3497hyy/evaluation-tables huggingface dataset:
Task: Optical Character Recognition (OCR)
Categories: Methodology
Natural Language Processing
Computer Vision

**Optical Character Recognition** or **Optical Character Reader** (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo (for example the text on signs and billboards in a landscape photo, license plates in cars...) or from subtitle text superimposed on an image (for example: from a television broadcast)
Task: Deblurring
Categories: Computer Vision

**Deblurring** is a computer vision task that involves removing the blurring artifacts from images or videos to restore the original, sharp content. Blurring can be caused by various factors such as camera shake, fast motion, and out-of-focus objects, and can result in a loss of detail and quality in the captured images. The goal of deblurring is to produce a clear, high-quality image that accurately represents the original scene.

( Image credit: [Deblurring Face Images using Uncertainty Guided Multi-Stream Semantic Networks](https://arxiv.org/pdf/1907.13106v1.pdf) )
Task: Structured Prediction
Categories: Methodology

**Structured Prediction** is an area of machine learning focusing on representations of spaces with combinatorial structure, and algorithms for inference and parameter estimation over these structures. Core methods include both tractable exact approaches like dynamic programming and spanning tree algorithms as well as heuristic techniques such as linear programming relaxations and greedy search.

Source: [Torch-Struct: Deep Structured Prediction Library ](https://arxiv.org/abs/2002.00876)
Task: Trajectory Prediction
Categories: Time Series
Computer Vision

**Trajectory Prediction** is the problem of predicting the short-term (1-3 seconds) and long-term (3-5 seconds) spatial coordinates of various road-agents such as cars, buses, pedestrians, rickshaws, and animals, etc. These road-agents have different dynamic behaviors that may correspond to aggressive or conservative driving styles.

Source: [Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs ](https://arxiv.org/abs/1912.01118)
Task: Depth Estimation
Categories: Computer Vision

**Depth Estimation** is the task of measuring the distance of each pixel relative to the camera. Depth is extracted from either monocular (single) or stereo (multiple views of a scene) images. Traditional methods use multi-view geometry to find the relationship between the images. Newer methods can directly estimate depth by minimizing the regression loss, or by learning to generate a novel view from a sequence. The most popular benchmarks are KITTI and NYUv2. Models are typically evaluated according to a RMS metric.

Source: [DIODE: A Dense Indoor and Outdoor DEpth Dataset ](https://arxiv.org/abs/1908.00463)
Task: Medical Code Prediction
Categories: Medical

Context: Prediction of medical codes from clinical notes is both a practical and essential need for every healthcare delivery organization within current medical systems. Automating annotation will save significant time and excessive effort by human coders today. A new milestone will mark a meaningful step toward fully Autonomous Medical Coding in machines reaching parity with human coders' performance in medical code prediction.

Question: What exactly is the medical code prediction problem?

Answer: Clinical notes contain much information about what precisely happened during the patient's entire stay. And those clinical notes (e.g., discharge summary) is typically long, loosely structured, consists of medical domain language, and sometimes riddled with spelling errors. So, it's a highly multi-label classification problem, and the forthcoming ICD-11 standard will add more complexity to the problem! The medical code prediction problem is to annotate this clinical note with multiple codes subset from nearly 70K total codes (in the current ICD-10 system, for example).
Task: Link Prediction
Categories: Graphs
Natural Language Processing

**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.

( Image credit: [Inductive Representation Learning on Large Graphs](https://arxiv.org/pdf/1706.02216v4.pdf) )
Task: Word Alignment
Categories: Natural Language Processing

**Word Alignment** is the task of finding the correspondence between source and target words in a pair of sentences that are translations of each other.

