J0nasW/paperswithcode huggingface dataset:
Task: MS-SSIM
Area: computer-vision

A MS-SSIM score helps to analyze how much a De-warping module has been able to de-warp a document image from its initial distorted view.
Task: Face Reconstruction
Area: computer-vision

Face reconstruction is the task of recovering the facial geometry of a face from an image.

<span style="color:grey; opacity: 0.6">( Image credit: Microsoft [Deep3DFaceReconstruction](https://github.com/Microsoft/Deep3DFaceReconstruction) )</span>
Task: Video-Based Person Re-Identification
Area: computer-vision

Video-based person re-identification (reID) aims to retrieve person videos with the same identity as a query person across multiple cameras
Task: Multi-Object Tracking and Segmentation
Area: computer-vision

Multiple object tracking and segmentation requires detecting, tracking, and segmenting objects belonging to a set of given classes. 

(Image and definition credit: [Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation](https://github.com/SysCV/pcan), NeurIPS 2021, Spotlight )
Task: Image Segmentation
Area: computer-vision

**Image Segmentation** is a computer vision task that involves dividing an image into multiple segments or regions, each of which corresponds to a different object or part of an object. The goal of image segmentation is to assign a unique label or category to each pixel in the image, so that pixels with similar attributes are grouped together.
Task: Genre classification
Area: computer-vision

Genre classification is the process of grouping objects together based on defined similarities such as shape, pixel, location, or intensity.
Task: Human Mesh Recovery
Area: computer-vision

Estimate 3D body mesh from images
Task: Stochastic Human Motion Prediction
Area: computer-vision

Stochastic Human Motion Prediction assumes future stochasticity and therefore tackles the task from a generative point of view. Instead of predicting a single future, it predicts N possible futures.
Task: Trajectory Forecasting
Area: computer-vision

Trajectory forecasting is a sequential prediction task, where a forecasting model predicts future trajectories of all moving agents (humans, vehicles, etc.) in a scene, based on their past trajectories and/or the scene context.

(Illustrative figure from [Social NCE: Contrastive Learning of Socially-aware Motion Representations](https://github.com/vita-epfl/social-nce))
Task: Contour Detection
Area: computer-vision

Object **Contour Detection** extracts information about the object shape in images.


<span class="description-source">Source: [Object Contour and Edge Detection with RefineContourNet ](https://arxiv.org/abs/1904.13353)</span>
Task: Action Unit Detection
Area: computer-vision

Action unit detection is the task of detecting action units from a video - for example, types of facial action units (lip tightening, cheek raising) from a video of a face.

<span style="color:grey; opacity: 0.6">( Image credit: [AU R-CNN](https://arxiv.org/pdf/1812.05788v2.pdf) )</span>
Task: OPD: Single-view 3D Openable Part Detection
Area: computer-vision

Detect the openable parts and predict their motion parameters from  single-view image
Task: controllable image captioning
Area: computer-vision

generate image captions conditioned on control signals
Task: Image Similarity Detection
Area: computer-vision

A fundamental computer vision task to determine whether a part of an image has been copied from another image.

Description from: [The 2021 Image Similarity Dataset and Challenge](https://paperswithcode.com/paper/the-2021-image-similarity-dataset-and)

Image credit: [The 2021 Image Similarity Dataset and Challenge](https://paperswithcode.com/paper/the-2021-image-similarity-dataset-and)
Task: Action Segmentation
Area: computer-vision

**Action Segmentation** is a challenging problem in high-level video understanding. In its simplest form, Action Segmentation aims to segment a temporally untrimmed video by time and label each segmented part with one of pre-defined action labels. The results of Action Segmentation can be further used as input to various applications, such as video-to-text and action localization.


<span class="description-source">Source: [TricorNet: A Hybrid Temporal Convolutional and Recurrent Network for Video Action Segmentation ](https://arxiv.org/abs/1705.07818)</span>
Task: Out of Distribution (OOD) Detection
Area: computer-vision

**Out of Distribution (OOD) Detection** is the task of detecting instances that do not belong to the distribution the classifier has been trained on. OOD data is often referred to as "unseen" data, as the model has not encountered it during training. 

