3497hyy/evaluation-tables huggingface dataset:
Task: Self-Supervised Learning
Categories: Computer Vision

**Self-Supervised Learning** is proposed for utilizing unlabeled data with the success of supervised learning. Producing a dataset with good labels is expensive, while unlabeled data is being generated all the time. The motivation of Self-Supervised Learning is to make use of the large amount of unlabeled data. The main idea of Self-Supervised Learning is to generate the labels from unlabeled data, according to the structure or characteristics of the data itself, and then train on this unsupervised data in a supervised manner. Self-Supervised Learning is wildly used in representation learning to make a model learn the latent features of the data. This technique is often employed in computer vision, video processing and robot control.

Source: [Self-supervised Point Set Local Descriptors for Point Cloud Registration ](https://arxiv.org/abs/2003.05199)

Image source: [LeCun](https://www.youtube.com/watch?v=7I0Qt7GALVk)
Task: Sperm Morphology Classification
Categories: Computer Vision

Multi-class classification of sperm head morphology.
Task: GPR
Categories: Computer Vision

Gaussian Process Regression
Task: Meme Captioning
Categories: Natural Language Processing

Automatic generation of natural language descriptions of the content of an input meme.
Task: Meme Classification
Categories: Natural Language Processing
Computer Vision

Meme classification refers to the task of classifying internet memes.
Task: Amodal Layout Estimation
Categories: Computer Vision

Amodal scene layout estimation involves estimating the static and dynamic portion of an urban driving scene in bird's-eye view, given a single image. The concept of "amodal" estimation refers to the fact that we also estimate layout of parts of the scene that are not observable in the image.
Task: Ad-hoc video search
Categories: Computer Vision

The Ad-hoc search task ended a 3 year cycle from 2016-2018 with a goal to model the end user search use-case, who is searching (using textual sentence queries) for segments of video containing persons, objects, activities, locations, etc. and combinations of the former. While the Internet Archive (IACC.3) dataset was adopted between 2016 to 2018, starting in 2019 a new data collection based on Vimeo Creative Commons (V3C) will be adopted to support the task for at least 3 more years.

Given the test collection (V3C1 or IACC.3), master shot boundary reference, and set of Ad-hoc queries (approx. 30 queries) released by NIST, return for each query a list of at most 1000 shot IDs from the test collection ranked according to their likelihood of containing the target query.
Task: Document Classification
Categories: Natural Language Processing

**Document Classification** is a procedure of assigning one or more labels to a document from a predetermined set of labels.

Source: [Long-length Legal Document Classification ](https://arxiv.org/abs/1912.06905)
Task: Contrastive Learning
Categories: Methodology
Computer Vision

**Contrastive Learning** is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart.

It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering.

(Image credit: [Schroff et al. 2015](https://arxiv.org/abs/1503.03832))
Task: Sentence Summarization
Categories: Natural Language Processing

Generating a summary of a given sentence.
Task: Cover song identification
Categories: Music

**Cover Song Identification** is the task of identifying an alternative version of a previous musical piece, even though it may differ substantially in timbre, tempo, structure, and even fundamental aspects relating to the harmony and melody of the song. The term “cover” is so wide that it ranges from acoustic renditions of a previous song, to Jimi Hendrix’ famous (and radical) reinterpretation of Bob Dylan’s “All Along the Watchtower”, to Rage Against the Machine essentially rewriting Bob Dylan’s “Maggie’s Farm”. Beyond its value for computational musicology and for enhancing music recommendation, Cover Song Identification is of interest because of its potential for benchmarking other music similarity and retrieval algorithms. Chord analysis, melody extraction and music similarity are all strongly connected to Cover Song Identification - another field of music analysis where AI has been applied.

Source: [Artificial Musical Intelligence: A Survey ](https://arxiv.org/abs/2006.10553)
Task: Motion Synthesis
Categories: Computer Code
Computer Vision

Creating a video where people in the images move (such as blinking or smiling) requires specialized AI technology like Deepfake or Motion Synthesis, which I cannot do directly here.

