J0nasW/paperswithcode huggingface dataset:
Task: Online Multi-Object Tracking
Area: computer-vision

The goal of **Online Multi-Object Tracking** is to estimate the spatio-temporal trajectories of multiple objects in an online video stream (i.e., the video is provided frame-by-frame), which is a fundamental problem for numerous real-time applications, such as video surveillance, autonomous driving, and robot navigation.


<span class="description-source">Source: [A Hybrid Data Association Framework for Robust Online Multi-Object Tracking ](https://arxiv.org/abs/1703.10764)</span>
Task: Novel Class Discovery
Area: computer-vision

The goal of Novel Class Discovery (NCD) is to identify new classes in unlabeled data, by exploiting prior knowledge from known classes. In this specific setup, the data is split in two sets. The first is a labeled set containing known classes and the second is an unlabeled set containing unknown classes that must be discovered.
Task: 3D Object Reconstruction From A Single Image
Area: computer-vision

Image: [Fan et al](https://arxiv.org/pdf/1612.00603v2.pdf)
Task: Referring Video Object Segmentation
Area: computer-vision

Referring video object segmentation aims at segmenting an object in video with language expressions. Unlike the previous video object segmentation, the task exploits a different type of supervision, language expressions, to identify and segment an object referred by the given language expressions in a video.
Task: Object Detection In Indoor Scenes
Area: computer-vision

Object detection in indoor scenes is the task of performing object detection within an indoor environment.


<span style="color:grey; opacity: 0.6">( Image credit: [Faster Bounding Box Annotation for Object Detection in Indoor Scenes](https://arxiv.org/pdf/1807.03142v1.pdf) )</span>
Task: Vehicle Re-Identification
Area: computer-vision

Vehicle re-identification is the task of identifying the same vehicle across multiple cameras.

<span style="color:grey; opacity: 0.6">( Image credit: [A Two-Stream Siamese Neural Network for Vehicle Re-Identification by Using Non-Overlapping Cameras](https://github.com/icarofua/siamese-two-stream) )</span>
Task: Motion Segmentation
Area: computer-vision

**Motion Segmentation** is an essential task in many applications in Computer Vision and Robotics, such as surveillance, action recognition and scene understanding. The classic way to state the problem is the following: given a set of feature points that are tracked through a sequence of images, the goal is to cluster those trajectories according to the different motions they belong to. It is assumed that the scene contains multiple objects that are moving rigidly and independently in 3D-space.


<span class="description-source">Source: [Robust Motion Segmentation from Pairwise Matches ](https://arxiv.org/abs/1905.09043)</span>
Task: Drone-view target localization
Area: computer-vision

(Drone -> Satellite) Given one drone-view image or video, the task aims to find the most similar satellite-view image to localize the target building in the satellite view.
Task: hand-object pose
Area: computer-vision

6D pose estimation of hand and object
Task: Object Categorization
Area: 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: Object Detection
Area: computer-vision

**Object Detection** is a computer vision task in which the goal is to detect and locate objects of interest in an image or video. The task involves identifying the position and boundaries of objects in an image, and classifying the objects into different categories. 

The state-of-the-art methods can be categorized into two main types: one-stage methods and two stage-methods: 

- One-stage methods prioritize inference speed, and example models include YOLO, SSD and RetinaNet. 

- Two-stage methods prioritize detection accuracy, and example models include Faster R-CNN, Mask R-CNN and Cascade R-CNN.

The most popular benchmark is the MSCOCO dataset. Models are typically evaluated according to a Mean Average Precision metric.

<span style="color:grey; opacity: 0.6">( Image credit: [Detectron](https://github.com/facebookresearch/detectron) )</span>
Task: Texture Classification
Area: computer-vision

**Texture Classification** is a fundamental issue in computer vision and image processing, playing a significant role in many applications such as medical image analysis, remote sensing, object recognition, document analysis, environment modeling, content-based image retrieval and many more.


<span class="description-source">Source: [Improving Texture Categorization with Biologically Inspired Filtering ](https://arxiv.org/abs/1312.0072)</span>
Task: Age Estimation
Area: computer-vision

Age Estimation is the task of estimating the age of a person from an image some other kind of data.

<span style="color:grey; opacity: 0.6">( Image credit: [BridgeNet](https://arxiv.org/pdf/1904.03358v1.pdf) )</span>
Task: Visual Localization
Area: computer-vision

**Visual Localization** is the problem of estimating the camera pose of a given image relative to a visual representation of a known scene.


<span class="description-source">Source: [Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization ](https://arxiv.org/abs/1908.06387)</span>
Task: Stereo-LiDAR Fusion
Area: computer-vision

Depth estimation using stereo cameras and a LiDAR sensor.
Task: Video Inpainting
Area: 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.


