3497hyy/evaluation-tables huggingface dataset:
Task: Clickbait Detection
Categories: Natural Language Processing
Playing Games

Clickbait detection is the task of identifying clickbait, a form of false advertisement, that uses hyperlink text or a thumbnail link that is designed to attract attention and to entice users to follow that link and read, view, or listen to the linked piece of online content, with a defining characteristic of being deceptive, typically sensationalized or misleading (Source: Adapted from Wikipedia)
Task: Negation Detection
Categories: Natural Language Processing

Negation detection is the task of identifying negation cues in text.
Task: Outlier Detection
Categories: Methodology
Graphs

**Outlier Detection** is a task of identifying a subset of a given data set which are considered anomalous in that they are unusual from other instances. It is one of the core data mining tasks and is central to many applications. In the security field, it can be used to identify potentially threatening users, in the manufacturing field it can be used to identify parts that are likely to fail.

Source: [Coverage-based Outlier Explanation ](https://arxiv.org/abs/1911.02617)
Task: Stance Detection
Categories: Natural Language Processing

Stance detection is the extraction of a subject's reaction to a claim made by a primary actor. It is a core part of a set of approaches to fake news assessment.

Example:

* Source: "Apples are the most delicious fruit in existence"
* Reply: "Obviously not, because that is a reuben from Katz's"
* Stance: deny
Task: Contour Detection
Categories: Computer Vision

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

Source: [Object Contour and Edge Detection with RefineContourNet ](https://arxiv.org/abs/1904.13353)
Task: Edge Detection
Categories: Computer Vision

**Edge Detection** is a fundamental image processing technique which involves computing an image gradient to quantify the magnitude and direction of edges in an image. Image gradients are used in various downstream tasks in computer vision such as line detection, feature detection, and image classification.

Source: [Artistic Enhancement and Style Transfer of Image Edges using Directional Pseudo-coloring ](https://arxiv.org/abs/1906.07981)

( Image credit: [Kornia](https://github.com/kornia/kornia) )
Task: Boundary Detection
Categories: Computer Vision

**Boundary Detection** is a vital part of extracting information encoded in images, allowing for the computation of quantities of interest including density, velocity, pressure, etc.

Source: [A Locally Adapting Technique for Boundary Detection using Image Segmentation ](https://arxiv.org/abs/1707.09030)
Task: Adversarial Text
Categories: Adversarial

Adversarial Text refers to a specialised text sequence that is designed specifically to influence the prediction of a language model. Generally, Adversarial Text attack are carried out on Large Language Models (LLMs). Research on understanding different adversarial approaches can help us build effective defense mechanisms to detect malicious text input and build robust language models.
Task: Adversarial Attack
Categories: Adversarial
Computer Vision

An **Adversarial Attack** is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and imperceptible to human eyes.

Source: [Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks ](https://arxiv.org/abs/2002.05388)
Task: Adversarial Defense
Categories: Adversarial

Competitions with currently unpublished results:

- [TrojAI](https://pages.nist.gov/trojai/)
Task: Robotic Grasping
Categories: Robots
Miscellaneous

This task is composed of using Deep Learning to identify how best to grasp objects using robotic arms in different scenarios. This is a very complex task as it might involve dynamic environments and objects unknown to the network.
Task: Multiple Instance Learning
Categories: Methodology

**Multiple Instance Learning** is a type of weakly supervised learning algorithm where training data is arranged in bags, where each bag contains a set of instances $X=\\{x_1,x_2, \ldots,x_M\\}$, and there is one single label $Y$ per bag, $Y\in\\{0, 1\\}$ in the case of a binary classification problem. It is assumed that individual labels $y_1, y_2,\ldots, y_M$ exist for the instances within a bag, but they are unknown during training. In the standard Multiple Instance assumption, a bag is considered negative if all its instances are negative. On the other hand, a bag is positive, if at least one instance in the bag is positive.

