3497hyy/evaluation-tables huggingface dataset:
Task: Sarcasm Detection
Categories: Natural Language Processing

The goal of **Sarcasm Detection** is to determine whether a sentence is sarcastic or non-sarcastic. Sarcasm is a type of phenomenon with specific perlocutionary effects on the hearer, such as to break their pattern of expectation. Consequently, correct understanding of sarcasm often requires a deep understanding of multiple sources of information, including the utterance, the conversational context, and, frequently some real world facts.

Source: [Attentional Multi-Reading Sarcasm Detection ](https://arxiv.org/abs/1809.03051)
Task: Hate Speech Detection
Categories: Natural Language Processing

Hate speech detection is the task of detecting if communication such as text, audio, and so on contains hatred and or encourages violence towards a person or a group of people. This is usually based on prejudice against 'protected characteristics' such as their ethnicity, gender, sexual orientation, religion, age et al. Some example benchmarks are ETHOS and HateXplain. Models can be evaluated with metrics like the F-score or F-measure.
Task: Scene Text Detection
Categories: Computer Vision

**Scene Text Detection** is a computer vision task that involves automatically identifying and localizing text within natural images or videos. The goal of scene text detection is to develop algorithms that can robustly detect and and label text with bounding boxes in uncontrolled and complex environments, such as street signs, billboards, or license plates.

Source: [ContourNet: Taking a Further Step toward Accurate Arbitrary-shaped Scene Text Detection ](https://arxiv.org/abs/2004.04940)
Task: Sound Event Detection
Categories: Audio

**Sound Event Detection** (SED) is the task of recognizing the sound events and their respective temporal start and end time in a recording. Sound events in real life do not always occur in isolation, but tend to considerably overlap with each other. Recognizing such overlapping sound events is referred as polyphonic SED.

Source: [A report on sound event detection with different binaural features ](https://arxiv.org/abs/1710.02997)
Task: Continuous Control
Categories: Robots
Playing Games
Computer Vision

Continuous control in the context of playing games, especially within artificial intelligence (AI) and machine learning (ML), refers to the ability to make a series of smooth, ongoing adjustments or actions to control a game or a simulation. This is in contrast to discrete control, where the actions are limited to a set of specific, distinct choices. Continuous control is crucial in environments where precision, timing, and the magnitude of actions matter, such as driving a car in a racing game, controlling a character in a simulation, or managing the flight of an aircraft in a flight simulator.
Task: Point Cloud Registration
Categories: Computer Vision

**Point Cloud Registration** is a fundamental problem in 3D computer vision and photogrammetry. Given several sets of points in different coordinate systems, the aim of registration is to find the transformation that best aligns all of them into a common coordinate system. Point Cloud Registration plays a significant role in many vision applications such as 3D model reconstruction, cultural heritage management, landslide monitoring and solar energy analysis.

Source: [Iterative Global Similarity Points : A robust coarse-to-fine integration solution for pairwise 3D point cloud registration ](https://arxiv.org/abs/1808.03899)
Task: Image Registration
Categories: Computer Vision

Image registration is the process of transforming different sets of data into one coordinate system. Data may be multiple photographs, data from different sensors, times, depths, or viewpoints. It is used in computer vision, medical imaging, and compiling and analyzing images and data from satellites. Registration is necessary in order to be able to compare or integrate the data obtained from these different measurements.

Source: [Image registration | Wikipedia](https://en.wikipedia.org/wiki/Image_registration)

( Image credit: [Kornia](https://github.com/kornia/kornia) )
Task: Audio Tagging
Categories: Audio

Audio tagging is a task to predict the tags of audio clips. Audio tagging tasks include music tagging, acoustic scene classification, audio event classification, etc.
Task: Audio-Visual Speech Recognition
Categories: Speech

Audio-visual speech recognition is the task of transcribing a paired audio and visual stream into text.
Task: Image Captioning
Categories: Natural Language Processing
Computer Vision

**Image Captioning** is the task of describing the content of an image in words. This task lies at the intersection of computer vision and natural language processing. Most image captioning systems use an encoder-decoder framework, where an input image is encoded into an intermediate representation of the information in the image, and then decoded into a descriptive text sequence. The most popular benchmarks are nocaps and COCO, and models are typically evaluated according to a BLEU or CIDER metric.