<span class="description-source">Source: [Deep Flow-Guided Video Inpainting ](https://arxiv.org/abs/1905.02884)</span>
Task: Depth Map Super-Resolution
Area: computer-vision

Depth map super-resolution is the task of upsampling depth images.

<span style="color:grey; opacity: 0.6">( Image credit: [A Joint Intensity and Depth Co-Sparse Analysis Model
for Depth Map Super-Resolution](https://arxiv.org/pdf/1304.5319v1.pdf) )</span>
Task: Face Parsing
Area: computer-vision

Classify pixels of a face image into different classes based on a given bounding box.
Task: Image Augmentation
Area: computer-vision

**Image Augmentation** is a data augmentation method that generates more training data from the existing training samples. Image Augmentation is especially useful in domains where training data is limited or expensive to obtain like in biomedical applications.

<span class="description-source">Source: [Improved Image Augmentation for Convolutional Neural Networks by Copyout and CopyPairing ](https://arxiv.org/abs/1909.00390)</span>

( Image credit: [Kornia](https://github.com/kornia/kornia) )
Task: Video Temporal Consistency
Area: computer-vision

A method that remove temporal flickering and other artifacts from videos, in particular those introduced by (non-temporal-aware) per-frame processing
Task: Audio-Visual Active Speaker Detection
Area: computer-vision

Determine if and when each visible person in the video is speaking.
Task: Caricature
Area: computer-vision

**Caricature** is a pictorial representation or description that deliberately exaggerates a person’s distinctive features or peculiarities to create an easily identifiable visual likeness with a comic effect. This vivid art form contains the concepts of abstraction, simplification and exaggeration.


<span class="description-source">Source: [Alive Caricature from 2D to 3D ](https://arxiv.org/abs/1803.06802)</span>
Task: Edge Detection
Area: computer-vision

**Edge Detection** is a fundamental image processing technique which involves computing an image gradient to quantify the magnitude and direction of edges in an image. Image gradients are used in various downstream tasks in computer vision such as line detection, feature detection, and image classification.


<span class="description-source">Source: [Artistic Enhancement and Style Transfer of Image Edges using Directional Pseudo-coloring ](https://arxiv.org/abs/1906.07981)</span>

( Image credit: [Kornia](https://github.com/kornia/kornia) )
Task: 3D Place Recognition
Area: computer-vision

Pointcloud-based place recognition and retrieval
Task: Human Interaction Recognition
Area: computer-vision

Human Interaction Recognition (HIR) is a field of study that involves the development of computer algorithms to detect and recognize human interactions in videos, images, or other multimedia content. The goal of HIR is to automatically identify and analyze the social interactions between people, their body language, and facial expressions.
Task: Autonomous Vehicles
Area: 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).

<span style="color:grey; opacity: 0.6">( Image credit: [GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision](https://arxiv.org/abs/2007.13124) )</span>
Task: 4D Spatio Temporal Semantic Segmentation
Area: computer-vision

Image: [Choy et al](https://paperswithcode.com/paper/4d-spatio-temporal-convnets-minkowski)
Task: Chart Question Answering
Area: computer-vision

Question Answering task on charts images
Task: Face Hallucination
Area: computer-vision

Face hallucination is the task of generating high-resolution (HR) facial images from low-resolution (LR) inputs.

<span style="color:grey; opacity: 0.6">( Image credit: [Deep CNN Denoiser and Multi-layer Neighbor Component Embedding for Face Hallucination](https://arxiv.org/pdf/1806.10726v1.pdf) )</span>
Task: Hyperspectral Unmixing
Area: computer-vision

**Hyperspectral Unmixing** is a procedure that decomposes the measured pixel spectrum of hyperspectral data into a collection of constituent spectral signatures (or endmembers) and a set of corresponding fractional abundances. Hyperspectral Unmixing techniques have been widely used for a variety of applications, such as mineral mapping and land-cover change detection.


<span class="description-source">Source: [An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing ](https://arxiv.org/abs/1810.12000)</span>
Task: Visual Reasoning
Area: computer-vision

Ability to understand  actions and reasoning  associated with any  visual images
Task: Action Recognition
Area: computer-vision

**Action Recognition** is a computer vision task that involves recognizing human actions in videos or images. The goal is to classify and categorize the actions being performed in the video or image into a predefined set of action classes.

In the video domain, it is an open question whether training an action classification network on a sufficiently large dataset, will give a similar boost in performance when applied to a different temporal task or dataset. The challenges of building video datasets has meant that most popular benchmarks for action recognition are small, having on the order of 10k videos. 

Please note some benchmarks may be located in the [Action Classification](https://paperswithcode.com/task/action-classification) or [Video Classification](https://paperswithcode.com/task/video-classification) tasks, e.g. Kinetics-400.
Task: Intubation Support Prediction
Area: computer-vision

Prediction of need for Intubation support of Covid-19 patients.
Task: Real-Time Object Detection
Area: computer-vision

**Real-Time Object Detection** is a computer vision task that involves identifying and locating objects of interest in real-time video sequences with fast inference while maintaining a base level of accuracy.