Source: [Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification ](https://arxiv.org/abs/1812.11560)
Task: Open Information Extraction
Categories: Natural Language Processing

In natural language processing, open information extraction is the task of generating a structured, machine-readable representation of the information in text, usually in the form of triples or n-ary propositions (Source: Wikipedia).
Task: Information Extraction
Categories: Medical
Natural Language Processing

Information extraction is the task of automatically extracting structured information from unstructured and / or semi-structured machine-readable documents and other electronically represented sources (Source: Wikipedia).
Task: Recipe Generation
Categories: Natural Language Processing
Miscellaneous

Food recipe generation from ingredients, title and/or food images.
Task: Medical Relation Extraction
Categories: Medical

Biomedical relation extraction is the task of detecting and classifying semantic relationships from biomedical text.
Task: Object Localization
Categories: Computer Vision

**Object Localization** is the task of locating an instance of a particular object category in an image, typically by specifying a tightly cropped bounding box centered on the instance. An object proposal specifies a candidate bounding box, and an object proposal is said to be a correct localization if it sufficiently overlaps a human-labeled “ground-truth” bounding box for the given object. In the literature, the “Object Localization” task is to locate one instance of an object category, whereas “object detection” focuses on locating all instances of a category in a given image.

Source: [Fast On-Line Kernel Density Estimation for Active Object Localization ](https://arxiv.org/abs/1611.05369)
Task: Emotion Recognition
Categories: Audio
Speech
Natural Language Processing
Miscellaneous
Computer Vision

**Emotion Recognition** is an important area of research to enable effective human-computer interaction. Human emotions can be detected using speech signal, facial expressions, body language, and electroencephalography (EEG). Source: [Using Deep Autoencoders for Facial Expression Recognition ](https://arxiv.org/abs/1801.08329)
Task: Action Localization
Categories: Computer Vision

Action Localization is finding the spatial and temporal co ordinates for an action in a video. An action localization model will identify which frame an action start and ends in video and return the x,y coordinates of an action. Further the co ordinates will change when the object performing action undergoes a displacement.
Task: Visual Localization
Categories: Computer Vision

**Visual Localization** is the problem of estimating the camera pose of a given image relative to a visual representation of a known scene.

Source: [Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization ](https://arxiv.org/abs/1908.06387)
Task: Lexical Normalization
Categories: Natural Language Processing

Lexical normalization is the task of translating/transforming a non standard text to a standard register.

Example:

```
new pix comming tomoroe
new pictures coming tomorrow
```

Datasets usually consists of tweets, since these naturally contain a fair amount of
these phenomena.

For lexical normalization, only replacements on the word-level are annotated.
Some corpora include annotation for 1-N and N-1 replacements. However, word
insertion/deletion and reordering is not part of the task.
Task: Lexical Simplification
Categories: Natural Language Processing

The goal of **Lexical Simplification** is to replace complex words (typically words that are used less often in language and are therefore less familiar to readers) with their simpler synonyms, without infringing the grammaticality and changing the meaning of the text.

Source: [Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization ](https://arxiv.org/abs/1809.04163)
Task: Semantic Role Labeling
Categories: Natural Language Processing

Semantic role labeling aims to model the predicate-argument structure of a sentence
and is often described as answering "Who did what to whom". BIO notation is typically
used for semantic role labeling.

Example:

| Housing | starts | are | expected | to | quicken | a | bit | from | August’s | pace |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| B-ARG1 | I-ARG1 | O | O | O | V | B-ARG2 | I-ARG2 | B-ARG3 | I-ARG3 | I-ARG3 |
Task: Semantic Textual Similarity
Categories: Natural Language Processing

Semantic textual similarity deals with determining how similar two pieces of texts are.
This can take the form of assigning a score from 1 to 5. Related tasks are paraphrase or duplicate identification.