( Image credit: [Reflective Decoding Network for Image Captioning, ICCV'19](https://openaccess.thecvf.com/content_ICCV_2019/papers/Ke_Reflective_Decoding_Network_for_Image_Captioning_ICCV_2019_paper.pdf))
Task: Video Captioning
Categories: Computer Vision

**Video Captioning** is a task of automatic captioning a video by understanding the action and event in the video which can help in the retrieval of the video efficiently through text.

Source: [NITS-VC System for VATEX Video Captioning Challenge 2020 ](https://arxiv.org/abs/2006.04058)
Task: Robot Navigation
Categories: Robots
Computer Vision

The fundamental objective of mobile **Robot Navigation** is to arrive at a goal position without collision. The mobile robot is supposed to be aware of obstacles and move freely in different working scenarios.

Source: [Learning to Navigate from Simulation via Spatial and Semantic Information Synthesis with Noise Model Embedding ](https://arxiv.org/abs/1910.05758)
Task: Visual Navigation
Categories: Robots
Computer Vision

**Visual Navigation** is the problem of navigating an agent, e.g. a mobile robot, in an environment using camera input only. The agent is given a target image (an image it will see from the target position), and its goal is to move from its current position to the target by applying a sequence of actions, based on the camera observations only.

Source: [Vision-based Navigation Using Deep Reinforcement Learning ](https://arxiv.org/abs/1908.03627)
Task: Visual Tracking
Categories: Computer Vision

**Visual Tracking** is an essential and actively researched problem in the field of computer vision with various real-world applications such as robotic services, smart surveillance systems, autonomous driving, and human-computer interaction. It refers to the automatic estimation of the trajectory of an arbitrary target object, usually specified by a bounding box in the first frame, as it moves around in subsequent video frames.

Source: [Learning Reinforced Attentional Representation for End-to-End Visual Tracking ](https://arxiv.org/abs/1908.10009)
Task: Object Tracking
Categories: Computer Vision

**Object tracking** is the task of taking an initial set of object detections, creating a unique ID for each of the initial detections, and then tracking each of the objects as they move around frames in a video, maintaining the ID assignment. State-of-the-art methods involve fusing data from RGB and event-based cameras to produce more reliable object tracking. CNN-based models using only RGB images as input are also effective. The most popular benchmark is OTB. There are several evaluation metrics specific to object tracking, including HOTA, MOTA, IDF1, and Track-mAP.

( Image credit: [Towards-Realtime-MOT
](https://github.com/Zhongdao/Towards-Realtime-MOT) )
Task: Image Retrieval
Categories: Computer Vision

**Image Retrieval** is a fundamental and long-standing computer vision task that involves finding images similar to a given query from a large database. It is often considered a form of fine-grained, instance-level classification. The task is integral to image recognition alongside [classification](/task/image-classification) and [cross-modal retrieval](/task/cross-modal-retrieva). By leveraging visual similarity and other criteria, image retrieval enables users to efficiently discover relevant images, making it a crucial tool in applications such as search and recommendation.

[Extending CLIP for Category-to-image Retrieval in E-commerce](https://arxiv.org/abs/2112.11294)

( Image credit: [DELF](https://github.com/tensorflow/models/tree/master/research/delf) )
Task: Ad-Hoc Information Retrieval
Categories: Natural Language Processing

Ad-hoc information retrieval refers to the task of returning information resources related to a user query formulated in natural language.
Task: News Annotation
Categories: Natural Language Processing

Assigning the appropriate labels to a news text based on a set of pre-defined labels.
Task: Web Page Tagging
Categories: Natural Language Processing

Assigning appropriate tags to a web page.
Task: Text Simplification
Categories: Natural Language Processing

**Text Simplification** is the task of reducing the complexity of the vocabulary and sentence structure of text while retaining its original meaning, with the goal of improving readability and understanding. Simplification has a variety of important societal applications, for example increasing accessibility for those with cognitive disabilities such as aphasia, dyslexia, and autism, or for non-native speakers and children with reading difficulties.