OOD detection is typically performed by training a model to distinguish between in-distribution (ID) data, which the model has seen during training, and OOD data, which it has not seen. This can be done using a variety of techniques, such as training a separate OOD detector, or modifying the model's architecture or loss function to make it more sensitive to OOD data.
Task: Stereo Matching
Area: computer-vision

**Stereo Matching** is one of the core technologies in computer vision, which recovers 3D structures of real world from 2D images. It has been widely used in areas such as autonomous driving, augmented reality and robotics navigation. Given a pair of rectified stereo images, the goal of Stereo Matching is to compute the disparity for each pixel in the reference image, where disparity is defined as the horizontal displacement between a pair of corresponding pixels in the left and right images.


<span class="description-source">Source: [Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching ](https://arxiv.org/abs/1909.03751)</span>
Task: Image Animation
Area: computer-vision

Image Animation is a field for image-animation of a source image by a driving video
Task: Point Cloud Classification
Area: computer-vision

Point Cloud Classification is a task involving the classification of unordered 3D point sets (point clouds).
Task: Synthetic-to-Real Translation
Area: computer-vision

Synthetic-to-real translation is the task of domain adaptation from synthetic (or virtual) data to real data.

<span style="color:grey; opacity: 0.6">( Image credit: [CYCADA](https://arxiv.org/pdf/1711.03213v3.pdf) )</span>
Task: Classification Consistency
Area: computer-vision

How often two shifts of the same image are classified the same

<span style="color:grey; opacity: 0.6">( Image credit: [Antialiased CNNs](https://github.com/adobe/antialiased-cnns) )</span>
Task: Scene Understanding
Area: computer-vision

Scene Understanding is something that to understand a scene. For instance, iPhone has function that help eye disabled person to take a photo by discribing what the camera sees. This is an example of Scene Understanding.
Task: Emotion Classification
Area: computer-vision

Emotion classification, or emotion categorization, is the task of recognising emotions to classify them into the corresponding category. Given an input, classify it as 'neutral or no emotion' or as one, or more, of several given emotions that best represent the mental state of the subject's facial expression, words, and so on. Some example benchmarks include ROCStories, Many Faces of Anger (MFA), and GoEmotions. Models can be evaluated using metrics such as the Concordance Correlation Coefficient (CCC) and the Mean Squared Error (MSE).
Task: Real-to-Cartoon translation
Area: computer-vision

Cartoonifying images
Task: Facial Action Unit Detection
Area: computer-vision

Facial action unit detection is the task of detecting action units from a video of a face - for example, lip tightening and cheek raising.

<span style="color:grey; opacity: 0.6">( Image credit: [Self-supervised Representation Learning from Videos for Facial Action Unit Detection](http://openaccess.thecvf.com/content_CVPR_2019/papers/Li_Self-Supervised_Representation_Learning_From_Videos_for_Facial_Action_Unit_Detection_CVPR_2019_paper.pdf) )</span>
Task: NMR J-coupling
Area: graphs

https://github.com/larsbratholm/champs_kaggle
Task: Graph Clustering
Area: graphs

**Graph Clustering** is the process of grouping the nodes of the graph into clusters, taking into account the edge structure of the graph in such a way that there are several edges within each cluster and very few between clusters. Graph Clustering intends to partition the nodes in the graph into disjoint groups.


<span class="description-source">Source: [Clustering for Graph Datasets via Gumbel Softmax ](https://arxiv.org/abs/2005.02372)</span>
Task: Inductive Link Prediction
Area: graphs

In inductive link prediction inference is performed on a new, unseen graph whereas classical transductive link prediction performs both training and inference on the same graph.
Task: Node Classification on Non-Homophilic (Heterophilic) Graphs
Area: graphs

There exists a non-trivial set of graphs where graph-aware models underperform their corresponding graph-agnostic models, e.g. SGC and GCN underperform MLP with 1 layer and 2 layers. Although still controversial, people believe the performance degradation results from heterophily, i.e. there exist much more inter-class edges than inner-class edges. This task aims to evaluate models designed for non-homophilic (heterophilic) datasets.
Task: Approximating Betweenness-Centrality ranking
Area: graphs

