3497hyy/evaluation-tables huggingface dataset:
Task: Morphological Tagging
Categories: Natural Language Processing

Morphological tagging is the task of assigning labels to a sequence of tokens that describe them morphologically. As compared to Part-of-speech tagging, morphological tagging also considers morphological features, such as case, gender or the tense of verbs.
Task: Video Semantic Segmentation
Categories: Computer Vision

The goal of video semantic segmentation is to assign a predefined class to each pixel in all frames of a video. This requires the model not only to predict accurate segmentation masks but also to ensure that these masks remain temporally consistent across frames. This task has broad applications in areas such as autonomous driving, medical video analysis, and AR/VR.
Task: AutoML
Categories: Methodology

Automated Machine Learning (**AutoML**) is a general concept which covers diverse techniques for automated model learning including automatic data preprocessing, architecture search, and model selection.
Source: Evaluating recommender systems for AI-driven data science (1905.09205)

Source: [CHOPT : Automated Hyperparameter Optimization Framework for Cloud-Based Machine Learning Platforms ](https://arxiv.org/abs/1810.03527)
Task: Safe Exploration
Categories: Robots

**Safe Exploration** is an approach to collect ground truth data by safely interacting with the environment.

Source: [Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems ](https://arxiv.org/abs/2005.04374)
Task: Automatic Post-Editing
Categories: Computer Vision

Automatic post-editing (APE) is used to correct errors in the translation made by the machine translation systems.
Task: Memex Question Answering
Categories: Natural Language Processing

Question answering with real-world multi-modal personal collections, e.g., photo albums with visual, text, time and location information.
Task: Video Inpainting
Categories: Computer Vision

The goal of **Video Inpainting** is to fill in missing regions of a given video sequence with contents that are both spatially and temporally coherent. Video Inpainting, also known as video completion, has many real-world applications such as undesired object removal and video restoration.

Source: [Deep Flow-Guided Video Inpainting ](https://arxiv.org/abs/1905.02884)
Task: Game of Chess
Categories: Playing Games

Chess is a two-player strategy board game played on a chessboard, a checkered gameboard with 64 squares arranged in an 8×8 grid. The idea of making a machine that could beat a Grandmaster human player was a fascination in the artificial community for decades. Famously IBM's DeepBlue beat Kasparov in the 1990s. More recently more human-like approaches such as AlphaZero have appeared.
Task: Game of Go
Categories: Playing Games

Go is an abstract strategy board game for two players, in which the aim is to surround more territory than the opponent. The task is to train an agent to play the game and be superior to other players.
Task: Autonomous Navigation
Categories: Computer Vision
Robots
Reasoning

Autonomous navigation is the task of autonomously navigating a vehicle or robot to or around a location without human guidance.

( Image credit: [Approximate LSTMs for Time-Constrained Inference:
Enabling Fast Reaction in Self-Driving Cars](https://arxiv.org/pdf/1905.00689v2.pdf) )
Task: Multi-agent Reinforcement Learning
Categories: Methodology
Playing Games

The target of **Multi-agent Reinforcement Learning** is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.

Source: [Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports ](https://arxiv.org/abs/2004.12274)
Task: Point Cloud Super Resolution
Categories: Computer Vision

Point cloud super-resolution is a fundamental problem
for 3D reconstruction and 3D data understanding. It takes
a low-resolution (LR) point cloud as input and generates
a high-resolution (HR) point cloud with rich details
Task: Natural Language Transduction
Categories: Natural Language Processing
Computer Vision

Converting one sequence into another
Task: Graph-to-Sequence
Categories: Natural Language Processing

Mapping an input graph to a sequence of vectors.
Task: FPS Games
Categories: Playing Games

First-person shooter (FPS) games Involve like call of duty so enjoy

( Image credit: [Procedural Urban Environments for FPS Games](https://arxiv.org/pdf/1604.05791v1.pdf) )
Task: Face Reenactment
Categories: Computer Vision

**Face Reenactment** is an emerging conditional face synthesis task that aims at fulfilling two goals simultaneously: 1) transfer a source face shape to a target face; while 2) preserve the appearance and the identity of the target face.