Source: [Multilingual Unsupervised Sentence Simplification](https://arxiv.org/abs/2005.00352)
Task: Text Clustering
Categories: Natural Language Processing

Grouping a set of texts in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). (Source: Adapted from Wikipedia)
Task: Morphological Analysis
Categories: Natural Language Processing

**Morphological Analysis** is a central task in language processing that can take a word as input and detect the various morphological entities in the word and provide a morphological representation of it.

Source: [Towards Finite-State Morphology of Kurdish ](https://arxiv.org/abs/2005.10652)
Task: Lexical Analysis
Categories: Natural Language Processing

Lexical analysis is the process of converting a sequence of characters into a sequence of tokens (strings with an assigned and thus identified meaning). (Source: Adapted from Wikipedia)
Task: Subjectivity Analysis
Categories: Natural Language Processing

A related task to sentiment analysis is the subjectivity analysis with the goal of labeling an opinion as either subjective or objective.
Task: Music Generation
Categories: Audio
Music

Musique guitar
Task: Multiobjective Optimization
Categories: Methodology

Multi-objective optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, multiattribute optimization or Pareto optimization) is an area of multiple criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. Multi-objective optimization has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives. Minimizing cost while maximizing comfort while buying a car, and maximizing performance whilst minimizing fuel consumption and emission of pollutants of a vehicle are examples of multi-objective optimization problems involving two and three objectives, respectively. In practical problems, there can be more than three objectives.
Task: Combinatorial Optimization
Categories: Methodology

**Combinatorial Optimization** is a category of problems which requires optimizing a function over a combination of discrete objects and the solutions are constrained. Examples include finding shortest paths in a graph, maximizing value in the Knapsack problem and finding boolean settings that satisfy a set of constraints. Many of these problems are NP-Hard, which means that no polynomial time solution can be developed for them. Instead, we can only produce approximations in polynomial time that are guaranteed to be some factor worse than the true optimal solution.

Source: [Recent Advances in Neural Program Synthesis ](https://arxiv.org/abs/1802.02353)
Task: Neural Architecture Search
Categories: Methodology

**Neural architecture search (NAS)** is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS essentially takes the process of a human manually tweaking a neural network and learning what works well, and automates this task to discover more complex architectures.

Image Credit : [NAS with Reinforcement Learning](https://arxiv.org/abs/1611.01578)
Task: Community Detection
Categories: 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.

Source: [Randomized Spectral Clustering in Large-Scale Stochastic Block Models ](https://arxiv.org/abs/2002.00839)
Task: Human-Object Interaction Detection
Categories: Computer Vision

Human-Object Interaction (HOI) detection is a task of identifying "a set of interactions" in an image, which involves the i) localization of the subject (i.e., humans) and target (i.e., objects) of interaction, and ii) the classification of the interaction labels.
Task: Program Synthesis
Categories: Computer Code
Reasoning

Program synthesis is the process of automatically generating a program or code snippet that satisfies a given specification or set of requirements. This can include generating code from a formal specification, a natural language description, or example inputs and outputs. The primary goal of program synthesis is to minimize human intervention in the coding process, reduce errors, and improve productivity.