This is typically solved using algorithms that combine object detection and tracking techniques to accurately detect and track objects in real-time. They use a combination of feature extraction, object proposal generation, and classification to detect and localize objects of interest.

<span style="color:grey; opacity: 0.6">( Image credit: [CenterNet](https://github.com/xingyizhou/CenterNet) )</span>
Task: Smile Recognition
Area: computer-vision

Smile recognition is the task of recognising a smiling face in a photo or video.
Task: Unsupervised Semantic Segmentation
Area: computer-vision

Models that learn to segment each image (i.e. assign a class to every pixel) without seeing the ground truth labels.

<span style="color:grey; opacity: 0.6">( 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) )</span>
Task: Blended-target Domain Adaptation
Area: computer-vision

Blended-target domain adaptation is to adapt a single source model to multiple different target domains. The task is similar to the multi-target domain adaptation. However, the domain labels are not available.
Task: Video Understanding
Area: 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.


<span class="description-source">Source: [Action Detection from a Robot-Car Perspective ](https://arxiv.org/abs/1807.11332)</span>
Task: 3D Single Object Tracking
Area: computer-vision

3D tracking of a single object, based on an initial 3D bounding box, provided to the tracker. 3D single object tracking is commonly performed using point cloud data from Lidars, as it provides valuable depth information, which is lost in camera images.
However, irregular point cloud structure and an increasing point sparsity with distance makes Lidar-based 3D single object tracking a nontrivial task.
Task: Handwritten Line Segmentation
Area: computer-vision

Handwritten Line Segmentation from Document Images.
Task: Conditional Image Generation
Area: computer-vision

Conditional image generation is the task of generating new images from a dataset conditional on their class.

<span style="color:grey; opacity: 0.6">( Image credit: [PixelCNN++](https://github.com/openai/pixel-cnn) )</span>
Task: Virtual Try-on
Area: computer-vision

Virtual try-on of clothing or other items such as glasses and makeup. Most recent techniques use Generative Adversarial Networks.
Task: 3D Geometry Perception
Area: computer-vision

Image: [Zhao et al](https://arxiv.org/pdf/1812.10775v2.pdf)
Task: Video Domain Adapation
Area: computer-vision

Unsupervised Domain Adaptation on Videos for the task of Action Recognition.
Task: Camouflaged Object Segmentation
Area: computer-vision

Camouflaged object segmentation (COS) or Camouflaged object detection (COD), which was originally promoted by [T.-N. Le et al.](https://www.sciencedirect.com/science/article/abs/pii/S1077314219300608) (2017), aims to identify objects that conceal their texture into the surrounding environment. The high intrinsic similarities between the target object and the background make COS/COD far more challenging than the traditional object segmentation task. Also, refer to the online benchmarks on [CAMO dataset](https://sites.google.com/view/ltnghia/research/camo), [COD dataset](http://dpfan.net/Camouflage/), and [online demo](http://mc.nankai.edu.cn/cod).


<span style="color:grey; opacity: 0.6">( Image source: [Anabranch Network for Camouflaged Object Segmentation](https://www.sciencedirect.com/science/article/abs/pii/S1077314219300608) )</span>
Task: Unseen Object Instance Segmentation
Area: computer-vision

Instance segmentation is the task of detecting and delineating each distinct object of interest appearing in an image.

Image Credit: [Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers](https://arxiv.org/abs/2103.12340)
Task: 3D dense captioning
Area: computer-vision

Dense captioning in 3D point clouds is an emerging vision-and-language task involving object-level 3D scene understanding. Apart from coarse semantic class prediction and bounding box regression as in traditional 3D object detection, 3D dense captioning aims at producing a further and finer instance-level label of natural language description on visual appearance and spatial relations for each scene object of interest.
Task: Facial Editing
Area: computer-vision

Image source: [Stitch it in Time: GAN-Based Facial Editing of Real Videos](https://arxiv.org/pdf/2201.08361v2.pdf)
Task: Micro-Expression Spotting
Area: computer-vision

Facial Micro-Expression Spotting is a challenging task in identifying onset, apex and/or offset over a short or long micro-expression sequence.
Task: Motion Disentanglement
Area: computer-vision

Disentangling irregular (anomalous) motion from regular motion.
Task: Semantic correspondence
Area: computer-vision

The task of semantic correspondence aims to establish reliable visual correspondence between different instances of the same object category.
Task: Zero-Shot Composed Image Retrieval (ZS-CIR)
Area: computer-vision

Given a query composed of a reference image and a relative caption, Composed Image Retrieval (CIR) aims to retrieve target images that are visually similar to the reference one but incorporate the changes specified in the relative caption. The bi-modality of the query provides users with more precise control over the characteristics of the desired image, as some features are more easily described with language, while others can be better expressed visually. 