Betweenness-centrality is a popular measure in network analysis that aims to describe the importance of nodes in a graph. It accounts for the fraction of shortest paths passing through that node and is a key measure in many applications including community detection and network dismantling.
Task: Steiner Tree Problem
Area: graphs

The **Steiner tree problem** is a computational problem in computer science and graph theory that involves finding the minimum weight subgraph in an undirected graph that connects a given set of terminal vertices. The goal of the Steiner tree problem is to minimize the total weight of the edges in the subgraph, and it is considered NP-hard, meaning that finding the optimal solution is computationally difficult.
Task: Graph Sampling
Area: graphs

Training GNNs or generating graph embeddings requires graph samples.
Task: Heterogeneous Node Classification
Area: graphs

Node classification in heterogeneous graphs, where nodes and/or edges have multiple types.
Task: 3D Hand Pose Estimation
Area: graphs

Image: [Zimmerman et l](https://arxiv.xsrg/pdf/1705.01389v3.pdf)
Task: Tree Decomposition
Area: graphs

**Tree Decomposition** is a technique in graph theory and computer science for representing a graph as a tree, where each node in the tree represents a set of vertices in the original graph. The goal of tree decomposition is to divide the graph into smaller, more manageable pieces, and to use the tree to represent the relationships between these pieces.
Task: Graph structure learning
Area: graphs

Semi-supervised node classification when a graph structure is not available.
Task: hypergraph embedding
Area: graphs

Compute useful representations of hyperedges and vertices
Task: Graph Embedding
Area: graphs

Graph embeddings learn a mapping from a network to a vector space, while preserving relevant network properties.

<span style="color:grey; opacity: 0.6">( Image credit: [GAT](https://github.com/PetarV-/GAT) )</span>
Task: Node Classification
Area: graphs

**Node Classification** is a machine learning task in graph-based data analysis, where the goal is to assign labels to nodes in a graph based on the properties of nodes and the relationships between them.

**Node Classification** models aim to predict non-existing node properties (known as the target property) based on other node properties. Typical models used for node classification consists of a large family of graph neural networks. Model performance can be measured using benchmark datasets like [Cora](/dataset/cora), [Citeseer](/dataset/citeseer), and [Pubmed](/dataset/pubmed), among others, typically using Accuracy and F1.

<span style="color:grey; opacity: 0.6">( Image credit: [Fast Graph Representation Learning With PyTorch Geometric](https://arxiv.org/pdf/1903.02428v3.pdf) )</span>
Task: Knowledge Base Completion
Area: graphs

Knowledge base completion is the task which automatically infers missing facts by reasoning about the information already present in the knowledge base. A knowledge base is a collection of relational
facts, often represented in the form of "subject", "relation", "object"-triples.
Task: Link Prediction
Area: graphs

**Link Prediction** is a task in graph and network analysis where the goal is to predict missing or future connections between nodes in a network. Given a partially observed network, the goal of link prediction is to infer which links are most likely to be added or missing based on the observed connections and the structure of the network.

<span style="color:grey; opacity: 0.6">( Image credit: [Inductive Representation Learning on Large Graphs](https://arxiv.org/pdf/1706.02216v4.pdf) )</span>
Task: Jet Tagging
Area: graphs

Jet tagging is the process of identifying the type of elementary particle that initiates a "jet", i.e., a collimated spray of outgoing particles. It is essentially a classification task that aims to distinguish jets arising from particles of interest, such as the Higgs boson or the top quark, from other less interesting types of jets.
Task: Learning-To-Rank
Area: graphs

Learning to rank is the application of machine learning to build ranking models. Some common use cases for ranking models are information retrieval (e.g., web search) and news feeds application (think Twitter, Facebook, Instagram).
Task: Feedback Vertex Set (FVS)
Area: graphs

The **Feedback Vertex Set (FVS)** problem is a computational problem in computer science and graph theory that involves finding the smallest possible subset of vertices in an undirected graph such that removing those vertices results in a graph that is acyclic, i.e., a forest. The goal of the FVS problem is to minimize the size of the feedback vertex set, and is considered NP-hard, meaning that finding the optimal solution is computationally difficult. For directed graphs, a feedback vertex set is instead a subset of vertices whose removal results in directed acyclic graph (DAG), not necessarily a forest. This task can refer to either looking for a set of provably minimal size (in as little time as possible), or a heuristic algorithm that produces small solutions quickly (but which may have even smaller sets).
Task: Ancestor-descendant prediction
Area: graphs

