3497hyy/evaluation-tables huggingface dataset:
Task: Font Recognition
Categories: Computer Vision

Font recognition (also called *visual font recognition* or *optical font recognition*) is the task of identifying the font family or families used in images containing text. Understanding which fonts are used in text may, for example, help designers find the right style, as well as help select an optical character recognition engine or model that is a better fit for certain texts.
Task: Adversarial Robustness
Categories: Adversarial

Adversarial Robustness evaluates the vulnerabilities of machine learning models under various types of adversarial attacks.
Task: JPEG Decompression
Categories: Computer Vision

Image credit: [Palette: Image-to-Image Diffusion Models](https://paperswithcode.com/paper/palette-image-to-image-diffusion-models)
Task: Spelling Correction
Categories: Natural Language Processing

Spelling correction is the task of detecting and correcting spelling mistakes.
Task: Linear Mode Connectivity
Categories: Knowledge Base

**Linear Mode Connectivity** refers to the relationship between input and output variables in a linear regression model. In a linear regression model, input variables are combined with weights to predict output variables. Understanding the linear model connectivity can help interpret model results and identify which input variables are most important for predicting output variables.
Task: Superpixels
Categories: Computer Vision

Superpixel techniques segment an image into regions based on similarity measures that utilize perceptual features, effectively grouping pixels that appear similar. The motivation behind this approach is to generate regions that provide meaningful descriptions while significantly reducing the data volume compared to using every individual pixel. By decreasing the number of primitives, these techniques reduce redundancy and simplify the complexity of recognition tasks. Superpixels replace the rigid structure of individual pixels with delineated regions that preserve meaningful content in the image, thereby aiding the interpretation of the scene’s structure and simplifying subsequent processing tasks. Generally, superpixel techniques rely on measures that evaluate color similarities and the shapes of regions, incorporating edges or significant changes in intensity to define these regions.
Task: Scene Segmentation
Categories: Computer Vision

Scene segmentation is the task of splitting a scene into its various object components.

Image adapted from [Temporally coherent 4D reconstruction of complex dynamic scenes](https://paperswithcode.com/paper/temporally-coherent-4d-reconstruction-of2).
Task: Open Intent Discovery
Categories: Natural Language Processing

Open intent discovery aims to leverage limited prior knowledge of known intents to find fine-grained known and open intent-wise clusters.
Task: Simultaneous Localization and Mapping
Categories: Computer Vision

Simultaneous localization and mapping (SLAM) is the task of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it.

( Image credit: [ORB-SLAM2](https://arxiv.org/pdf/1610.06475v2.pdf) )
Task: MRI Reconstruction
Categories: Medical

In its most basic form, MRI reconstruction consists in retrieving a complex-valued image from its under-sampled Fourier coefficients.
Besides, it can be addressed as a encoder-decoder task, in which the normative model in the latent space will only capture the relevant information without noise or corruptions. Then, we decode the latent space in order to have a reconstructed MRI.
Task: Odd One Out
Categories: Reasoning

This task tests to what extent a language model is able to identify the odd word.

Source: [BIG-bench](https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/odd_one_out)
Task: DNN Testing
Categories: Adversarial

Testing the reliability of DNNs.
Task: Variational Monte Carlo
Categories: Miscellaneous

Variational methods for quantum physics
Task: Sleep Staging
Categories: Time Series

Human Sleep Staging into W-R-N or W-R-L-D classes from multiple or single polysomnography signals
Task: Sleep Stage Detection
Categories: Medical

Human Sleep Staging into W-N1-N2-N3-REM classes from multiple or single polysomnography signals
Task: Term Extraction
Categories: Natural Language Processing

Term Extraction, or Automated Term Extraction (ATE), is about extraction domain-specific terms from natural language text.
For example, the sentence “We meta-analyzed mortality using random-effect models” contains the domain-specific single-word terms "meta-analyzed", "mortality" and the multi-word term "random-effect models".
Task: Vietnamese Aspect-Based Sentiment Analysis
Categories: Natural Language Processing

UIT-ViSFD: A Vietnamese Smartphone Feedback Dataset for Aspect-Based Sentiment Analysis