Image source: [Learning Semantic Textual Similarity from Conversations](https://arxiv.org/pdf/1804.07754.pdf)
Task: Semantic Composition
Categories: Natural Language Processing

Understanding the meaning of text by composing the meanings of the individual words in the text (Source: https://arxiv.org/pdf/1405.7908.pdf)
Task: Semantic Parsing
Categories: Natural Language Processing

**Semantic Parsing** is the task of transducing natural language utterances into formal meaning representations. The target meaning representations can be defined according to a wide variety of formalisms. This include linguistically-motivated semantic representations that are designed to capture the meaning of any sentence such as λ-calculus or the abstract meaning representations. Alternatively, for more task-driven approaches to Semantic Parsing, it is common for meaning representations to represent executable programs such as SQL queries, robotic commands, smart phone instructions, and even general-purpose programming languages like Python and Java.

Source: [Tranx: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation ](https://arxiv.org/abs/1810.02720)
Task: Grammatical Error Correction
Categories: Natural Language Processing

Grammatical Error Correction (GEC) is the task of correcting different kinds of errors in text such as spelling, punctuation, grammatical, and word choice errors.

GEC is typically formulated as a sentence correction task. A GEC system takes a potentially erroneous sentence as input and is expected to transform it to its corrected version. See the example given below:

| Input (Erroneous) | Output (Corrected) |
| ------------------------- | ---------------------- |
|She see Tom is catched by policeman in park at last night. | She saw Tom caught by a policeman in the park last night.|
Task: Dependency Parsing
Categories: Natural Language Processing

Dependency parsing is the task of extracting a dependency parse of a sentence that represents its grammatical
structure and defines the relationships between "head" words and words, which modify those heads.

Example:

```
root
|
| +-------dobj---------+
| | |
nsubj | | +------det-----+ | +-----nmod------+
+--+ | | | | | | |
| | | | | +-nmod-+| | | +-case-+ |
+ | + | + + || + | + | |
I prefer the morning flight through Denver
```

Relations among the words are illustrated above the sentence with directed, labeled
arcs from heads to dependents (+ indicates the dependent).
Task: General Classification
Categories: Methodology

Algorithms trying to solve the general task of classification.
Task: Audio Classification
Categories: Audio

**Audio Classification** is a machine learning task that involves identifying and tagging audio signals into different classes or categories. The goal of audio classification is to enable machines to automatically recognize and distinguish between different types of audio, such as music, speech, and environmental sounds.
Task: Environmental Sound Classification
Categories: Audio

Classification of Environmental Sounds. Most often sounds found in Urban environments. Task related to noise monitoring.
Task: Scene Classification
Categories: Computer Vision

**Scene Classification** is a task in which scenes from photographs are categorically classified. Unlike object classification, which focuses on classifying prominent objects in the foreground, Scene Classification uses the layout of objects within the scene, in addition to the ambient context, for classification.

Source: [Scene classification with Convolutional Neural Networks ](http://cs231n.stanford.edu/reports/2017/pdfs/102.pdf)
Task: Acoustic Scene Classification
Categories: Audio

The goal of acoustic scene classification is to classify a test recording into one of the provided predefined classes that characterizes the environment in which it was recorded.

Source: [DCASE 2019](http://dcase.community/challenge2019/task-acoustic-scene-classification)
Source: [DCASE 2018](https://dcase.community/challenge2018/task-acoustic-scene-classification)
Task: Pose Estimation
Categories: Computer Vision

**Pose Estimation** is a computer vision task where the goal is to detect the position and orientation of a person or an object. Usually, this is done by predicting the location of specific keypoints like hands, head, elbows, etc. in case of Human Pose Estimation.