Program synthesis often involves the use of advanced algorithms, artificial intelligence, and machine learning techniques to search the space of possible programs that meet the given constraints. This process can be guided by a variety of techniques, such as constraint solving, symbolic execution, and genetic algorithms.
Task: Protein Secondary Structure Prediction
Categories: Medical

Protein secondary structure prediction is a vital task in bioinformatics, aiming to determine the arrangement of amino acids in proteins, including α-helices, β-sheets, and coils. By analyzing amino acid sequences, computational algorithms and machine learning techniques predict these structural elements. This knowledge is crucial for understanding protein function and interactions. While progress has been made, challenges remain, especially with non-local interactions and low sequence homology. Advancements in machine learning hold promise for improving prediction accuracy, furthering our understanding of protein biology.
Task: Two-sample testing
Categories: Methodology
Miscellaneous

In statistical hypothesis testing, a two-sample test is a test performed on the data of two random samples, each independently obtained from a different given population. The purpose of the test is to determine whether the difference between these two populations is statistically significant. The statistics used in two-sample tests can be used to solve many machine learning problems, such as domain adaptation, covariate shift and generative adversarial networks.
Task: Gesture Recognition
Categories: Computer Vision

**Gesture Recognition** is an active field of research with applications such as automatic recognition of sign language, interaction of humans and robots or for new ways of controlling video games.

Source: [Gesture Recognition in RGB Videos Using Human Body Keypoints and Dynamic Time Warping ](https://arxiv.org/abs/1906.12171)
Task: 2D Pose Estimation
Categories: Computer Vision

detective pose
Task: Human Interaction Recognition
Categories: Computer Vision

Human Interaction Recognition (HIR) is a field of study that involves the development of computer algorithms to detect and recognize human interactions in videos, images, or other multimedia content. The goal of HIR is to automatically identify and analyze the social interactions between people, their body language, and facial expressions.
Task: Material Recognition
Categories: Computer Vision

Material recognition focuses on identifying classes, types, states, and properties of materials.
Task: Overlapping Mention Recognition
Categories: Natural Language Processing

Overlapping mention recognition is the task of correctly identifying all mentions of an entity in the presence of overlapping entity mentions.
Task: Nested Mention Recognition
Categories: Natural Language Processing

Nested mention recognition is the task of correctly modeling the nested structure of mentions.
Task: Extreme Multi-Label Classification
Categories: Methodology

Extreme Multi-Label Classification is a supervised learning problem where an instance may be associated with multiple labels. The two main problems are the unbalanced labels in the dataset and the amount of different labels.
Task: Multi-Label Text Classification
Categories: Methodology
Natural Language Processing

According to Wikipedia "In machine learning, multi-label classification and the strongly related problem of multi-output classification are variants of the classification problem where multiple labels may be assigned to each instance. Multi-label classification is a generalization of multiclass classification, which is the single-label problem of categorizing instances into precisely one of more than two classes; in the multi-label problem there is no constraint on how many of the classes the instance can be assigned to."
Task: Multi-Label Image Classification
Categories: Computer Vision

The Multi-Label Image Classification focuses on predicting labels for images in a multi-class classification problem where each image may belong to more than one class.
Task: Multi-Label Learning
Categories: Methodology
Reasoning

Multi-label learning (MLL) is a generalization of the binary and multi-category classification problems and deals with tagging a data instance with several possible class labels simultaneously [1]. Each of the assigned labels conveys a specific semantic relationship with the multi-label data instance [2, 3]. Multi-label learning has continued to receive a lot of research interest due to its practical application in many real-world problems such as recommender systems [4], image annotation [5], and text classification [6].

References:

1. Kumar, S., Rastogi, R., Low rank label subspace transformation for multi-label learning with missing labels. Information Sciences 596, 53–72 (2022)

2. Zhang M-L, Zhou Z-H (2013) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819–1837

3. Gibaja E, Ventura S (2015) A tutorial on multilabel learning. ACM Comput Surveys (CSUR) 47(3):1–38

4. Bogaert M, Lootens J, Van den Poel D, Ballings M (2019) Evaluating multi-label classifiers and recommender systems in the financial service sector. Eur J Oper Res 279(2):620– 634

5. Jing L, Shen C, Yang L, Yu J, Ng MK (2017) Multi-label classification by semi-supervised singular value decomposition. IEEE Trans Image Process 26(10):4612–4625