Source: [Neural Network-based Word Alignment through Score Aggregation ](https://arxiv.org/abs/1606.09560)
Task: Super-Resolution
Categories: Graphs
Computer Vision

**Super-Resolution** is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: [MemNet](https://github.com/tyshiwo/MemNet) )
Task: Single-cell modeling
Categories: Medical

Single Cell RNA sequencing (scRNAseq) revolutionized our understanding of the fundamental of life sciences. The technology enables an unprecedented resolution to study heterogeneity in cell populations and their functionalities.
Task: Electroencephalogram (EEG)
Categories: Methodology
Medical
Time Series

**Electroencephalogram (EEG)** is a method of recording brain activity using electrophysiological indexes. When the brain is active, a large number of postsynaptic potentials generated synchronously by neurons are formed after summation. It records the changes of electric waves during brain activity and is the overall reflection of the electrophysiological activities of brain nerve cells on the surface of cerebral cortex or scalp. Brain waves originate from the postsynaptic potential of the apical dendrites of pyramidal cells. The formation of synchronous rhythm of EEG is also related to the activity of nonspecific projection system of cortex and thalamus. EEG is the basic theoretical research of brain science. EEG monitoring is widely used in its clinical application.
Task: Facial Recognition and Modelling
Categories: Music
Miscellaneous
Computer Vision

Facial tasks in machine learning operate based on images or video frames (or other datasets) focussed on human faces.
Task: Autonomous Vehicles
Categories: Robots
Computer Code
Computer Vision

Autonomous vehicles is the task of making a vehicle that can guide itself without human conduction.

Many of the state-of-the-art results can be found at more general task pages such as [3D Object Detection](https://paperswithcode.com/task/3d-object-detection) and [Semantic Segmentation](https://paperswithcode.com/task/semantic-segmentation).

( Image credit: [GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision](https://arxiv.org/abs/2007.13124) )
Task: Image Manipulation Detection
Categories: Computer Vision

The task of detecting images or image parts that have been tampered or manipulated (sometimes also referred to as doctored). This typically encompasses image splicing, copy-move, or image inpainting.
Task: Image-to-Image Translation
Categories: Computer Vision

**Image-to-Image Translation** is a task in computer vision and machine learning where the goal is to learn a mapping between an input image and an output image, such that the output image can be used to perform a specific task, such as style transfer, data augmentation, or image restoration.

( Image credit: [Unpaired Image-to-Image Translation
using Cycle-Consistent Adversarial Networks](https://arxiv.org/pdf/1703.10593v6.pdf) )
Task: Domain Adaptation
Categories: Methodology
Computer Vision

**Domain Adaptation** is the task of adapting models across domains. This is motivated by the challenge where the test and training datasets fall from different data distributions due to some factor. Domain adaptation aims to build machine learning models that can be generalized into a target domain and dealing with the discrepancy across domain distributions.

Further readings:

- [A Brief Review of Domain Adaptation](https://paperswithcode.com/paper/a-brief-review-of-domain-adaptation)

( Image credit: [Unsupervised Image-to-Image Translation Networks](https://arxiv.org/pdf/1703.00848v6.pdf) )
Task: Speech Recognition
Categories: Audio
Speech

**Speech Recognition** is the task of converting spoken language into text. It involves recognizing the words spoken in an audio recording and transcribing them into a written format. The goal is to accurately transcribe the speech in real-time or from recorded audio, taking into account factors such as accents, speaking speed, and background noise.

( Image credit: [SpecAugment](https://arxiv.org/pdf/1904.08779v2.pdf) )
Task: Part-Of-Speech Tagging
Categories: Natural Language Processing

Part-of-speech tagging (POS tagging) is the task of tagging a word in a text with its part of speech.
A part of speech is a category of words with similar grammatical properties. Common English
parts of speech are noun, verb, adjective, adverb, pronoun, preposition, conjunction, etc.