Source: [One-shot Face Reenactment ](https://arxiv.org/abs/1908.03251)
Task: Passage Re-Ranking
Categories: Natural Language Processing

Passage re-ranking is the task of scoring and re-ranking a collection of retrieved documents based on an input query.
Task: Language Acquisition
Categories: Natural Language Processing

Language acquisition refers to tasks related to the learning of a second language.
Task: Depth Completion
Categories: Computer Vision

The **Depth Completion** task is a sub-problem of depth estimation. In the sparse-to-dense depth completion problem, one wants to infer the dense depth map of a 3-D scene given an RGB image and its corresponding sparse reconstruction in the form of a sparse depth map obtained either from computational methods such as SfM (Strcuture-from-Motion) or active sensors such as lidar or structured light sensors.

Source: [LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Imagery ](https://arxiv.org/abs/1905.02744),
[Unsupervised Depth Completion from Visual Inertial Odometry](https://arxiv.org/abs/1905.08616)
Task: Ancient Text Restoration
Categories: Miscellaneous

Image credit: [Restoring and attributing ancient texts using deep neural networks
](https://paperswithcode.com/paper/restoring-and-attributing-ancient-texts-using)
Task: Card Games
Categories: Playing Games

Card games involve playing cards: the task is to train an agent to play the game with specified rules and beat other players.
Task: Real-Time Strategy Games
Categories: Playing Games

Real-Time Strategy (RTS) tasks involve training an agent to play video games with continuous gameplay and high-level macro-strategic goals such as map control, economic superiority and more.

( Image credit: [Multi-platform Version of StarCraft: Brood War in a Docker Container](https://github.com/Games-and-Simulations/sc-docker) )
Task: Medical Report Generation
Categories: Methodology
Medical

Medical report generation (MRG) is a task which focus on training AI to automatically generate professional report according the input image data. This can help clinicians make faster and more accurate decision since the task itself is both time consuming and error prone even for experienced doctors.

Aggfgg

Deep neural network and transformer based architecture are currently the most popular methods for this certain task, however, when we try to transfer out pre-trained model into this certain domain, their performance always degrade.

The following are some of the reasons why RSG is hard for pre-trained models:

* Language datasets in a particular domain can sometimes be quite different from the large number of datasets available on the Internet
* During the fine-tuning phase, datasets in the medical field are often unevenly distributed

More recently, multi-modal learning and contrastive learning have shown some inspiring results in this field, but it's still challenging and requires further attention.

Here are some additional readings to go deeper on the task:

* On the Automatic Generation of Medical Imaging Reports

[ https://doi.org/10.48550/arXiv.1711.08195](https://doi.org/10.48550/arXiv.1711.08195)

* A scoping review of transfer learning research on medical image analysis using ImageNet

[ https://arxiv.org/abs/2004.13175](https://arxiv.org/abs/2004.13175)

* A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis

[ https://arxiv.org/abs/2004.12150]( https://arxiv.org/abs/2004.12150)

(Image credit : Transformers in Medical Imaging: A Survey)
Task: Aspect-Based Sentiment Analysis (ABSA)
Categories: Natural Language Processing

**Aspect-Based Sentiment Analysis (ABSA)** is a Natural Language Processing task that aims to identify and extract the sentiment of specific aspects or components of a product or service. ABSA typically involves a multi-step process that begins with identifying the aspects or features of the product or service that are being discussed in the text. This is followed by sentiment analysis, where the sentiment polarity (positive, negative, or neutral) is assigned to each aspect based on the context of the sentence or document. Finally, the results are aggregated to provide an overall sentiment for each aspect.

And recent works propose more challenging ABSA tasks to predict sentiment triplets or quadruplets (Chen et al., 2022), the most influential of which are ASTE (Peng et al., 2020; Zhai et al., 2022), TASD (Wan et al., 2020), ASQP (Zhang et al., 2021a) and ACOS with an emphasis on the implicit aspects or opinions (Cai et al., 2020a).