In this paper, we present a process of building a social listening system based on aspect-based sentiment analysis in Vietnamese from creating a dataset to building a real application. Firstly, we create UIT-ViSFD, a Vietnamese Smartphone Feedback Dataset as a new benchmark corpus built based on strict annotation schemes for evaluating aspect-based sentiment analysis, consisting of 11,122 human-annotated comments for mobile e-commerce, which is freely available for research purposes. We also present a proposed approach based on the Bi-LSTM architecture with the fastText word embeddings for the Vietnamese aspect-based sentiment task. Our experiments show that our approach achieves the best performances with the F1-score of 84.48% for the aspect task and 63.06% for the sentiment task, which performs several conventional machine learning and deep learning systems. Last but not least, we build SA2SL, a social listening system based on the best performance model on our dataset, which will inspire more social listening systems in the future. Dataset download: https://www.facebook.com/ViDataset

Paper: Phan, Luong Luc, Phuc Huynh Pham, Kim Thi-Thanh Nguyen, Tham Thi Nguyen, Sieu Khai Huynh, Luan Thanh Nguyen, Tin Van Huynh, and Kiet Van Nguyen. "SA2SL: From Aspect-Based Sentiment Analysis to Social Listening System for Business Intelligence." arXiv preprint arXiv:2105.15079 (2021).
Task: Change Detection
Categories: Computer Vision

**Change Detection** is a computer vision task that involves detecting changes in an image or video sequence over time. The goal is to identify areas in the image or video that have undergone changes, such as appearance changes, object disappearance or appearance, or even changes in the scene's background.

Image credit: ["A TRANSFORMER-BASED SIAMESE NETWORK FOR CHANGE DETECTION"](https://arxiv.org/pdf/2201.01293v1.pdf)
Task: Image Stylization
Categories: Computer Vision

**Image stylization** is a task that involves transforming an input image into a new image that has a different style, while preserving the content of the original image. The goal of image stylization is to create visually appealing images with a specific style or aesthetic, such as impressionism, cubism, or surrealism. It can also be used to make images more visually appealing for specific applications, such as social media or advertising.
Task: Multimodal Association
Categories: Natural Language Processing
Time Series

**Multimodal association** refers to the process of associating multiple modalities or types of data in time series analysis. In time series analysis, multiple modalities or types of data can be collected, such as sensor data, images, audio, and text. Multimodal association aims to integrate these different types of data to improve the understanding and prediction of the time series.

For example, in a smart home application, sensor data from temperature, humidity, and motion sensors can be combined with images from cameras to monitor the activities of residents. By analyzing the multimodal data together, the system can detect anomalies or patterns that may not be visible in individual modalities alone.

Multimodal association can be achieved using various techniques, including deep learning models, statistical models, and graph-based models. These models can be trained on the multimodal data to learn the associations and dependencies between the different types of data.
Task: Infrared image super-resolution
Categories: Computer Vision

Aims at upsampling the IR image and create the high resolution image with help of a low resolution image.
Task: Speech-to-Speech Translation
Categories: Speech

Speech-to-speech translation (S2ST) consists on translating speech from one language to speech in another language. This can be done with a cascade of automatic speech recognition (ASR), text-to-text machine translation (MT), and text-to-speech (TTS) synthesis
sub-systems, which is text-centric. Recently, works on S2ST without relying on intermediate text representation is emerging.
Task: Camera Calibration
Categories: Computer Vision

Camera calibration involves estimating camera parameters(including camera intrinsics and extrinsics) to infer geometric features from captured sequences, which is crucial for computer vision and robotics. Driven by different architectures of the neural network,
the researchers have developed two main paradigms for learning-based camera calibration and its applications. One is Regression-based Calibration，Reconstruction-based Calibration is another.
Task: Neural Radiance Caching
Categories: Computer Vision

Involves the task of predicting photorealistic pixel colors from feature buffers.

Image source: [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://arxiv.org/pdf/2201.05989v1.pdf)
Task: Facial Editing
Categories: Computer Vision

Image source: [Stitch it in Time: GAN-Based Facial Editing of Real Videos](https://arxiv.org/pdf/2201.08361v2.pdf)
Task: Scene Graph Generation
Categories: Computer Vision

A scene graph is a structured representation of an image, where nodes in a scene graph correspond to object bounding boxes with their object categories, and edges correspond to their pairwise relationships between objects. The task of **Scene Graph Generation** is to generate a visually-grounded scene graph that most accurately correlates with an image.