Given two entities, make a binary prediction if they have ancestor-descendant relationship, based on existing and missing hierarchical edges in the graph.
Task: Physics-informed machine learning
Area: graphs

Machine learning used to represent physics-based and/or engineering models
Task: Triple Classification
Area: graphs

Triple classification aims to judge whether a given triple (h, r, t) is correct or not with respect to the knowledge graph.
Task: Structual Feature Correlation
Area: graphs

Expressive Power of GNN to predict structural feature's correlation mutually.
Task: Twinwidth Contraction Sequence
Area: graphs

**Twinwidth Contraction Sequence** is a concept in graph theory and computer science that refers to a sequence of graph contractions that transform a graph into a smaller, more manageable graph. The goal of the Twinwidth Contraction Sequence is to find a sequence of contractions that results in a graph with a small "twinwidth", which is a measure of how well the graph can be decomposed into small, connected subgraphs.
Task: Root Cause Ranking
Area: graphs

Detection of causal anomalous nodes in graphs
Task: Tree Map Layout
Area: graphs

Hierarchical tree visualisation by assigning sizes and positions of nodes. https://en.wikipedia.org/wiki/Treemapping
Task: Graph Regression
Area: graphs

The regression task is similar to graph classification but using different loss function and performance metric.
Task: Graph Generation
Area: graphs

**Graph Generation** is an important research area with significant applications in drug and material designs.


<span class="description-source">Source: [Graph Deconvolutional Generation ](https://arxiv.org/abs/2002.07087)</span>
Task: Graph Classification
Area: graphs

**Graph Classification** is a task that involves classifying a graph-structured data into different classes or categories. Graphs are a powerful way to represent relationships and interactions between different entities, and graph classification can be applied to a wide range of applications, such as social network analysis, bioinformatics, and recommendation systems. In graph classification, the input is a graph, and the goal is to learn a classifier that can accurately predict the class of the graph.

<span style="color:grey; opacity: 0.6">( Image credit: [Hierarchical Graph Pooling with Structure Learning](https://github.com/cszhangzhen/HGP-SL) )</span>
Task: Inductive Relation Prediction
Area: graphs

Inductive setting of the knowledge graph completion task. This requires a model to perform link prediction on an entirely new test graph with new set of entities.
Task: Graph Matching
Area: graphs

**Graph Matching** is the problem of finding correspondences between two sets of vertices while preserving complex relational information among them. Since the graph structure has a strong capacity to represent objects and robustness to severe deformation and outliers, it is frequently adopted to formulate various correspondence problems in the field of computer vision. Theoretically, the Graph Matching problem can be solved by exhaustively searching the entire solution space. However, this approach is infeasible in practice because the solution space expands exponentially as the size of input data increases. For that reason, previous studies have attempted to solve the problem by using various approximation techniques.


<span class="description-source">Source: [Consistent Multiple Graph Matching with Multi-layer Random Walks Synchronization ](https://arxiv.org/abs/1712.02575)</span>
Task: Rubik's Cube
Area: graphs

Solving the Rubik's Cube is a pathfinding task on a massive implicit graph.
Task: Community Detection
Area: graphs

**Community Detection** is one of the fundamental problems in network analysis, where the goal is to find groups of nodes that are, in some sense, more similar to each other than to the other nodes.


<span class="description-source">Source: [Randomized Spectral Clustering in Large-Scale Stochastic Block Models ](https://arxiv.org/abs/2002.00839)</span>
Task: graph partitioning
Area: graphs

Graph Partitioning is generally the first step of distributed graph computing tasks. The targets are load-balance and minimizing the communication volume.
Task: Entity Alignment
Area: knowledge-base

**Entity Alignment** is the task of finding entities in two knowledge bases that refer to the same real-world object. It plays a vital role in automatically integrating multiple knowledge bases.  
Note: results that have incorporated machine translated entity names (introduced in the RDGCN paper) or pre-alignment name embeddings are considered to have used **extra training labels** (both are marked with "Extra Training Data" in the leaderboard) and are **not adhere to a comparable setting** with others that have followed the original setting of the benchmark.