Example:

| Vinken | , | 61 | years | old |
| --- | ---| --- | --- | --- |
| NNP | , | CD | NNS | JJ |
Task: Data Integration
Categories: Knowledge Base
Natural Language Processing

**Data integration** (also called information integration) is the process of consolidating data from a set of heterogeneous data sources into a single uniform data set (materialized integration) or view on the data (virtual integration). Data integration pipelines involve subtasks such as schema matching, table annotation, entity resolution, value normalization, data cleansing, and data fusion. Application domains of data integration include data warehousing, data lakes, and knowledge base consolidation.
Surveys on Data integration:

- [Dong, Srivastava: Big data integration](https://ieeexplore.ieee.org/abstract/document/6544914), 2013.

- [Doan, Halevy, Ives: Principles of Data Integration](https://research.cs.wisc.edu/dibook/), 2012.
Task: Dialogue
Categories: Speech
Natural Language Processing

Dialogue is notoriously hard to evaluate. Past approaches have used human evaluation.
Task: Arabic Sentiment Analysis
Categories: Natural Language Processing

Arabic sentiment analysis is the process of computationally identifying and categorizing opinions expressed in a piece of arabic text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral (Source: Oxford Languages)
Task: Text-To-Speech Synthesis
Categories: Audio
Speech
Natural Language Processing

**Text-To-Speech Synthesis** is a machine learning task that involves converting written text into spoken words. The goal is to generate synthetic speech that sounds natural and resembles human speech as closely as possible.
Task: Learning-To-Rank
Categories: Graphs
Miscellaneous

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: 3D Multi-Person Pose Estimation (root-relative)
Categories: Computer Vision

This task aims to solve root-relative 3D multi-person pose estimation (person-centric coordinate system). No ground truth human bounding box and human root joint coordinates are used during testing stage.

( Image credit: [RootNet](https://github.com/mks0601/3DMPPE_ROOTNET_RELEASE) )
Task: Relation Extraction
Categories: Natural Language Processing

**Relation Extraction** is the task of predicting attributes and relations for entities in a sentence. For example, given a sentence “Barack Obama was born in Honolulu, Hawaii.”, a relation classifier aims at predicting the relation of “bornInCity”. Relation Extraction is the key component for building relation knowledge graphs, and it is of crucial significance to natural language processing applications such as structured search, sentiment analysis, question answering, and summarization.

Source: [Deep Residual Learning for Weakly-Supervised Relation Extraction ](https://arxiv.org/abs/1707.08866)
Task: Video Understanding
Categories: Computer Vision

A crucial task of **Video Understanding** is to recognise and localise (in space and time) different actions or events appearing in the video.

Source: [Action Detection from a Robot-Car Perspective ](https://arxiv.org/abs/1807.11332)
Task: Hypernym Discovery
Categories: Natural Language Processing

Given a corpus and a target term (hyponym), the task of hypernym discovery consists of extracting a set of its most appropriate hypernyms from the corpus. For example, for the input word “dog”, some valid hypernyms would be “canine”, “mammal” or “animal”.
Task: Stochastic Optimization
Categories: Methodology
Miscellaneous

**Stochastic Optimization** is the task of optimizing certain objective functional by generating and using stochastic random variables. Usually the Stochastic Optimization is an iterative process of generating random variables that progressively finds out the minima or the maxima of the objective functional. Stochastic Optimization is usually applied in the non-convex functional spaces where the usual deterministic optimization such as linear or quadratic programming or their variants cannot be used.

Source: [ASOC: An Adaptive Parameter-free Stochastic Optimization Techinique for Continuous Variables ](https://arxiv.org/abs/1506.08004)
Task: Instance Search
Categories: Audio
Computer Vision

Visual **Instance Search** is the task of retrieving from a database of images the ones that contain an instance of a visual query. It is typically much more challenging than finding images from the database that contain objects belonging to the same category as the object in the query. If the visual query is an image of a shoe, visual Instance Search does not try to find images of shoes, which might differ from the query in shape, color or size, but tries to find images of the exact same shoe as the one in the query image. Visual Instance Search challenges image representations as the features extracted from the images must enable such fine-grained recognition despite variations in viewpoints, scale, position, illumination, etc. Whereas holistic image representations, where each image is mapped to a single high-dimensional vector, are sufficient for coarse-grained similarity retrieval, local features are needed for instance retrieval.