 **Zero-Shot Composed Image Retrieval (ZS-CIR)** is a subtask of CIR that aims to design an approach that manages to combine the reference image and the relative caption without the need for supervised learning.
Task: Monocular 3D Human Pose Estimation
Area: computer-vision

This task targets at 3D human pose estimation with a single RGB camera.
Task: Multimodal Patch Matching
Area: computer-vision

Multimodal patch matching focuses on matching patches originating from different sources, such as visible RGB and near-infrared.
Task: Video-to-image Affordance Grounding
Area: computer-vision

Given a demonstration video V and a target image I, the goal of video-to-image affordance grounding predict an affordance heatmap over the target image according to the hand-interacted region in the video, accompanied by the affordance action (e.g., press, turn).
Task: Face Image Retrieval
Area: computer-vision

Face image retrieval is the task of retrieving faces similar to a query, according to the given
criteria (e.g. identity) and rank them using their distances to the query.

<span style="color:grey; opacity: 0.6">( Image credit: [CP-mtML](http://openaccess.thecvf.com/content_cvpr_2016/papers/Bhattarai_CP-mtML_Coupled_Projection_CVPR_2016_paper.pdf) )</span>
Task: JPEG Artifact Correction
Area: computer-vision

Correction of visual artifacts caused by JPEG compression, these artifacts are usually grouped into three types: blocking, blurring, and ringing. They are caused by quantization and removal of high frequency DCT coefficients.
Task: 3D Part Segmentation
Area: computer-vision

Segmenting 3D object parts

<span style="color:grey; opacity: 0.6">( Image credit: [MeshCNN: A Network with an Edge](https://arxiv.org/pdf/1809.05910v2.pdf) )</span>
Task: Image-to-Image Translation
Area: 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.

<span style="color:grey; opacity: 0.6">( Image credit: [Unpaired Image-to-Image Translation
using Cycle-Consistent Adversarial Networks](https://arxiv.org/pdf/1703.10593v6.pdf) )</span>
Task: Imputation
Area: computer-vision

Substituting missing data with values according to some criteria.
Task: Human Pose Forecasting
Area: computer-vision

Human pose forecasting is the task of detecting and predicting future human poses.

<span style="color:grey; opacity: 0.6">( Image credit: [EgoPose](https://github.com/Khrylx/EgoPose) )</span>
Task: Corpus Video Moment Retrieval
Area: computer-vision

The task extends the Single Video Moment Retrieval task to the Corpus setup where a single textual query is used to temporally localize relevant moments across all videos in the dataset.
Task: Amodal Layout Estimation
Area: computer-vision

Amodal scene layout estimation involves estimating the static and dynamic portion of an urban driving scene in bird's-eye view, given a single image. The concept of "amodal" estimation refers to the fact that we also estimate layout of parts of the scene that are not observable in the image.
Task: 2D Human Pose Estimation
Area: computer-vision

What is Human Pose Estimation?
Human pose estimation is the process of estimating the configuration of the body (pose) from a single, typically monocular, image. Background. Human pose estimation is one of the key problems in computer vision that has been studied for well over 15 years.  The reason for its importance is the
abundance of applications that can benefit from such a technology. For example,
human pose estimation allows for higher-level reasoning in the context of human-computer interaction and activity recognition; it is also one of the basic building blocks for marker-less motion capture (MoCap) technology. MoCap technology is useful for applications ranging from character animation to clinical analysis of gait pathologies.
Task: Animal Pose Estimation
Area: computer-vision

Animal pose estimation is the task of identifying the pose of an animal.

<span style="color:grey; opacity: 0.6">( Image credit: [Using DeepLabCut for 3D markerless pose estimation across species and behaviors](http://www.mousemotorlab.org/s/NathMathis2019.pdf) )</span>
Task: 3D Reconstruction
Area: 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: Image Steganography
Area: computer-vision

**Image Steganography** is the main content of information hiding. The sender conceal a secret message into a cover image, then get the container image called stego, and finish the secret message’s transmission on the public channel by transferring the stego image. Then the receiver part of the transmission can reveal the secret message out. Steganalysis is an attack to the steganography algorithm. The listener on the public channel intercept the image and analyze whether the image contains secret information.


<span class="description-source">Source: [Invisible Steganography via Generative Adversarial Networks ](https://arxiv.org/abs/1807.08571)</span>
Task: License Plate Detection
Area: computer-vision

License Plate Recognition is an image-processing technology used to identify vehicles by their license plates. This technology is used in various security and traffic applications.
Task: PSO-ConvNets Dynamics 2
Area: computer-vision

Incorporating distilled Cucker-Smale elements into PSO algorithm using KNN and intertwine training with SGD (Pull back method)