A common benchmark for this task is [MPII Human Pose](https://paperswithcode.com/sota/pose-estimation-on-mpii-human-pose)

( Image credit: [Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose](https://github.com/Daniil-Osokin/lightweight-human-pose-estimation.pytorch) )
Task: Intent Classification
Categories: Natural Language Processing

**Intent Classification** is the task of correctly labeling a natural language utterance from a predetermined set of intents

Source: [Multi-Layer Ensembling Techniques for Multilingual Intent Classification ](https://arxiv.org/abs/1806.07914)
Task: Sentiment Analysis
Categories: Natural Language Processing
Computer Vision

**Sentiment Analysis** is the task of classifying the polarity of a given text. For instance, a text-based tweet can be categorized into either "positive", "negative", or "neutral". Given the text and accompanying labels, a model can be trained to predict the correct sentiment.

**Sentiment Analysis** techniques can be categorized into machine learning approaches, lexicon-based approaches, and even hybrid methods. Some subcategories of research in sentiment analysis include: multimodal sentiment analysis, aspect-based sentiment analysis, fine-grained opinion analysis, language specific sentiment analysis.

More recently, deep learning techniques, such as RoBERTa and T5, are used to train high-performing sentiment classifiers that are evaluated using metrics like F1, recall, and precision. To evaluate sentiment analysis systems, benchmark datasets like SST, GLUE, and IMDB movie reviews are used.

Further readings:

- [Sentiment Analysis Based on Deep Learning: A Comparative Study](https://paperswithcode.com/paper/sentiment-analysis-based-on-deep-learning-a)
Task: Complex Word Identification
Categories: Natural Language Processing

Identifying difficult words or expressions in a text.
Task: Language Identification
Categories: Audio
Natural Language Processing

Language identification is the task of determining the language of a text.
Task: Entity Alignment
Categories: Knowledge Base
Natural Language Processing

**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.

Source: [Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding ](https://arxiv.org/abs/1708.05045)

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: Entity Linking
Categories: Natural Language Processing

Assigning a unique identity to entities (such as famous individuals, locations, or companies) mentioned in text (Source: Wikipedia).
Task: Named Entity Recognition (NER)
Categories: Natural Language Processing

**Named Entity Recognition (NER)** is a task of Natural Language Processing (NLP) that involves identifying and classifying named entities in a text into predefined categories such as person names, organizations, locations, and others. The goal of NER is to extract structured information from unstructured text data and represent it in a machine-readable format. Approaches typically use BIO notation, which differentiates the beginning (B) and the inside (I) of entities. O is used for non-entity tokens.

Example:

| Mark | Watney | visited | Mars |
| --- | ---| --- | --- |
| B-PER | I-PER | O | B-LOC |

( Image credit: [Zalando](https://research.zalando.com/welcome/mission/research-projects/flair-nlp/) )
Task: Image Shadow Removal
Categories: Computer Vision

Merge with the Shadow Removal
Task: Table-based Fact Verification
Categories: Natural Language Processing

Verifying facts given semi-structured data.
Task: Visual Relationship Detection
Categories: Computer Vision

Visual relationship detection (VRD) is one newly developed computer vision task aiming to recognize relations or interactions between objects in an image. It is a further learning task after object recognition and is essential for fully understanding images, even the visual world.
Task: Slot Filling
Categories: Natural Language Processing

The goal of **Slot Filling** is to identify from a running dialog different slots, which correspond to different parameters of the user’s query. For instance, when a user queries for nearby restaurants, key slots for location and preferred food are required for a dialog system to retrieve the appropriate information. Thus, the main challenge in the slot-filling task is to extract the target entity.

Source: [Real-time On-Demand Crowd-powered Entity Extraction ](https://arxiv.org/abs/1704.03627)

Image credit: [Robust Retrieval Augmented Generation for Zero-shot Slot Filling](https://arxiv.org/pdf/2108.13934.pdf)
Task: Infrared And Visible Image Fusion
Categories: Computer Vision

Image fusion with paired infrared and visible images
Task: Human Detection
Categories: Computer Vision