Source: [Scene Graph Generation by Iterative Message Passing ](https://arxiv.org/abs/1701.02426)
Task: Hurricane Forecasting
Categories: Computer Vision

Tropical Cyclone Forecasting using Computer Vision, Deep Learning, and Time-Series methods
Task: Manufacturing simulation
Categories: Knowledge Base

Simulation of manufacturing system for applying AI methods and big data analysis
Task: Classification
Categories: Methodology
Audio
Graphs
Computer Code
Time Series
Reasoning
Natural Language Processing
Adversarial
Miscellaneous
Medical
Computer Vision

**Classification** is the task of categorizing a set of data into predefined classes or groups. The aim of classification is to train a model to correctly predict the class or group of new, unseen data. The model is trained on a labeled dataset where each instance is assigned a class label. The learning algorithm then builds a mapping between the features of the data and the class labels. This mapping is then used to predict the class label of new, unseen data points. The quality of the prediction is usually evaluated using metrics such as accuracy, precision, and recall.
Task: Video Propagation
Categories: Computer Vision

Propagating information in processed frames to unprocessed frames
Task: Conditional Text Generation
Categories: Natural Language Processing

The task of generating text according to some pre-specified conditioning (e.g. topic or sentiment or constraint)
Task: Toponym Resolution
Categories: Natural Language Processing

The goal is to find a mapping from a toponym (a location mention) in the text to a spatial footprint.
Task: ARQMath2
Categories: Natural Language Processing

Answer Retrieval for Questions about Math v2 (2021)
Task: Math Information Retrieval
Categories: Natural Language Processing

Information Retrieval on Math Contents
Task: Lane Detection
Categories: Computer Vision

**Lane Detection** is a computer vision task that involves identifying the boundaries of driving lanes in a video or image of a road scene. The goal is to accurately locate and track the lane markings in real-time, even in challenging conditions such as poor lighting, glare, or complex road layouts.

Lane detection is an important component of advanced driver assistance systems (ADAS) and autonomous vehicles, as it provides information about the road layout and the position of the vehicle within the lane, which is crucial for navigation and safety. The algorithms typically use a combination of computer vision techniques, such as edge detection, color filtering, and Hough transforms, to identify and track the lane markings in a road scene.

( Image credit: [End-to-end Lane Detection
](https://github.com/wvangansbeke/LaneDetection_End2End) )
Task: Fire Detection
Categories: Time Series

Detection of fire using multi-variate time series sensor data.
Task: Highlight Detection
Categories: Computer Vision

https://youtu.be/pJ0auP7dbcY?si=vSiZevfJ57YUKC2q
Task: Spatio-Temporal Video Grounding
Categories: Computer Vision

Spatio-temporal video grounding is a computer vision and natural language processing (NLP) task that involves linking textual descriptions to specific spatio-temporal regions or moments in a video. In other words, it aims to determine which parts of a video correspond to a given textual query or description. This task is essential for various applications, including video summarization, content-based video retrieval, video captioning, and more.
Task: Audio Effects Modeling
Categories: Audio

Modeling of audio effects such as reverberation, compression, distortion, etc.
Task: POS Tagging
Categories: Natural Language Processing

Part of Speech Tagging
Task: Nonparametric Clustering

Clustering when the number of clusters is unknwon
Task: EMG Gesture Recognition
Categories: Medical

Electromyographic Gesture Recognition
Task: Task-Oriented Dialogue Systems
Categories: Natural Language Processing

Achieving a pre-defined task through a dialog.
Task: Deep-Sea Treasure, Image version
Categories: Playing Games

Image state version of the multi-objective reinforcement learning toy environment originally introduced in "Empirical evaluation methods for multiobjective reinforcement learning algorithms" by P. Vamplew et al.
Task: Aspect Extraction
Categories: Natural Language Processing

