J0nasW/paperswithcode huggingface dataset:
Task: 3D Point Cloud Reconstruction
Area: computer-vision

Encoding and reconstruction of 3D point clouds.
Task: 3D Instance Segmentation
Area: computer-vision

Image: [OccuSeg](https://arxiv.org/pdf/2003.06537v3.pdf)
Task: Visual Question Answering (VQA)
Area: computer-vision

**Visual Question Answering (VQA)** is a task in computer vision that involves answering questions about an image. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language.

Image Source: [visualqa.org](https://visualqa.org/)
Task: BBBC021 NSC Accuracy
Area: computer-vision

BBBC021 is a dataset of fully imaged human cells. Cells are treated with one of 113 small molecules at 8 concentrations, and fluorescent images are captured staining for nucleus, actin and microtubules. The phenotypic profiling problem is presented, where the goal is to extract features containing meaningful information about the cellular phenotype exhibited. Each of 103 unique compound concentration treatment is labeled with a mechanism-of-action (MOA). The MOA is predicted for each unique treatment (averaging features over all treatment examples) by matching the MOA of the closest point excluding points of the same compound. The dataset and more information can be found at https://bbbc.broadinstitute.org/BBBC021.
Task: Unsupervised Video Summarization
Area: computer-vision

**Unsupervised video summarization** approaches overcome the need for ground-truth data (whose production requires time-demanding and laborious manual annotation procedures), based on learning mechanisms that require only an adequately large collection of original videos for their training. Specifically, the training is based on heuristic rules, like the sparsity, the representativeness, and the diversity of the utilized input features/characteristics.
Task: Age and Gender Estimation
Area: computer-vision

Age and gender estimation is a dual-task of identifying the age via regression analysis and classification of gender of a person.
Task: Font Style Transfer
Area: computer-vision

**Font style transfer** is the task of converting text written in one font into text written in another font while preserving the meaning of the original text. It is used to change the appearance of text while keeping its content intact.
Task: 3D Dense Shape Correspondence
Area: computer-vision

Finding a meaningful correspondence between two or more shapes is one of the most fundamental shape analysis tasks. The problem can be generally stated as: given input shapes S1,S2,...,SN, find a meaningful relation (or mapping) between their elements. Under different contexts, the problem has also been referred to as registration, alignment, or simply, matching. Shape correspondence is a key algorithmic component in tasks such as 3D scan alignment and space-time reconstruction, as well as an indispensable prerequisite in diverse applications including attribute transfer, shape interpolation, and statistical modeling.
Task: Box-supervised Instance Segmentation
Area: computer-vision

This task aims to achieve instance segmentation with weakly bounding box annotations.
Task: Scene Change Detection
Area: computer-vision

Scene change detection (SCD) refers to the task  of localizing changes and identifying change-categories given two scenes. A scene can be either an RGB (+D) image or a 3D reconstruction (point cloud).  If the scene is an image, SCD is a form of pixel-level prediction because each pixel in the image is classified according to a category. On the other hand, if the scene is point cloud, SCD is a form of point-level prediction because each point in the cloud is classified according to a category. 

Some example benchmarks for this task are VL-CMU-CD, PCD, and CD2014. Recently, more complicated benchmarks such as ChangeSim, HDMap, and Mallscape are released.

Models are usually evaluated with the Mean Intersection-Over-Union (Mean IoU), Pixel Accuracy, or F1 metrics.
Task: Object SLAM
Area: computer-vision

SLAM (Simultaneous Localisation and Mapping) at the level of object
Task: 3D Canonical Hand Pose Estimation
Area: computer-vision

Image: [Lin et al](https://arxiv.org/pdf/2006.01320v1.pdf)
Task: VNLA
Area: computer-vision

Find objects in photorealistic environments by requesting and executing language subgoals.
Task: Pedestrian Attribute Recognition
Area: computer-vision

Pedestrian attribution recognition is the task of recognizing pedestrian features - such as whether they are talking on a phone, whether they have a backpack, and so on.