DEFINICIÓN
EL Granada SOUND es un festival de música india de ámbito nacional e internacional que se celebra en Granada, España. Desde su primera edición en 2012, el evento ha ido creciendo en popularidad. El festival tiene lugar durante dos días del mes de septiembre en el Cortijo del Conde, ofreciendo una experiencia única al mezclar la música con la cultura de la ciudad.
La edición de este año está programada para el 12 y 13 de septiembre. El cartel incluye a Mikel Ízalo, Zahara, Xoel López, Miss Caffeina, Ventiuno, Niña Polaca, Karavana, Paula Mattheus, Barry B, Viva Suecia, Ojete Calor, Siloé, Dorian, SamuraÏ, Besmaya, Carlos Ares, Mafalda Cardenal, Melifluo.
El festival lo organiza The Music Repúblic, (una promotora famosa a nivel nacional que produce y gestiona eventos musicales en toda España desde 2010). No hay información actualizada de quien es el director de la edición de 2025, pero otros años ha sido Antonio Romero.
Además de la promoción de eventos, The Music Republic ofrece servicios de management y booking para artistas, consolidándose como una de las empresas más destacadas en la industria musical nacional.
Con sede en Valencia, la empresa ha experimentado un crecimiento significativo en los últimos años, gestionando eventos a lo largo de toda la geografía española y colaborando con una amplia variedad de artistas y profesionales del sector.

DAFO
Fortalezas:
Reconocimiento y trayectoria: El festival cuenta con una sólida reputación y experiencia en la organización de eventos musicales, lo que le otorga prestigio y confianza entre los asistentes.
Variedad artística: Ofrece una programación diversa que atrae a diferentes públicos, enriqueciendo la oferta cultural de la ciudad.
Ubicación estratégica: Celebrado en Granada, una ciudad con una rica tradición cultural y turística, lo que facilita la asistencia de público local y foráneo.
Debilidades:
Falta de presencia de grupos locales: En ediciones anteriores, se ha observado una limitada participación de bandas locales, lo que podría desaprovechar el talento regional y restar identidad al festival.
Dependencia de patrocinadores: Como muchos eventos de gran escala, el festival puede depender en exceso de patrocinadores, lo que podría afectar su autonomía en decisiones artísticas.
Oportunidades:
Expansión de actividades complementarias: Integrar eventos previos y paralelos en distintos puntos de la ciudad puede enriquecer la experiencia del público y promover la economía local.
Colaboraciones con artistas locales: Incorporar más talento local en la programación podría fortalecer la identidad del festival y fomentar la escena musical de Granada.
Amenazas:
Competencia de otros festivales: La proliferación de festivales en España puede restar público y recursos al Granada Sound.
Factores externos imprevisibles: Situaciones como condiciones climáticas adversas o cambios en las normativas pueden afectar la realización del evento.
Task: Intent Detection
Categories: Natural Language Processing

**Intent Detection** is a task of determining the underlying purpose or goal behind a user's search query given a context. The task plays a significant role in search and recommendations.
A traditional approach for intent detection implies using an intent detector model to classify user search query into predefined intent categories, given a context.
One of the key challenges of the task implies identifying user intents for cold-start sessions, i.e., search sessions initiated by a non-logged-in
or unrecognized user.

Source: [Analyzing and Predicting Purchase Intent in E-commerce: Anonymous vs. Identified Customers](https://arxiv.org/abs/2012.08777)
Task: Action Detection
Categories: Time Series
Computer Vision

Action Detection aims to find both where and when an action occurs within a video clip and classify what the action is taking place. Typically results are given in the form of action tublets, which are action bounding boxes linked across time in the video. This is related to temporal localization, which seeks to identify the start and end frame of an action, and action recognition, which seeks only to classify which action is taking place and typically assumes a trimmed video.
Task: Fake News Detection
Categories: Natural Language Processing

**Fake News Detection** is a natural language processing task that involves identifying and classifying news articles or other types of text as real or fake. The goal of fake news detection is to develop algorithms that can automatically identify and flag fake news articles, which can be used to combat misinformation and promote the dissemination of accurate information.