3497hyy/evaluation-tables huggingface dataset:
Task: Optic Cup Segmentation
Categories: Medical

Optic cup segmentation, concentric with optic disc, useful for glaucoma management (ophthalmology)
Task: Optic Cup Detection
Categories: Medical

Region proposal for optic cup
Task: Optic Disc Detection
Categories: Medical

Region proposal for optic disc
Task: OpenAI Gym
Categories: Playing Games

An open-source toolkit from OpenAI that implements several Reinforcement Learning benchmarks including: classic control, Atari, Robotics and MuJoCo tasks.

(Description by [Evolutionary learning of interpretable decision trees](https://paperswithcode.com/paper/evolutionary-learning-of-interpretable))

(Image Credit: [OpenAI Gym](https://gym.openai.com/))
Task: Multi-Task Learning
Categories: Methodology

Multi-task learning aims to learn multiple different tasks simultaneously while maximizing
performance on one or all of the tasks.

( Image credit: [Cross-stitch Networks for Multi-task Learning](https://arxiv.org/pdf/1604.03539v1.pdf) )
Task: Multi-target Domain Adaptation
Categories: Computer Vision

The idea of Multi-target Domain Adaptation is to adapt a model from a single labelled source domain to multiple unlabelled target domains.
Task: Response Generation
Categories: Natural Language Processing

A task where an agent should play the $DE$ role and generate a text to respond to a $P$ message.
Task: 3D Shape Representation
Categories: Computer Vision

Image: [MeshNet](https://arxiv.org/pdf/1811.11424v1.pdf)
Task: Data Ablation
Categories: Computer Vision

Data Ablation is the study of change in data, and its effects in the performance of Neural Networks.
Task: Audio Source Separation
Categories: Audio

**Audio Source Separation** is the process of separating a mixture (e.g. a pop band recording) into isolated sounds from individual sources (e.g. just the lead vocals).

Source: [Model selection for deep audio source separation via clustering analysis ](https://arxiv.org/abs/1910.12626)
Task: Partially View-aligned Multi-view Learning
Categories: Computer Vision

In multi-view learning, Partially View-aligned Problem (PVP) refers to the case when only a portion of data is aligned, thus leading to data inconsistency.
Task: Numerical Integration
Categories: Miscellaneous

Numerical integration is the task to calculate the numerical value of a definite integral or the numerical solution of differential equations.
Task: Abnormal Event Detection In Video
Categories: Computer Vision

**Abnormal Event Detection In Video** is a challenging task in computer vision, as the definition of what an abnormal event looks like depends very much on the context. For instance, a car driving by on the street is regarded as a normal event, but if the car enters a pedestrian area, this is regarded as an abnormal event. A person running on a sports court (normal event) versus running outside from a bank (abnormal event) is another example. Although what is considered abnormal depends on the context, we can generally agree that abnormal events should be unexpected events that occur less often than familiar (normal) events

Source: [Unmasking the abnormal events in video ](https://arxiv.org/abs/1705.08182)

Image: [Ravanbakhsh et al](https://arxiv.org/pdf/1708.09644v1.pdf)
Task: 3D Absolute Human Pose Estimation
Categories: Knowledge Base
Robots
Computer Vision

This task aims to solve absolute (camera-centric not root-relative) 3D human pose estimation.

( Image credit: [RootNet](https://github.com/mks0601/3DMPPE_ROOTNET_RELEASE) )
Task: imbalanced classification
Categories: Miscellaneous

learning classifier from class-imbalanced data
Task: Hope Speech Detection
Categories: Natural Language Processing

Detecting speech associated with positive, uplifting,
promise, potential, support, reassurance, suggestions, or inspiration.
Task: Trajectory Modeling
Categories: Time Series

The equivalent of language modeling but for trajectories.
Task: Unsupervised Anomaly Detection
Categories: Graphs
Miscellaneous
Computer Vision

The objective of **Unsupervised Anomaly Detection** is to detect previously unseen rare objects or events without any prior knowledge about these. The only information available is that the percentage of anomalies in the dataset is small, usually less than 1%. Since anomalies are rare and unknown to the user at training time, anomaly detection in most cases boils down to the problem of modelling the normal data distribution and defining a measurement in this space in order to classify samples as anomalous or normal. In high-dimensional data such as images, distances in the original space quickly lose descriptive power (curse of dimensionality) and a mapping to some more suitable space is required.

Source: [Unsupervised Learning of Anomaly Detection from Contaminated Image Data using Simultaneous Encoder Training ](https://arxiv.org/abs/1905.11034)
Task: Human fMRI response prediction
Categories: Computer Vision

The task is: Given a) the set of videos of everyday events and b) the corresponding brain responses recorded while human participants viewed those videos, use computational models to predict brain responses for videos.
Task: Sequential Bayesian Inference
Categories: Time Series

Also known as Bayesian filtering or [recursive Bayesian estimation](https://en.wikipedia.org/wiki/Recursive_Bayesian_estimation), this task aims to perform inference on latent state-space models.
Task: Radar Object Detection
Categories: Robots

The radar object detection (ROD) task aims to classify and localize the objects in 3D purely from radar's radio frequency (RF) images.
Task: License Plate Detection
Categories: Computer Vision

License Plate Detection is an image-processing technology used to identify vehicles by their license plates. This technology is used in various security and traffic applications.
Task: Multi-Object Tracking and Segmentation
Categories: Computer Vision

Multiple object tracking and segmentation requires detecting, tracking, and segmenting objects belonging to a set of given classes.