<span style="color:grey; opacity: 0.6">( Image credit: [HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis](https://arxiv.org/pdf/1709.09930v1.pdf) )</span>
Task: Multi-Human Parsing
Area: computer-vision

Multi-human parsing is the task of parsing multiple humans in crowded scenes.

<span style="color:grey; opacity: 0.6">( Image credit: [Multi-Human Parsing](https://github.com/ZhaoJ9014/Multi-Human-Parsing) )</span>
Task: Talking Head Generation
Area: computer-vision

Talking head generation is the task of generating a talking face from a set of images of a person.

<span style="color:grey; opacity: 0.6">( Image credit: [Few-Shot Adversarial Learning of Realistic Neural Talking Head Models](https://arxiv.org/pdf/1905.08233v2.pdf) )</span>
Task: text-guided-image-editing
Area: computer-vision

Editing images using text prompts.
Task: Optical Character Recognition (OCR)
Area: 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: Text Spotting
Area: computer-vision

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: Temporal Metadata Manipulation Detection
Area: computer-vision

Detecting when the timestamp of an outdoor photograph has been manipulated
Task: Out-of-Distribution Detection
Area: computer-vision

Detect out-of-distribution or anomalous examples.
Task: 3D Semantic Instance Segmentation
Area: computer-vision

Image: [3D-SIS](https://github.com/Sekunde/3D-SIS)
Task: Data Ablation
Area: computer-vision

Data Ablation is the study of change in data, and its effects in the performance of Neural Networks.
Task: Face to Face Translation
Area: computer-vision

Given a video of a person speaking in a source language, generate a video of the same person speaking in a target language.
Task: Video Classification
Area: computer-vision

**Video Classification** is the task of producing a label that is relevant to the video given its frames. A good video level classifier is one that not only provides accurate frame labels, but also best describes the entire video given the features and the annotations of the various frames in the video. For example, a video might contain a tree in some frame, but the label that is central to the video might be something else (e.g., “hiking”). The granularity of the labels that are needed to describe the frames and the video depends on the task. Typical tasks include assigning one or more global labels to the video, and assigning one or more labels for each frame inside the video.


<span class="description-source">Source: [Efficient Large Scale Video Classification ](https://arxiv.org/abs/1505.06250)</span>
Task: Pose Prediction
Area: computer-vision

Pose prediction is to predict future poses given a window of previous poses.
Task: Body Detection
Area: computer-vision

Detection of the persons or the characters defined in the dataset.
Task: Calving Front Delineation In Synthetic Aperture Radar Imagery
Area: computer-vision

Delineating the calving front of a marine-terminating glacier in synthetic aperture radar (SAR) imagery. This can, for example, be done through Semantic Segmentation.
Task: Overlapping Pose Estimation
Area: computer-vision

Pose estimation with overlapping poses.
Task: Interactive Video Object Segmentation
Area: computer-vision

The interactive scenario assumes the user gives iterative refinement inputs to the algorithm, in our case in the form of a scribble, to segment the objects of interest. Methods have to produce a segmentation mask for that object in all the frames of a video sequence taking into account all the user interactions.
Task: Image Manipulation Detection
Area: 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: Few Shot Action Recognition
Area: computer-vision

Few-shot (FS) action recognition is a challenging com-
puter vision problem, where the task is to classify an unlabelled query video into one of the action categories in the support set having limited samples per action class.
Task: Procedure Learning
Area: computer-vision

Given a set of videos of the same task, the goal is to identify the key-steps required to perform the task.
Task: COOS-7 Accuracy
Area: computer-vision

COOS-7 contains 132,209 single-cell images of mouse cells, where the task is to predict protein subcellular localization. Images are spread over 1 training set and 4 testing sets, where each single-cell image contains a protein and nucleus fluorescent channels. COOS-7 provides a classification setting where four test datasets have increasing degrees of covariate shift: some images are random subsets of the training data, while others are from experiments reproduced months later and imaged by different instruments. While most classifiers perform well on test datasets similar to the training dataset, all classifiers failed to generalize their performance to datasets with greater covariate shifts. Read more at https://www.alexluresearch.com/publication/coos/.
Task: Semi-Supervised Human Pose Estimation
Area: computer-vision

