on their geographical locations and other factors present. Earthquake studies: By learning the earthquake-affected areas we can determine the dangerous zones. References : Wiki Hierarchical clustering Ijarcs matteucc analyticsvidhya knowm
AIS-Bonn / OCVP Object Centric Video PredictionOfficial implementation of: "Object-Centric Video Prediction via Decoupling of Object Dynamics and Interactions" by Villar-Corrales et al. ICIP 2023
gorkaydemir / DINOSAUR[ICLR 2023 - UNOFFICIAL] Bridging the Gap to Real-World Object-Centric Learning
zyp123494 / DynaVolDynaVol: Unsupervised Learning for Dynamic Scenes through Object-Centric Voxelization (ICLR2024) & DynaVol-S: Dynamic Scene Understanding through Object-Centric Voxelization and Neural Rendering (TPAMI2025)
lhj-lhj / MetaSlotMetaSlot: Break Through the Fixed Number of Slots in Object-Centric Learning
angelvillar96 / PlaySlotOfficial implementation of: "PlaySlot: Learning Inverse Latent Dynamics for Controllable Object-Centric Video Prediction and Planning" by Villar-Corrales & Behnke. ICML 2025
priyankamandikal / GraffRepository for Learning Dexterous Grasping with Object-Centric Visual Affordances [ICRA 2021]