( Source: [MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction](https://arxiv.org/abs/2305.12627) )
Task: Stereo Matching
Categories: Computer Vision

**Stereo Matching** is one of the core technologies in computer vision, which recovers 3D structures of real world from 2D images. It has been widely used in areas such as autonomous driving, augmented reality and robotics navigation. Given a pair of rectified stereo images, the goal of Stereo Matching is to compute the disparity for each pixel in the reference image, where disparity is defined as the horizontal displacement between a pair of corresponding pixels in the left and right images.

Source: [Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching ](https://arxiv.org/abs/1909.03751)
Task: DeepFake Detection
Categories: Audio
Speech
Miscellaneous
Computer Vision

**DeepFake Detection** is the task of detecting fake videos or images that have been generated using deep learning techniques. Deepfakes are created by using machine learning algorithms to manipulate or replace parts of an original video or image, such as the face of a person. The goal of deepfake detection is to identify such manipulations and distinguish them from real videos or images.

Description source: [DeepFakes: a New Threat to Face Recognition? Assessment and Detection](https://arxiv.org/pdf/1812.08685.pdf)

Image source: [DeepFakes: a New Threat to Face Recognition? Assessment and Detection](https://paperswithcode.com/paper/deepfakes-a-new-threat-to-face-recognition)
Task: Change Point Detection
Categories: Time Series

**Change Point Detection** is concerned with the accurate detection of abrupt and significant changes in the behavior of a time series.

Change point detection is the task of finding changes in the underlying model of a signal or time series. They are two main methods:

1) Online methods, that aim to detect changes as soon as they occur in a real-time setting

2) Offline methods that retrospectively detect changes when all samples are received.

Source: [Selective review of offline change point detection methods](https://arxiv.org/pdf/1801.00718.pdf)
Task: Distributional Reinforcement Learning
Categories: Methodology

Value distribution is the distribution of the random return received by a reinforcement learning agent. it been used for a specific purpose such as implementing risk-aware behaviour.

We have random return Z whose expectation is the value Q. This random return is also described by a recursive equation, but one of a distributional nature
Task: Fake Image Detection
Categories: Computer Vision

( Image credit: [FaceForensics++](https://github.com/ondyari/FaceForensics) )
Task: Outdoor Positioning
Categories: Miscellaneous

Outdoor Positioning (e.g. GPS)
Task: Humor Detection
Categories: Natural Language Processing

Humor detection is the task of identifying comical or amusing elements.
Task: Entity Embeddings
Categories: Methodology

Entity Embeddings is a technique for applying deep learning to tabular data. It involves representing the categorical data of an information systems entity with multiple dimensions.
Task: Linguistic Acceptability
Categories: Natural Language Processing

Linguistic Acceptability is the task of determining whether a sentence is grammatical or ungrammatical.

Image Source: [Warstadt et al](https://arxiv.org/pdf/1901.03438v4.pdf)
Task: graph partitioning
Categories: Graphs

Graph Partitioning is generally the first step of distributed graph computing tasks. The targets are load-balance and minimizing the communication volume.
Task: Time Series Clustering
Categories: Time Series

**Time Series Clustering** is an unsupervised data mining technique for organizing data points into groups based on their similarity. The objective is to maximize data similarity within clusters and minimize it across clusters. Time-series clustering is often used as a subroutine of other more complex algorithms and is employed as a standard tool in data science for anomaly detection, character recognition, pattern discovery, visualization of time series.

Source: [Comprehensive Process Drift Detection with Visual Analytics ](https://arxiv.org/abs/1907.06386)
Task: Anaphora Resolution
Categories: Natural Language Processing

Resolving what expression a pronoun or a noun phrase refers to.
Task: Network Pruning
Categories: Methodology

**Network Pruning** is a popular approach to reduce a heavy network to obtain a light-weight form by removing redundancy in the heavy network. In this approach, a complex over-parameterized network is first trained, then pruned based on come criterions, and finally fine-tuned to achieve comparable performance with reduced parameters.

Source: [Ensemble Knowledge Distillation for Learning Improved and Efficient Networks ](https://arxiv.org/abs/1909.08097)
Task: Autonomous Driving
Categories: Robots
Miscellaneous
Computer Vision

Autonomous driving is the task of driving a vehicle 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: [Exploring the Limitations of Behavior Cloning for Autonomous Driving](https://arxiv.org/pdf/1904.08980v1.pdf))