However, if you want a video made from the images you provided, I can create an animated slideshow with cute effects and royalty-free background music. Would you be interested in this?
Task: Thai Word Segmentation
Categories: Natural Language Processing

Thai word segmentation
Task: One-shot model fusion

Fusing different neural networks into a single network in one-shot
Task: Object Discovery
Categories: Computer Vision

**Object Discovery** is the task of identifying previously unseen objects.

Source: [Unsupervised Object Discovery and Segmentation of RGBD-images ](https://arxiv.org/abs/1710.06929)
Task: Unsupervised Object Segmentation
Categories: Computer Vision

Image credit: [ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation](https://paperswithcode.com/paper/clevrtex-a-texture-rich-benchmark-for)
Task: Information Plane
Categories: Methodology

To obtain the Information Plane (IP) of deep neural networks, which shows the trajectories of the hidden layers during training in a 2D plane using as coordinate axes the mutual information between the input and the hidden layer, and the mutual information between the output and the hidden layer.
Task: Mutual Information Estimation
Categories: Methodology

To estimate mutual information from samples, specially for high-dimensional variables.
Task: Lip to Speech Synthesis
Categories: Computer Vision

Given a silent video of a speaker, generate the corresponding speech that matches the lip movements.
Task: Physics-informed machine learning
Categories: Graphs
Computer Vision

Machine learning used to represent physics-based and/or engineering models
Task: Multilingual Machine Comprehension in English Hindi
Categories: Natural Language Processing

Multilingual Machine Comprehension (MMC) is a Question-Answering (QA) sub-task that involves quoting the answer for a question from a given snippet, where the question and the snippet can be in different languages. Results on an extended version of the recently released XQuAD dataset, which we propose to use as the evaluation benchmark for future research.
Task: Transparent Object Detection
Categories: Computer Vision

Detecting transparent objects in 2D or 3D
Task: 3D Depth Estimation
Categories: Computer Vision

Image: [monodepth2](https://github.com/nianticlabs/monodepth2)
Task: 3D Shape Reconstruction From A Single 2D Image
Categories: Computer Vision

Image: [Liao et al](https://arxiv.org/pdf/1811.12016v1.pdf)
Task: Aggression Identification
Categories: Natural Language Processing

Develop a classifier that could make a 3-way classification in-between ‘Overtly Aggressive’, ‘Covertly Aggressive’ and ‘Non-aggressive’ text data. For this, TRAC-2 dataset of 5,000 aggression-annotated data from social media each in Bangla (in both Roman and Bangla script), Hindi (in both Roman and Devanagari script) and English for training and validation is to be used.
Task: Misogynistic Aggression Identification
Categories: Natural Language Processing

Develop a binary classifier for classifying the text as ‘gendered’ or ‘non-gendered’. For this, the TRAC-2 dataset of 5,000 annotated data from social media each in Bangla (in both Roman and Bangla script), Hindi (in both Roman and Devanagari script) and English for training and validation is to be used.
Task: 3D Object Detection
Categories: Computer Vision

**3D Object Detection** is a task in computer vision where the goal is to identify and locate objects in a 3D environment based on their shape, location, and orientation. It involves detecting the presence of objects and determining their location in the 3D space in real-time. This task is crucial for applications such as autonomous vehicles, robotics, and augmented reality.

( Image credit: [AVOD](https://github.com/kujason/avod) )
Task: Knowledge Graph Completion
Categories: Knowledge Base
Graphs
Natural Language Processing

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: $(h, r, ?)$.

Source: [One-Shot Relational Learning for Knowledge Graphs ](https://arxiv.org/abs/1808.09040)
Task: Core set discovery
Categories: Methodology

A core set in machine learning is defined as the minimal set of training samples that allows a supervised algorithm to deliver a result as good as the one obtained when the whole set is used.
Task: 3D Face Reconstruction
Categories: Computer Vision

**3D Face Reconstruction** is a computer vision task that involves creating a 3D model of a human face from a 2D image or a set of images. The goal of 3D face reconstruction is to reconstruct a digital 3D representation of a person's face, which can be used for various applications such as animation, virtual reality, and biometric identification.