Semi-supervised human pose estimation aims to leverage the unlabelled data along with labeled data to improve the model performance.
Task: Training-free 3D Point Cloud Classification
Area: computer-vision

Evaluation on target datasets for 3D Point Cloud Classification without any training
Task: Monocular 3D Object Detection
Area: computer-vision

Monocular 3D Object Detection is the task to draw 3D bounding box around objects in a single 2D RGB image. It is localization task but without any extra information like depth or other sensors or multiple-images.
Task: Cloud Removal
Area: computer-vision

The majority of all optical observations collected via spaceborne satellites are affected by haze or clouds. Consequently, persistent cloud coverage affects the remote sensing practitioner's capabilities of a continuous and seamless monitoring of our planet. **Cloud removal** is the task of reconstructing cloud-covered information while preserving originally cloud-free details.

Image Source: [URL](https://patrickTUM.github.io/cloud_removal/)
Task: Speaker-Specific Lip to Speech Synthesis
Area: computer-vision

How accurately can we infer an individual’s speech style and content from his/her lip movements? [1]

In this task, the model is trained on a specific speaker, or a very limited set of speakers. 

[1] Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis, CVPR 2020.
Task: Infrared image super-resolution
Area: computer-vision

Aims at upsampling the IR image and create the high resolution image with help of a low resolution image.
Task: Image Super-Resolution
Area: computer-vision

**Image Super-Resolution** is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.
Task: Unsupervised Semantic Segmentation with Language-image Pre-training
Area: computer-vision

A segmentation task which does not utilise any human-level supervision for semantic segmentation except for a backbone which is initialised with features pre-trained with image-level labels.
Task: Classifier calibration
Area: computer-vision

Confidence calibration – the problem of predicting probability estimates representative of the true correctness likelihood – is important for classification models in many applications. The two common calibration metrics are Expected Calibration Error (ECE) and Maximum Calibration Error (MCE).
Task: PSO-ConvNets Dynamics 1
Area: computer-vision

Incorporating distilled Cucker-Smale elements into PSO algorithm using KNN and intertwine training with SGD
Task: Trajectory Prediction
Area: 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.


<span class="description-source">Source: [Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs ](https://arxiv.org/abs/1912.01118)</span>
Task: Continuous Object Recognition
Area: computer-vision

Continuous object recognition is the task of performing object recognition on a data stream and learning continuously, trying to mitigate issues such as catastrophic forgetting.

<span style="color:grey; opacity: 0.6">( Image credit: [CORe50 dataset](https://vlomonaco.github.io/core50/) )</span>
Task: 3D Inpainting
Area: computer-vision

**3D Inpainting** is the removal of unwanted objects
from a 3D scene, such that the replaced region is visually
plausible and consistent with its context.
Task: Image-To-Gps Verification
Area: computer-vision

The image-to-GPS verification task asks whether a given image is taken at a claimed GPS location.

<span style="color:grey; opacity: 0.6">( Image credit: [Image-to-GPS Verification Through A Bottom-Up Pattern Matching Network](https://arxiv.org/pdf/1811.07288v1.pdf) )</span>
Task: Hand Detection
Area: computer-vision

As an important subject in the field of computer vision, hand detection plays an important role in many tasks such as human-computer interaction, automatic driving, virtual reality and so on.
Task: Vision-Language Navigation
Area: computer-vision

Vision-language navigation (VLN) is the task of navigating an embodied agent to carry out natural language instructions inside real 3D environments.

<span style="color:grey; opacity: 0.6">( Image credit: [Learning to Navigate Unseen Environments:
Back Translation with Environmental Dropout](https://arxiv.org/pdf/1904.04195v1.pdf) )</span>
Task: Self-Supervised Image Classification
Area: computer-vision

This is the task of image classification using representations learnt with self-supervised learning. Self-supervised methods generally involve a pretext task that is solved to learn a good representation and a loss function to learn with. One example of a loss function is an autoencoder based loss where the goal is reconstruction of an image pixel-by-pixel. A more popular recent example is a contrastive loss, which measure the similarity of sample pairs in a representation space, and where there can be a varying target instead of a fixed target to reconstruct (as in the case of autoencoders).