Aspect extraction is the task of identifying and extracting terms relevant for opinion mining and sentiment analysis, for example terms for product attributes or features.
Task: Human Mesh Recovery
Categories: Computer Vision

Estimate 3D body mesh from images
Task: 3D human pose and shape estimation

Estimate 3D human pose and shape (e.g. SMPL) from images
Task: Mistake Detection
Categories: Computer Vision

Mistakes are natural occurrences in many tasks and an opportunity for an AR assistant to provide help. Identifying such mistakes requires modelling procedural knowledge and retaining long-range sequence information. In its simplest form Mistake Detection aims to classify each coarse action segment into one of the three classes: {“correct”, “mistake”, “correction”}.
Task: Object Categorization
Categories: Computer Vision

Object categorization identifies which label, from a
given set, best corresponds to an image region defined by
an input image and bounding box.
Task: Referring Expression
Categories: Computer Vision

Referring expressions places a bounding box around
the instance corresponding to the provided description and
image.
Task: 3D Semantic Scene Completion
Categories: Computer Vision

This task was introduced in "Semantic Scene Completion from a Single Depth Image" (https://arxiv.org/abs/1611.08974) at CVPR 2017 . The target is to infer the dense 3D voxelized semantic scene from an incompleted 3D input (e.g. point cloud, depth map) and an optional RGB image. A recent summary can be found in the paper "3D Semantic Scene Completion: a Survey" (https://arxiv.org/abs/2103.07466), published at IJCV 2021.
Task: Suggestion mining
Categories: Natural Language Processing

Suggestion mining can be defined as the extraction of suggestions from unstructured text,
Task: 3D Canonicalization
Categories: Computer Vision

3D Canonicalization is the process of estimating a transformation-invariant feature for classification and part segmentation tasks.
Task: Question to Declarative Sentence
Categories: Natural Language Processing

Question Answer to Declarative Sentence (QA2D) is the task of generating declarative statements from question, answer pairs.

See:
Demszky, D., Guu, K., & Liang, P. (2018). Transforming Question Answering Datasets Into Natural Language Inference Datasets. arXiv preprint. arXiv:1809.02922
Task: Skill Generalization
Categories: Robots

Image credit: [A Generalist Agent](https://storage.googleapis.com/deepmind-media/A%20Generalist%20Agent/Generalist%20Agent.pdf)
Task: Unsupervised 3D Point Cloud Linear Evaluation
Categories: Computer Vision

Training a linear classifier(e.g. SVM) on the representations learned in an unsupervised manner on the pretrained(e.g. ShapeNet) dataset.
Task: 3D Point Cloud Linear Classification
Categories: Computer Vision

Training a linear classifier(e.g. SVM) on the embeddings/representations of 3D point clouds. The embeddings/representations are usually trained in an unsupervised manner.
Task: Motion Interpolation

3D human motion sequences interpolation and completion
Task: Geometry-aware processing

Harness manifolds and manifold-aware optimization to process data (e.g. SPD manifold representations using covariance matrix).
Task: Second-order methods

Use second-order statistics to process data.
Task: 3D Scene Reconstruction
Categories: Computer Vision

Creating 3D scene either using conventional SFM pipelines or latest deep learning approaches.
Task: Geographic Question Answering

Geographic Question Answering ( by way of IR, NLP, and/or Knowledge base apply)
Task: PSO-ConvNets Dynamics 1
Categories: Computer Vision

Incorporating distilled Cucker-Smale elements into PSO algorithm using KNN and intertwine training with SGD
Task: PSO-ConvNets Dynamics 2
Categories: Computer Vision

Incorporating distilled Cucker-Smale elements into PSO algorithm using KNN and intertwine training with SGD (Pull back method)
Task: Punctuation Restoration
Categories: Natural Language Processing

Punctuation Restoration
Task: Personality Recognition in Conversation
Categories: Natural Language Processing

Given a speaker's conversation with others, it is required to recognize the speaker's personality traits through the conversation record, which includes two scenarios, (1) $1-1$ conversations: the robot recognizes the personality traits of the speaker through the conversation between them (e.g., psychological counseling), (2) $1-N$ conversations : the robot listens to the speaker's conversations with other $N$ people and then recognizes the speaker's personality traits (e.g., group chatbot, home service robot). Since $1-N$ includes the case of $1-1$, we only discusses PRC in $1-N$ conversations. The task of PRC in $1-N$ conversations can be formulated as:

$Per_i = argmax_{Per'_i}P(Per'_i | C_{i,j}, \cdots, C_{i,N})$

where $Per_i=[Neu, Ext, Ope, Agr, Con]$ is a 5-dimensional vector representing Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness. $C_{i,j}$ is the conversations between $Speaker_i$ and $Speaker_j$ ($1 \leq j \leq N$).
Task: Prompt Engineering
Categories: Natural Language Processing
Computer Vision

**Prompt engineering** is the process of designing and refining the prompts used to generate text from language models, such as GPT-3 or similar models. The goal of prompt engineering is to improve the quality and relevance of the generated text by carefully crafting the prompts to elicit the desired responses from the model.

Prompt engineering involves several steps, including selecting the appropriate model architecture and parameters, designing the prompt format and structure, selecting the appropriate task and training data, and fine-tuning the model using the selected prompt and data.

Prompt engineering is a crucial step in the development of language models, as it can greatly influence the quality and effectiveness of the model's responses. By carefully designing and refining the prompts used to generate text, researchers and developers can improve the accuracy and relevance of the model's output, making it more useful for a wide range of applications, including chatbots, language translation, content creation, and more.
Task: PAC learning

Probably Approximately Correct (PAC) learning analyzes machine learning mathematically using probability bounds.
Task: Active Speaker Localization
Categories: Audio

Active Speaker Localization (ASL) is the process of spatially localizing an active speaker (talker) in an environment using either audio, vision or both.
Task: Medical Image Classification
Categories: Medical

**Medical Image Classification** is a task in medical image analysis that involves classifying medical images, such as X-rays, MRI scans, and CT scans, into different categories based on the type of image or the presence of specific structures or diseases. The goal is to use computer algorithms to automatically identify and classify medical images based on their content, which can help in diagnosis, treatment planning, and disease monitoring.
Task: Referring Image Matting
Categories: Computer Vision

Extracting the meticulous alpha matte of the specific object from the image that can best match the given natural language description, e.g., a keyword or a expression.
Task: Multispectral Object Detection
Categories: Computer Vision

Only using RGB cameras for automatic outdoor scene analysis is challenging when, for example, facing insufficient illumination or adverse weather. To improve the recognition reliability, multispectral systems add additional cameras (e.g. infra-red) and perform object detection from multispectral data. Although multispectral scene analysis with deep learning has be shown to have a great potential, there are still many open research questions and it has not been widely deployed in industrial contexts.
Task: Molecule Captioning
Categories: Medical

Molecular description generation entails the creation of a detailed textual depiction illuminating the structure, properties, biological activity, and applications of a molecule based on its molecular descriptors. It furnishes chemists and biologists with a swift conduit to essential molecular information, thus efficiently guiding their research and experiments.
Task: Affordance Recognition
Categories: Computer Vision

Affordance recognition from Human-Object Interaction
Task: Human-Object Interaction Concept Discovery
Categories: Computer Vision

Discovering the reasonable HOI concepts/categories from known categories and their instances. Actually, it is also a matrix (verb-object matrix) complementation problem.
Task: Unsupervised Semantic Segmentation
Categories: Computer Vision

Models that learn to segment each image (i.e. assign a class to every pixel) without seeing the ground truth labels.

( Image credit: [SegSort: Segmentation by Discriminative Sorting of Segments](http://openaccess.thecvf.com/content_ICCV_2019/papers/Hwang_SegSort_Segmentation_by_Discriminative_Sorting_of_Segments_ICCV_2019_paper.pdf) )
Task: Symbolic Regression
Categories: Knowledge Base
Natural Language Processing

producing a mathematical expression (symbolic expression)
that fits a given tabular data.
Task: Cloze Test
Categories: Natural Language Processing

The cloze task refers to infilling individual words.
Task: Medical waveform analysis
Categories: Methodology
Medical

Information extraction from medical waveforms such as the electrocardiogram (ECG), arterial blood pressure (ABP) central venous pressure (CVP), photoplethysmogram (PPG, Pleth).