( Image credit: [3DDFA_V2](https://github.com/cleardusk/3DDFA_V2) )
Task: NetHack
Categories: Playing Games

Mean in-game score over 1000 episodes with random seeds not seen during training. See https://arxiv.org/abs/2006.13760 (Section 2.4 Evaluation Protocol) for details.
Task: Scanpath prediction
Categories: Computer Vision

Learning to Predict Sequences of Human Fixations.
Task: Action Quality Assessment
Categories: Computer Vision

Assessing/analyzing/quantifying how well an action was performed.
Task: Action Anticipation
Categories: Computer Vision

Next action anticipation is defined as observing 1, ... , T frames and predicting the action that happens after a gap of T_a seconds. It is important to note that a new action starts after T_a seconds that is not seen in the observed frames. Here T_a=1 second.
Task: Human action generation
Categories: Computer Vision

Yan et al. (2019) CSGN:

"When the dancer is stepping, jumping and spinning on the
stage, attentions of all audiences are attracted by the streamof the fluent and graceful movements. Building a model that is capable of dancing is as fascinating a task as appreciating the performance itself. In this paper, we aim to generate long-duration human actions represented as skeleton sequences, e.g. those that cover the entirety of a dance, with hundreds of moves and countless possible combinations."

( Image credit: [Convolutional Sequence Generation for Skeleton-Based Action Synthesis](http://www.dahualin.org/publications/dhl19_csgn.pdf) )
Task: Cross-Lingual Transfer
Categories: Natural Language Processing

Cross-lingual transfer refers to transfer learning using data and models available for one language for which ample such resources are available (e.g., English) to solve tasks in another, commonly more low-resource, language.
Task: 3D Canonical Hand Pose Estimation
Categories: Computer Vision

Image: [Lin et al](https://arxiv.org/pdf/2006.01320v1.pdf)
Task: 3D Hand Pose Estimation
Categories: Computer Vision

Image: [Zimmerman et l](https://arxiv.xsrg/pdf/1705.01389v3.pdf)
Task: 3D Object Reconstruction
Categories: Computer Vision

Image: [Choy et al](https://arxiv.org/pdf/1604.00449v1.pdf)
Task: 3D Shape Reconstruction
Categories: Computer Vision

Image credit: [GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision
, ECCV'20](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123600511.pdf)
Task: drone-based object tracking
Categories: Computer Vision

drone-based object tracking
Task: Incremental Learning
Categories: Methodology

Incremental learning aims to develop artificially intelligent systems that can continuously learn to address new tasks from new data while preserving knowledge learned from previously learned tasks.
Task: Point Cloud Classification
Categories: Graphs
Computer Vision

Point Cloud Classification is a task involving the classification of unordered 3D point sets (point clouds).
Task: Camouflaged Object Segmentation
Categories: Computer Vision

Camouflaged object segmentation (COS) or Camouflaged object detection (COD), which was originally promoted by [T.-N. Le et al.](https://www.sciencedirect.com/science/article/abs/pii/S1077314219300608) (2017), aims to identify objects that conceal their texture into the surrounding environment. The high intrinsic similarities between the target object and the background make COS/COD far more challenging than the traditional object segmentation task. Also, refer to the online benchmarks on [CAMO dataset](https://sites.google.com/view/ltnghia/research/camo), [COD dataset](http://dpfan.net/Camouflage/), and [online demo](http://mc.nankai.edu.cn/cod).