A common evaluation protocol is to train a linear classifier on top of (frozen) representations learnt by self-supervised methods. The leaderboards for the linear evaluation protocol can be found below. In practice, it is more common to fine-tune features on a downstream task. An alternative evaluation protocol therefore uses semi-supervised learning and finetunes on a % of the labels. The leaderboards for the finetuning protocol can be accessed [here](https://paperswithcode.com/task/semi-supervised-image-classification).

You may want to read some blog posts before reading the papers and checking the leaderboards:

- [Contrastive Self-Supervised Learning](https://ankeshanand.com/blog/2020/01/26/contrative-self-supervised-learning.html) - Ankesh Anand
- [The Illustrated Self-Supervised Learning](https://amitness.com/2020/02/illustrated-self-supervised-learning/) - Amit Chaudhary
- [Self-supervised learning and computer vision](https://www.fast.ai/2020/01/13/self_supervised/) - Jeremy Howard
- [Self-Supervised Representation Learning](https://lilianweng.github.io/lil-log/2019/11/10/self-supervised-learning.html) - Lilian Weng

There is also Yann LeCun's talk at AAAI-20 which you can watch [here](https://vimeo.com/390347111) (35:00+).

<span style="color:grey; opacity: 0.6">( Image credit: [A Simple Framework for Contrastive Learning of Visual Representations](https://arxiv.org/pdf/2002.05709v1.pdf) )</span>
Task: Webpage Object Detection
Area: computer-vision

Detect Web Element for various classes from candidate web elements obtained from DOM tree (No need for Bounding Box Regression)
Task: Contrastive Learning
Area: computer-vision

**Contrastive Learning** is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart.

It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering.

<span style="color:grey; opacity: 0.6">(Image credit: [Schroff et al. 2015](https://arxiv.org/abs/1503.03832))</span>
Task: Action Triplet Detection
Area: computer-vision

Detecting and localizing bounding boxes of tools and anatomies. Then prediction their relationship as action triplet <instrument, verb, target>
Task: Self-Driving Cars
Area: computer-vision

Self-driving cars : the task of making a car that can drive itself without human guidance.

<span style="color:grey; opacity: 0.6">( Image credit: [Learning a Driving Simulator](https://github.com/commaai/research) )</span>
Task: 3D Hand Pose Estimation
Area: computer-vision

Image: [Zimmerman et l](https://arxiv.xsrg/pdf/1705.01389v3.pdf)
Task: Texture Synthesis
Area: computer-vision

The fundamental goal of example-based **Texture Synthesis** is to generate a texture, usually larger than the input, that faithfully captures all the visual characteristics of the exemplar, yet is neither identical to it, nor exhibits obvious unnatural looking artifacts.


<span class="description-source">Source: [Non-Stationary Texture Synthesis by Adversarial Expansion ](https://arxiv.org/abs/1805.04487)</span>
Task: 3D Point Cloud Reinforcement Learning
Area: computer-vision

Reinforcement learning / robot learning from 3D point clouds
Task: Depiction Invariant Object Recognition
Area: computer-vision

Depiction invariant object recognition is the task of recognising objects irrespective of how they are visually depicted (line drawing, realistic shaded drawing, photograph etc.).

<span style="color:grey; opacity: 0.6">( Image credit: [SwiDeN](https://arxiv.org/pdf/1607.08764v1.pdf) )</span>
Task: Pedestrian Detection
Area: computer-vision

Pedestrian detection is the task of detecting pedestrians from a camera.

Further state-of-the-art results (e.g. on the KITTI dataset) can be found at [3D Object Detection](https://paperswithcode.com/task/object-detection).