(Image and definition credit: [Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation](https://github.com/SysCV/pcan), NeurIPS 2021, Spotlight )
Task: Drivable Area Detection
Categories: Computer Vision

The drivable area detection is a subset topic of object detection. The model marks the safe and legal roads for regular driving in color blocks shaped by area.
Task: Audio captioning
Categories: Audio

Audio Captioning is the task of describing audio using text. The general approach is to use an audio encoder to encode the audio (example: PANN, CAV-MAE), and to use a decoder (example: transformer) to generate the text.
To judge the quality of audio captions, though machine translation metrics (BLEU, METEOR, ROUGE) and image captioning metrics (SPICE, CIDER) are used, they are not very well-suited. Attempts have been made to use pretrained language model based metrics such as Sentence-BERT.
Task: Blind Face Restoration
Categories: Computer Vision

Blind face restoration aims at recovering high-quality faces from the low-quality counterparts suffering from unknown degradation, such as low-resolution, noise, blur, compression artifacts, etc. When applied to real-world scenarios, it becomes more challenging, due to more complicated degradation, diverse poses and expressions.

Description source: [Towards Real-World Blind Face Restoration with Generative Facial Prior](https://paperswithcode.com/paper/towards-real-world-blind-face-restoration)

Image source: [Towards Real-World Blind Face Restoration with Generative Facial Prior](https://paperswithcode.com/paper/towards-real-world-blind-face-restoration)
Task: Exponential degradation
Categories: Time Series

Exponential degradation used to solve problems where systems exposed to an exponential loss in performances such as reparable industrial systems.
Task: Im2Spec
Categories: Computer Vision

Predicting spectra from images (and vice versa)
Task: De-identification
Categories: Natural Language Processing
Computer Vision

De-identification is the task of detecting privacy-related entities in text, such as person names, emails and contact data.
Task: Emergent communications on relations
Categories: Natural Language Processing

Emergent communications in the context of relations.
Task: Chinese Spell Checking
Categories: Natural Language Processing

Chinese Spell Checking (CSC) aims to detect and correct erroneous characters for user-generated text in Chinese language.
Task: Text-to-Image Generation
Categories: Natural Language Processing
Computer Vision

The development of the brain's blood supply in an embryo involves a complex process with several stages. Initially, there is a network of connections between the carotid and basilar artery systems that usually disappear during development. However, in some cases, these connections remain, leading to variations in the adult cerebral circulation. Around the 24th day of embryonic life (at the 3 mm embryonic stage), the internal carotid arteries (ICA) emerge. They are formed from a combination of the 3rd branchial arch arteries and the distal segments of the paired dorsal aortae. The ICA initially provides all the blood needed by the developing brain. As the brain grows, particularly the occipital region, brainstem, and cerebellum, the blood supply from the ICA becomes insufficient, prompting the development of the posterior circulation. At this stage (4-5 mm embryonic stage), the posterior circulation, specifically the hindbrain, is supplied by two longitudinal neural arteries. These arteries receive blood from several temporary connections (anastomoses) with the ICA, including: Trigeminal artery (TA), Otic artery (OA), Hypoglossal artery (HA), Proatlantal artery (ProA). These carotid-vertebrobasilar anastomoses act as temporary bridges, providing blood flow to the developing posterior circulation until the vertebral arteries (VA) and the basilar artery (BA) are fully formed.6

The BA forms between the 5-8 mm stage by the fusion of the longitudinal neural arteries. As the posterior communicating artery develops and connects with the distal BA, the TA, OA, and HA typically regress. Unlike the TA, OA, and HA, the ProA persists until the VA are fully developed. A segment of the ProA becomes incorporated into the V3 segment of the VA and the distal portions of the occipital artery. The VA develop between the 7-12 mm stage from transverse connections between cervical intersegmental arteries, starting with the ProA and progressing downwards to the 6th intersegmental artery. This 6th intersegmental artery eventually forms the origin of the adult VA from the subclavian artery. 7-8

Persistence of the HA into adulthood, the focus of this discussion, is a rare occurrence. It is the second most common persistent carotid-vertebrobasilar anastomosis, following the persistent trigeminal artery. The failure of the HA to regress during embryological development leads to PPHA. This condition can impact the normal blood flow dynamics in the brain and, in certain cases, can be associated with cerebrovascular issues like aneurysms or ischemic events.
Task: Phrase Ranking
Categories: Natural Language Processing

This task aims to evaluate the “global” rank list of phrases that a method finds from the input corpus.
Task: Keyphrase Extraction
Categories: Natural Language Processing

A classic task to extract salient phrases that best summarize a document, which essentially has two stages: candidate generation and keyphrase ranking.
Task: Phrase Tagging
Categories: Natural Language Processing

A fine-grained task that aims to find all occurrences of phrases in sentences.
Task: inverse tone mapping
Categories: Computer Vision

For stack based inverse tone mapping
Task: eXtreme-Video-Frame-Interpolation
Categories: Computer Vision

Type of Video Frame Interpolation (VFI) that interpolates an intermediate frame on X4K1000FPS dataset containing 4K videos of 1000 fps with the extreme motion. The dataset has a wide variety of textures, extremely large motions, zoomings and occlusions, which have never been seen in the previous VFI benchmark datasets.