( Image source: [Anabranch Network for Camouflaged Object Segmentation](https://www.sciencedirect.com/science/article/abs/pii/S1077314219300608) )
Task: Part-aware Panoptic Segmentation
Categories: Computer Vision

Panoptic segmentation with part-aware predictions.
Task: Pose Retrieval
Categories: Computer Vision

Retrieval of similar human poses from images or videos
Task: Referring Expression Segmentation
Categories: Computer Vision

The task aims at labeling the pixels of an image or video that represent an object instance referred by a linguistic expression. In particular, the referring expression (RE) must allow the identification of an individual object in a discourse or scene (the referent). REs unambiguously identify the target instance.
Task: Small Object Detection
Categories: Computer Vision

**Small Object Detection** is a computer vision task that involves detecting and localizing small objects in images or videos. This task is challenging due to the small size and low resolution of the objects, as well as other factors such as occlusion, background clutter, and variations in lighting conditions.

( Image credit: [Feature-Fused SSD](https://arxiv.org/pdf/1709.05054v3.pdf) )
Task: Animal Pose Estimation
Categories: Computer Vision

Animal pose estimation is the task of identifying the pose of an animal.

( Image credit: [Using DeepLabCut for 3D markerless pose estimation across species and behaviors](http://www.mousemotorlab.org/s/NathMathis2019.pdf) )
Task: Predictive Process Monitoring
Categories: Time Series

A branch of predictive analysis that attempts to predict some future state of a business process.
Task: Manufacturing Quality Control
Categories: Computer Vision

AI for Quality control in manufacturing processes.
Task: Semi-Supervised Video Object Segmentation
Categories: Computer Vision

The semi-supervised scenario assumes the user inputs a full mask of the object(s) of interest in the first frame of a video sequence. Methods have to produce the segmentation mask for that object(s) in the subsequent frames.
Task: 6D Pose Estimation
Categories: Computer Vision

Image: [Zeng et al](https://arxiv.org/pdf/1609.09475v3.pdf)
Task: Video Generation
Categories: Natural Language Processing
Computer Vision

( Various Video Generation Tasks.
Gif credit: [MaGViT](https://paperswithcode.com/paper/magvit-masked-generative-video-transformer) )
Task: Image Manipulation
Categories: Computer Vision

Image Manipulation is the process of altering or transforming an existing image to achieve a desired effect or to modify its content. This can involve various techniques and tools to enhance, modify, or create images based on specific requirements.
Task: Program induction
Categories: Computer Code

Generating program code for domain-specific tasks
Task: Multimodal Deep Learning
Categories: Methodology
Natural Language Processing

**Multimodal deep learning** is a type of deep learning that combines information from multiple modalities, such as text, image, audio, and video, to make more accurate and comprehensive predictions. It involves training deep neural networks on data that includes multiple types of information and using the network to make predictions based on this combined data.

One of the key challenges in multimodal deep learning is how to effectively combine information from multiple modalities. This can be done using a variety of techniques, such as fusing the features extracted from each modality, or using attention mechanisms to weight the contribution of each modality based on its importance for the task at hand.

Multimodal deep learning has many applications, including image captioning, speech recognition, natural language processing, and autonomous vehicles. By combining information from multiple modalities, multimodal deep learning can improve the accuracy and robustness of models, enabling them to perform better in real-world scenarios where multiple types of information are present.
Task: Few-Shot Transfer Learning for Saliency Prediction
Categories: Computer Vision

Saliency prediction aims to predict important locations in a visual scene. It is a per-pixel regression task with predicted values ranging from 0 to 1.

Benefiting from deep learning research and large-scale datasets, saliency prediction has achieved significant success in the past decade. However, it still remains challenging to predict saliency maps on images in new domains that lack sufficient data for data-hungry models.
Task: Graph Learning
Categories: Graphs

Graph learning is a branch of machine learning that focuses on the analysis and interpretation of data represented in graph form. In this context, a graph is a collection of nodes (or vertices) and edges, where nodes represent entities and edges represent the relationships or interactions between these entities. This structure is particularly useful for modeling complex networks found in various domains such as social networks, biological networks, and communication networks.

Graph learning leverages the relationships and structures within the graph to learn and make predictions. It includes techniques like graph neural networks (GNNs), which extend the concept of neural networks to handle graph-structured data. These models are adept at capturing the dependencies and influence of connected nodes, leading to more accurate predictions in scenarios where relationships play a key role.