6. Chen Z, Ren J (2021) Multi-label text classification with latent word-wise label information. Appl Intell 51(2):966–979
Task: Image Retrieval with Multi-Modal Query
Categories: Miscellaneous

The problem of retrieving images from a database based on a multi-modal (image- text) query. Specifically, the query text prompts some modification in the query image and the task is to retrieve images with the desired modifications.
Task: Cross-Lingual
Categories: Natural Language Processing

Cross-lingual natural language processing is the task of 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: Intrinsic Image Decomposition
Categories: Computer Vision

**Intrinsic Image Decomposition** is the process of separating an image into its formation components such as reflectance (albedo) and shading (illumination). Reflectance is the color of the object, invariant to camera viewpoint and illumination conditions, whereas shading, dependent on camera viewpoint and object geometry, consists of different illumination effects, such as shadows, shading and inter-reflections. Using intrinsic images, instead of the original images, can be beneficial for many computer vision algorithms. For instance, for shape-from-shading algorithms, the shading images contain important visual cues to recover geometry, while for segmentation and detection algorithms, reflectance images can be beneficial as they are independent of confounding illumination effects. Furthermore, intrinsic images are used in a wide range of computational photography applications, such as material recoloring, relighting, retexturing and stylization.

Source: [CNN based Learning using Reflection and Retinex Models for Intrinsic Image Decomposition ](https://arxiv.org/abs/1712.01056)
Task: Image Restoration
Categories: Computer Vision

**Image Restoration** is a family of inverse problems for obtaining a high quality image from a corrupted input image. Corruption may occur due to the image-capture process (e.g., noise, lens blur), post-processing (e.g., JPEG compression), or photography in non-ideal conditions (e.g., haze, motion blur).

Source: [Blind Image Restoration without Prior Knowledge ](https://arxiv.org/abs/2003.01764)
Task: Image Clustering
Categories: Computer Vision

Models that partition the dataset into semantically meaningful clusters without having access to the ground truth labels.

Image credit: ImageNet clustering results of [SCAN: Learning to Classify Images without Labels (ECCV 2020)](https://arxiv.org/abs/2005.12320)
Task: Multi-Label Classification
Categories: Methodology
Medical
Computer Vision
Reasoning

**Multi-Label Classification** is the supervised learning problem where an instance may be associated with multiple labels. This is an extension of single-label classification (i.e., multi-class, or binary) where each instance is only associated with a single class label.

Source: [Deep Learning for Multi-label Classification ](https://arxiv.org/abs/1502.05988)
Task: Visual Place Recognition
Categories: Computer Vision

**Visual Place Recognition** is the task of matching a view of a place with a different view of the same place taken at a different time.

Source: [Visual place recognition using landmark distribution descriptors ](https://arxiv.org/abs/1608.04274)

Image credit: [Visual place recognition using landmark distribution descriptors](https://arxiv.org/pdf/1608.04274.pdf)
Task: Survival Analysis
Categories: Miscellaneous

**Survival Analysis** is a branch of statistics focused on the study of time-to-event data, usually called survival times. This type of data appears in a wide range of applications such as failure times in mechanical systems, death times of patients in a clinical trial or duration of unemployment in a population. One of the main objectives of Survival Analysis is the estimation of the so-called survival function and the hazard function. If a random variable has density function $f$ and cumulative distribution function $F$, then its survival function $S$ is $1-F$, and its hazard $λ$ is $f/S$.

Source: [Gaussian Processes for Survival Analysis ](https://arxiv.org/abs/1611.00817)

Image: [Kvamme et al.](https://arxiv.org/pdf/1910.06724v1.pdf)
Task: Document Image Classification
Categories: Computer Vision

Document image classification is the task of classifying documents based on images of their contents.

( Image credit: [Real-Time Document Image Classification using Deep CNN and Extreme Learning Machines](https://arxiv.org/pdf/1711.05862v1.pdf) )