J0nasW/paperswithcode huggingface dataset:
Task: Single-shot HDR Reconstruction
Area: computer-vision

SVE-based HDR imaging, also known as single-shot HDR imaging, algorithms capture a scene with pixel-wise varying exposures in a single image and then computationally synthesize an HDR image, which benefits from the multiple exposures of the single image.
Task: Colorization
Area: computer-vision

**Colorization** is the process of adding plausible color information to monochrome photographs or videos. Colorization is a highly undetermined problem, requiring mapping a real-valued luminance image to a three-dimensional color-valued one, that has not a unique solution.


<span class="description-source">Source: [ChromaGAN: An Adversarial Approach for Picture Colorization ](https://arxiv.org/abs/1907.09837)</span>
Task: Point cloud reconstruction
Area: computer-vision

This task aims to solve inherent problems in raw point clouds: sparsity, noise, and irregularity.
Task: Underwater Image Restoration
Area: computer-vision

Underwater image restoration aims to rectify the distorted colors and present the true colors of the underwater scene.
Task: Partially View-aligned Multi-view Learning
Area: 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: Scene Text Recognition
Area: computer-vision

See [Scene Text Detection](https://paperswithcode.com/task/scene-text-detection) for leaderboards in this task.
Task: Demosaicking
Area: computer-vision

Most modern digital cameras acquire color images by measuring only one color channel per pixel, red, green, or blue, according to a specific pattern called the Bayer pattern. **Demosaicking** is the processing step that reconstruct a full color image given these incomplete measurements.


<span class="description-source">Source: [Revisiting Non Local Sparse Models for Image Restoration ](https://arxiv.org/abs/1912.02456)</span>
Task: Face Recognition
Area: computer-vision

**Facial Recognition** is the task of making a positive identification of a face in a photo or video image against a pre-existing database of faces. It begins with detection - distinguishing human faces from other objects in the image - and then works on identification of those detected faces.

The state of the art tables for this task are contained mainly in the consistent parts of the task : the face verification and face identification tasks.

<span style="color:grey; opacity: 0.6">( Image credit: [Face Verification](https://shuftipro.com/face-verification) )</span>
Task: Saliency Prediction
Area: computer-vision

A saliency map is a model that predicts eye fixations on a visual scene.
Task: Pose Tracking
Area: computer-vision

**Pose Tracking** is the task of estimating multi-person human poses in videos and assigning unique instance IDs for each keypoint across frames. Accurate estimation of human keypoint-trajectories is useful for human action recognition, human interaction understanding, motion capture and animation.


<span class="description-source">Source: [LightTrack: A Generic Framework for Online Top-Down Human Pose Tracking ](https://arxiv.org/abs/1905.02822)</span>
Task: Physical Video Anomaly Detection
Area: computer-vision

Detecting if an entire short clip of a physical or mechanical process features an anomalous motion
Task: Image Dehazing
Area: computer-vision

<span style="color:grey; opacity: 0.6">( Image credit: [Densely Connected Pyramid Dehazing Network](https://github.com/hezhangsprinter/DCPDN) )</span>
Task: Action Quality Assessment
Area: computer-vision

Assessing/analyzing/quantifying how well an action was performed.
Task: Referring Image Matting (Keyword-based)
Area: computer-vision

Keyword-based referring image matting, taking an image and a keyword word as the input.
Task: Deblurring
Area: computer-vision

**Deblurring** is a computer vision task that involves removing the blurring artifacts from images or videos to restore the original, sharp content. Blurring can be caused by various factors such as camera shake, fast motion, and out-of-focus objects, and can result in a loss of detail and quality in the captured images. The goal of deblurring is to produce a clear, high-quality image that accurately represents the original scene.

<span style="color:grey; opacity: 0.6">( Image credit: [Deblurring Face Images using Uncertainty Guided Multi-Stream Semantic Networks](https://arxiv.org/pdf/1907.13106v1.pdf) )</span>
Task: Unsupervised Long Term Person Re-Identification
Area: computer-vision

Long-term Person Re-Identification(Clothes-Changing Person Re-ID) is a computer vision task in which the goal is to match a person's identity across different cameras, clothes, and locations in a video or image sequence. It involves detecting and tracking a person and then using features such as appearance, and body shape to match their identity in different frames. The goal is to associate the same person across multiple non-overlapping camera views in a robust and efficient manner.
Task: Skeleton Based Action Recognition
Area: computer-vision

**Skeleton-based Action Recognition** is a computer vision task that involves recognizing human actions from a sequence of 3D skeletal joint data captured from sensors such as Microsoft Kinect, Intel RealSense, and wearable devices. The goal of skeleton-based action recognition is to develop algorithms that can understand and classify human actions from skeleton data, which can be used in various applications such as human-computer interaction, sports analysis, and surveillance.

<span style="color:grey; opacity: 0.6">( Image credit: [View Adaptive Neural Networks for High
Performance Skeleton-based Human Action
Recognition](https://arxiv.org/pdf/1804.07453v3.pdf) )</span>
Task: Pose Estimation
Area: computer-vision

**Pose Estimation** is a computer vision task where the goal is to detect the position and orientation of a person or an object. Usually, this is done by predicting the location of specific keypoints like hands, head, elbows, etc. in case of Human Pose Estimation.