Key applications of graph learning include recommender systems, drug discovery, social network analysis, and fraud detection. By utilizing the inherent structure of graph data, graph learning algorithms can uncover deep insights and patterns that are not apparent with traditional machine learning approaches.
Task: Talking Face Generation
Categories: Computer Vision

Talking face generation aims to synthesize a sequence of face images that correspond to given speech semantics

( Image credit: [Talking Face Generation by Adversarially Disentangled Audio-Visual Representation](https://github.com/Hangz-nju-cuhk/Talking-Face-Generation-DAVS) )
Task: Industrial Robots
Categories: Robots

An industrial robot is a robot system used for manufacturing. Industrial robots are automated, programmable and capable of movement on three or more axes.
Task: Time Series Regression
Categories: Time Series

Predicting one or more scalars for an entire time series example.
Task: Prediction Intervals
Categories: Miscellaneous

A prediction interval is an estimate of an interval in which a future observation will fall, with a certain probability, given what has already been observed. Prediction intervals are often used in regression analysis.
Task: Attribute Value Extraction
Categories: Natural Language Processing

**Attribute Value Extraction** is the task of extracting values for a given set of attributes of interest from free text input. Attribute value extraction is for example applied in the context of e-commerce where product attribute values are extracted from product offers.

The related task [Attribute Mining](https://paperswithcode.com/task/attribute-mining) assume that the target attribute set is unknown, while attribute value extraction assumes that the attribute set is given.
[Multimodal Attribute Extraction](https://paperswithcode.com/task/multimodal-attribute-value-extraction) aims at extracting attribute values from multi-modal input such as text plus images.
Task: Leadership Inference

Leadership is a process of individuals (leaders) who influence a group to achieve collective goals. In nature, leadership can be viewed as a process of initiation of coordinated activity. In the task of leadership inference, the main goals are to infer leaders, relationships between leaders and followers, as well as the process of leaders use to influence the group to achieve collective decisions. In machine learning, leadership inference can be viewed as a problem of finding a latent multi-agent system that governs behaviors of group of individuals from data.
Task: text-based games
Categories: Playing Games

Text-based games to evaluate the Reinforcement Learning Agents
Task: Commonsense Reasoning for RL
Categories: Natural Language Processing
Reasoning

Commonsense reasoning for Reinforcement Learning agents
Task: Unsupervised Pre-training
Categories: Methodology

Pre-training a neural network using unsupervised (self-supervised) auxiliary tasks on unlabeled data.
Task: Federated Learning
Categories: Methodology
Adversarial

**Federated Learning** is a machine learning approach that allows multiple devices or entities to collaboratively train a shared model without exchanging their data with each other. Instead of sending data to a central server for training, the model is trained locally on each device, and only the model updates are sent to the central server, where they are aggregated to improve the shared model.

This approach allows for privacy-preserving machine learning, as each device keeps its data locally and only shares the information needed to improve the model.
Task: Quantum Circuit Equivalence Checking
Categories: Methodology

Equivalence Checking of Quantum Circuits
Task: Quantum Circuit Mapping
Categories: Methodology

Mapping quantum circuits to quantum devices
Task: JSONiq Query Execution
Categories: Miscellaneous
Computer Code

Execute JSONiq query, typically on semi-structured JSON data
Task: Metaheuristic Optimization
Categories: Methodology

In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem. For some examples, you can visit https://aliasgharheidari.com/Publications.html
Task: Authorship Verification
Categories: Natural Language Processing

Authorship verification (**AV**) is a research subject in the field of digital text forensics that concerns itself with the question, whether two documents have been written by the same person.

Definition taken from the paper **Assessing the Applicability of Authorship Verification Methods**,
available at:
Task: Program Repair
Categories: Computer Code
Reasoning

Task of teaching ML models to modify an existing program to fix a bug in a given code.
Task: Cross-Lingual Document Classification
Categories: Natural Language Processing

Cross-lingual document classification refers to the task of using data and models available for one language for which ample such resources are available (e.g., English) to solve classification tasks in another, commonly low-resource, language.