<span class="description-source">Source: [Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding ](https://arxiv.org/abs/1708.05045)</span>

The task of entity alignment is related to the task of [entity resolution](https://paperswithcode.com/task/entity-resolution) which focuses on matching structured entity descriptions in different contexts.
Task: Linear Mode Connectivity
Area: knowledge-base

**Linear Mode Connectivity** refers to the relationship between input and output variables in a linear regression model. In a linear regression model, input variables are combined with weights to predict output variables. Understanding the linear model connectivity can help interpret model results and identify which input variables are most important for predicting output variables.
Task: Relational Pattern Learning
Area: knowledge-base

Learning and extracting the hidden patterns among the relations in a Knowledge Graph.
Task: Knowledge Graph Completion
Area: knowledge-base

Knowledge graphs $G$ are represented as a collection of triples $\\{(h, r, t)\\}\subseteq E\times R\times E$, where $E$ and $R$ are the entity set and relation set. The task of **Knowledge Graph Completion** is to either predict unseen relations $r$ between two existing entities: $(h, ?, t)$ or predict the tail entity $t$ given the head entity and the query relation: $(h, r, ?)$.


<span class="description-source">Source: [One-Shot Relational Learning for Knowledge Graphs ](https://arxiv.org/abs/1808.09040)</span>
Task: Complex Query Answering
Area: knowledge-base

This task is concerned with answering complex queries over incomplete knowledge graphs. In the most simple case, the task is reduced to link prediction: a 1-hop query for predicting the existence of an edge between a pair of nodes. Complex queries are concerned with other structures between nodes, such as 2-hop and 3-paths, and intersecting paths with intermediate variables.
Task: RDF Dataset Discovery
Area: knowledge-base

Given a URI find the RDF datasets containing this URI.
Task: Table annotation
Area: knowledge-base

**Table annotation** is the task of annotating a table with terms/concepts from knowledge graph or database schema. Table annotation is typically broken down into the following five subtasks: 

1. Cell Entity Annotation ([CEA](https://paperswithcode.com/task/cell-entity-annotation))
2. Column Type Annotation ([CTA](https://paperswithcode.com/task/column-type-annotation))
3. Column Property Annotation ([CPA](https://paperswithcode.com/task/columns-property-annotation))
4. [Table Type Detection](https://paperswithcode.com/task/table-type-detection)
5. [Row Annotation](https://paperswithcode.com/task/row-annotation)

The [SemTab](http://www.cs.ox.ac.uk/isg/challenges/sem-tab/) challenge is closely related to the Table Annotation problem. It is a yearly challenge which focuses on the first three tasks of table annotation and its purpose is to benchmark different table annotation systems.
Task: Manufacturing simulation
Area: knowledge-base

Simulation of manufacturing system for applying AI methods and big data analysis
Task: Models Alignment
Area: knowledge-base

**Models Alignment** is the process of ensuring that multiple models used in a machine learning system are consistent with each other and aligned with the goals of the system. This involves defining clear and consistent objectives for each model, identifying and addressing any inconsistencies or biases in the data used to train each model, testing and validating each model to ensure its accuracy, and ensuring that the predictions and decisions made by each model are consistent and aligned with the overall goals of the system.
Task: Open Knowledge Graph Canonicalization
Area: knowledge-base

Open Information Extraction approaches leads to creation of large Knowledge bases (KB) from the web. The problem with such methods is that their entities and relations are not canonicalized, which leads to storage of redundant and ambiguous facts. For example, an Open KB storing *\<Barack Obama, was born in, Honolulu\>* and *\<Obama, took birth in, Honolulu\>* doesn't know that *Barack Obama* and *Obama* mean the same entity. Similarly, *took birth in* and *was born in* also refer to the same relation. Problem of Open KB canonicalization involves identifying groups of equivalent entities and relations in the KB.

<span style="color:grey; opacity: 0.6">( Image credit: [CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information](https://github.com/malllabiisc/cesi) )</span>