<span style="color:grey; opacity: 0.6">( Image credit: [High-level Semantic Feature Detection: A New Perspective for Pedestrian Detection](https://github.com/liuwei16/CSP) )</span>
Task: Co-Salient Object Detection
Area: computer-vision

**Co-Salient Object Detection** is a computational problem that aims at highlighting the common and salient foreground regions (or objects) in an image group. Please also refer to the online benchmark: http://dpfan.net/cosod3k/




<span style="color:grey; opacity: 0.6">( Image credit: [Taking a Deeper Look at Co-Salient Object Detection, CVPR2020](https://openaccess.thecvf.com/content_CVPR_2020/papers/Fan_Taking_a_Deeper_Look_at_Co-Salient_Object_Detection_CVPR_2020_paper.pdf) )</span>
Task: Steganalysis
Area: computer-vision

Detect the usage of Steganography
Task: Document Image Classification
Area: computer-vision

Document image classification is the task of classifying documents based on images of their contents.

<span style="color:grey; opacity: 0.6">( Image credit: [Real-Time Document Image Classification using Deep CNN and Extreme Learning Machines](https://arxiv.org/pdf/1711.05862v1.pdf) )</span>
Task: Image Imputation
Area: computer-vision

Image imputation is the task of creating plausible images from low-resolution images or images with missing data.

<span style="color:grey; opacity: 0.6">( Image credit: [NASA](https://www.jpl.nasa.gov/edu/news/2019/4/19/how-scientists-captured-the-first-image-of-a-black-hole/) )</span>
Task: 3D Object Classification
Area: computer-vision

3D Object Classification is the task of predicting the class of a 3D object point cloud.  It is a voxel level prediction where each voxel is classified into a category. The popular benchmark for this task is the ModelNet dataset. The models for this task are usually evaluated with the Classification Accuracy metric.

Image: [Sedaghat et al](https://arxiv.org/pdf/1604.03351v2.pdf)
Task: Interspecies Facial Keypoint Transfer
Area: computer-vision

Find cross-domain semantic correspondence between faces of different species
Task: Panoptic Segmentation
Area: computer-vision

**Panoptic Segmentation** is a computer vision task that combines semantic segmentation and instance segmentation to provide a comprehensive understanding of the scene. The goal of panoptic segmentation is to segment the image into semantically meaningful parts or regions, while also detecting and distinguishing individual instances of objects within those regions.

<span style="color:grey; opacity: 0.6">( Image credit: [Detectron2](https://github.com/facebookresearch/detectron2) )</span>
Task: Seeing Beyond the Visible
Area: computer-vision

The objective of this challenge is to automate the process of estimating the soil parameters, specifically, potassium (KKK), phosphorus pentoxide (P2O5P_2O_5P2​O5​), magnesium (MgMgMg) and pHpHpH, through extracting them from the airborne hyperspectral images captured over agricultural areas in Poland (the exact locations are not revealed). To make the solution applicable in real-life use cases, all the parameters should be estimated as precisely as possible.
Task: 3D Surface Generation
Area: computer-vision

Image: [AtlasNet](https://arxiv.org/pdf/1802.05384v3.pdf)
Task: 3D Room Layouts From A Single RGB Panorama
Area: computer-vision

Image: [Zou et al](https://arxiv.org/pdf/1803.08999v1.pdf)
Task: Heterogeneous Face Recognition
Area: computer-vision

Heterogeneous face recognition is the task of matching face images acquired from different sources (i.e., different sensors or different wavelengths) for identification or verification.

<span style="color:grey; opacity: 0.6">( Image credit: [Pose Agnostic Cross-spectral Hallucination via Disentangling Independent Factors](https://arxiv.org/pdf/1909.04365v1.pdf) )</span>
Task: Mobile Periocular Recognition
Area: computer-vision

Periocular recognition is the task of recognising a person based on their eyes (periocular).

<span style="color:grey; opacity: 0.6">( Image credit: [Heterogeneity Aware Deep Embedding for Mobile Periocular Recognition](https://arxiv.org/pdf/1811.00846v1.pdf) )</span>
Task: Class Incremental Learning
Area: computer-vision

Incremental learning of a sequence of tasks when the task-ID is not available at test time.