Source: [Dynamicity and Durability in Scalable Visual Instance Search ](https://arxiv.org/abs/1805.10942)
Task: Person Search
Categories: Computer Vision

**Person Search** is a task which aims at matching a specific person among a great number of whole scene images.

Source: [Re-ID Driven Localization Refinement for Person Search ](https://arxiv.org/abs/1909.08580)
Task: Keyword Spotting
Categories: Speech
Computer Vision

In speech processing, keyword spotting deals with the identification of keywords in utterances.

( Image credit: [Simon Grest](https://github.com/simongrest/kaggle-freesound-audio-tagging-2019) )
Task: 3D Human Pose Estimation
Categories: Computer Vision

**3D Human Pose Estimation** is a computer vision task that involves estimating the 3D positions and orientations of body joints and bones from 2D images or videos. The goal is to reconstruct the 3D pose of a person in real-time, which can be used in a variety of applications, such as virtual reality, human-computer interaction, and motion analysis.
Task: Text Spotting
Categories: Computer Vision

Scene Text Spotting is the combination of Scene Text Detection and Scene Text Recognition in an end-to-end manner.
It is the ability to read natural text in the wild.
Task: Imputation
Categories: Time Series
Computer Vision

Substituting missing data with values according to some criteria.
Task: Image Quality Estimation
Categories: Computer Vision

Same as [Image Quality Assessment](https://paperswithcode.com/task/image-quality-assessment).
Task: Video Quality Assessment
Categories: Time Series
Computer Vision

Video Quality Assessment is a computer vision task aiming to mimic video-based human subjective perception. The goal is to produce a mos score, where higher score indicates better perceptual quality. Some well-known benchmarks for this task are KoNViD-1k, LIVE-VQC, YouTube-UGC and LSVQ. SROCC/PLCC/RMSE are usually used to evaluate the performance of different models.
Task: Image Enhancement
Categories: Computer Vision

**Image Enhancement** is basically improving the interpretability or perception of information in images for human viewers and providing ‘better’ input for other automated image processing techniques. The principal objective of Image Enhancement is to modify attributes of an image to make it more suitable for a given task and a specific observer.

Source: [A Comprehensive Review of Image Enhancement Techniques ](https://arxiv.org/abs/1003.4053)
Task: Machine Translation
Categories: Natural Language Processing

**Machine translation** is the task of translating a sentence in a source language to a different target language.

Approaches for machine translation can range from rule-based to statistical to neural-based. More recently, encoder-decoder attention-based architectures like BERT have attained major improvements in machine translation.

One of the most popular datasets used to benchmark machine translation systems is the WMT family of datasets. Some of the most commonly used evaluation metrics for machine translation systems include BLEU, METEOR, NIST, and others.

( Image credit: [Google seq2seq](https://github.com/google/seq2seq) )
Task: Medical Image Segmentation
Categories: Medical
Computer Vision

**Medical Image Segmentation** is a computer vision task that involves dividing an medical image into multiple segments, where each segment represents a different object or structure of interest in the image. The goal of medical image segmentation is to provide a precise and accurate representation of the objects of interest within the image, typically for the purpose of diagnosis, treatment planning, and quantitative analysis.

( Image credit: [IVD-Net](https://github.com/josedolz/IVD-Net) )
Task: Image Generation
Categories: Medical
Natural Language Processing
Miscellaneous
Computer Vision

**Image Generation** (synthesis) is the task of generating new images from an existing dataset.

- **Unconditional generation** refers to generating samples unconditionally from the dataset, i.e. $p(y)$
- **[Conditional image generation](/task/conditional-image-generation)** (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.

In this section, you can find state-of-the-art leaderboards for **unconditional generation**. For conditional generation, and other types of image generations, refer to the subtasks.

( Image credit: [StyleGAN](https://github.com/NVlabs/stylegan) )
Task: Scene Generation
Categories: Computer Vision

make to t shirt an Ad with a little bit of action
Task: Style Transfer
Categories: Computer Vision

**Style Transfer** is a technique in computer vision and graphics that involves generating a new image by combining the content of one image with the style of another image. The goal of style transfer is to create an image that preserves the content of the original image while applying the visual style of another image.

( Image credit: [A Neural Algorithm of Artistic Style](https://arxiv.org/pdf/1508.06576v2.pdf) )

1. "T" as a sofa:

The "T" horizontal strip can mimic the back of a sofa with a delicate cushion or details of the uphols or appliances with the color button.

The "T" vertical strip can show a feet or arm of the sofa, shiny, yet firm.

2. Merge "P":

Put "P" next to "T", your curve to delicately with the top "T." It is intertwined. The circular part of "P" can show a cushion or a curved chair and synchronize the subject of furniture.

Make sure "P" is visually relying on "T", which reflects the relationship of cohesion and balance.

5. Coherence of "B" and "I":

"B" can be aligned as a pair of cushions or a modern chair, with mild curves with glossy and modern aesthetics.

"I" can be a symbol of a shiny furniture or a vertical light bar and completes the shapes without overburdess them.

Color palette 4:

Includes soft soil colors such as beige, top and gray shades, along with silent or silver gold tips to touch elegance.

Consider a slope effect to enhance modernity, to keep colors elegant and complex.

5. Connect the letters:

Use the overlap or intertwined edges that the letters meet for the symbol of unity.

The plan should allow viewers to distinguish each letter while feeling part of the same "structure".

6. Background patterns:

Use delicate geometric patterns or textures that mimic fabrics or furniture materials such as wood seeds or woven fibers.

These patterns must remain minimalist and focus on highlighting the logo, while maintaining communication.

While it deals with the subject of furniture and design, this concept conveys modernity, creativity and professional. If you like, I can create a draft design for better visualization.
Task: Person Re-Identification
Categories: Computer Vision

**Person Re-Identification** is a computer vision task in which the goal is to match a person's identity across different cameras or locations in a video or image sequence. It involves detecting and tracking a person and then using features such as appearance, body shape, and clothing to match their identity in different frames. The goal is to associate the same person across multiple non-overlapping camera views in a robust and efficient manner.
Task: 3D Reconstruction
Categories: Methodology
Computer Vision

**3D Reconstruction** is the task of creating a 3D model or representation of an object or scene from 2D images or other data sources. The goal of 3D reconstruction is to create a virtual representation of an object or scene that can be used for a variety of purposes, such as visualization, animation, simulation, and analysis. It can be used in fields such as computer vision, robotics, and virtual reality.

Image: [Gwak et al](https://arxiv.org/pdf/1705.10904v2.pdf)
Task: Open Set Learning
Categories: Miscellaneous

Traditional supervised learning aims to train a classifier in the closed-set world, where training and test samples share the same label space. Open set learning (OSL) is a more challenging and realistic setting, where there exist test samples from the classes that are unseen during training. Open set recognition (OSR) is the sub-task of detecting test samples which do not come from the training.
Task: Music Source Separation
Categories: Music

Music source separation is the task of decomposing music into its constitutive components, e. g., yielding separated stems for the vocals, bass, and drums.

( Image credit: [SigSep](https://github.com/sigsep ) )
Task: Temporal Action Localization
Categories: Computer Vision

Temporal Action Localization aims to detect activities in the video stream and output beginning and end timestamps. It is closely related to Temporal Action Proposal Generation.
Task: Efficient Exploration
Categories: Methodology

**Efficient Exploration** is one of the main obstacles in scaling up modern deep reinforcement learning algorithms. The main challenge in Efficient Exploration is the balance between exploiting current estimates, and gaining information about poorly understood states and actions.

Source: [Randomized Value Functions via Multiplicative Normalizing Flows ](https://arxiv.org/abs/1806.02315)