A common benchmark for this task is [MPII Human Pose](https://paperswithcode.com/sota/pose-estimation-on-mpii-human-pose)

<span style="color:grey; opacity: 0.6">( Image credit: [Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose](https://github.com/Daniil-Osokin/lightweight-human-pose-estimation.pytorch) )</span>
Task: Facial Attribute Classification
Area: computer-vision

Facial attribute classification is the task of classifying various attributes of a facial image - e.g. whether someone has a beard, is wearing a hat, and so on.

<span style="color:grey; opacity: 0.6">( Image credit: [Multi-task Learning of Cascaded CNN for Facial Attribute Classification
](https://arxiv.org/pdf/1805.01290v1.pdf) )</span>
Task: Natural Image Orientation Angle Detection
Area: computer-vision

Image orientation angle detection is a pretty challenging task for a machine because the machine has to learn the features of an image in such a way so that it can detect the arbitrary angle by which the image is rotated. Though there are some modern cameras with features involving inertial sensors that can correct image orientation in steps of 90 degrees, those features are seldom used. In this paper, we propose a method to detect the orientation angle of a digitally captured image where the image may have been captured by a camera at a tilted angle (between 0\degree to 359\degree).
Task: Motion Detection
Area: computer-vision

**Motion Detection** is a process to detect the presence of any moving entity in an area of interest. Motion Detection is of great importance due to its application in various areas such as surveillance and security, smart homes, and health monitoring.


<span class="description-source">Source: [Different Approaches for Human Activity Recognition– A Survey ](https://arxiv.org/abs/1906.05074)</span>
Task: Image Matting
Area: computer-vision

**Image Matting** is the process of accurately estimating the foreground object in images and videos. It is a very important technique in image and video editing applications, particularly in film production for creating visual effects. In case of image segmentation, we segment the image into foreground and background by labeling the pixels. Image segmentation generates a binary image, in which a pixel either belongs to foreground or background. However, Image Matting is different from the image segmentation, wherein some pixels may belong to foreground as well as background, such pixels are called partial or mixed pixels. In order to fully separate the foreground from the background in an image, accurate estimation of the alpha values for partial or mixed pixels is necessary.


<span class="description-source">Source: [Automatic Trimap Generation for Image Matting ](https://arxiv.org/abs/1707.00333)</span>

<span class="description-source">Image Source: [Real-Time High-Resolution Background Matting](https://arxiv.org/pdf/2012.07810v1.pdf)</span>
Task: Semi-Supervised Video Object Segmentation
Area: computer-vision

The semi-supervised scenario assumes the user inputs a full mask of the object(s) of interest in the first frame of a video sequence. Methods have to produce the segmentation mask for that object(s) in the subsequent frames.
Task: Scene Segmentation
Area: 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: Object Discovery
Area: computer-vision

**Object Discovery** is the task of identifying previously unseen objects.


<span class="description-source">Source: [Unsupervised Object Discovery and Segmentation of RGBD-images ](https://arxiv.org/abs/1710.06929)</span>
Task: Multimodal Forgery Detection
Area: computer-vision

**Multimodal Forgery Detection** task is a deep forgery detection method which uses both video and audio.
Task: Simultaneous Localization and Mapping
Area: 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.

<span style="color:grey; opacity: 0.6">( Image credit: [ORB-SLAM2](https://arxiv.org/pdf/1610.06475v2.pdf) )</span>
Task: Video Grounding
Area: computer-vision

**Video grounding** is the task of linking spoken language descriptions to specific video segments. In video grounding, the model is given a video and a natural language description, such as a sentence or a caption, and its goal is to identify the specific segment of the video that corresponds to the description. This can involve tasks such as localizing the objects or actions mentioned in the description within the video, or associating a specific time interval with the description.
Task: Point Cloud Registration
Area: computer-vision

**Point Cloud Registration** is a fundamental problem in 3D computer vision and photogrammetry. Given several sets of points in different coordinate systems, the aim of registration is to find the transformation that best aligns all of them into a common coordinate system. Point Cloud Registration plays a significant role in many vision applications such as 3D model reconstruction, cultural heritage management, landslide monitoring and solar energy analysis.


<span class="description-source">Source: [Iterative Global Similarity Points : A robust coarse-to-fine integration solution for pairwise 3D point cloud registration ](https://arxiv.org/abs/1808.03899)</span>
Task: 3D Face Reconstruction
Area: computer-vision

**3D Face Reconstruction** is a computer vision task that involves creating a 3D model of a human face from a 2D image or a set of images. The goal of 3D face reconstruction is to reconstruct a digital 3D representation of a person's face, which can be used for various applications such as animation, virtual reality, and biometric identification.

<span style="color:grey; opacity: 0.6">( Image credit: [3DDFA_V2](https://github.com/cleardusk/3DDFA_V2) )</span>
Task: 3D Car Instance Understanding
Area: computer-vision

3D Car Instance Understanding is the task of estimating properties (e.g.translation, rotation and shape) of a moving or parked vehicle on the road.

<span style="color:grey; opacity: 0.6">( Image credit: [Occlusion-Net](http://openaccess.thecvf.com/content_CVPR_2019/papers/Reddy_Occlusion-Net_2D3D_Occluded_Keypoint_Localization_Using_Graph_Networks_CVPR_2019_paper.pdf) )</span>
Task: Compositional Zero-Shot Learning
Area: computer-vision

**Compositional Zero-Shot Learning (CZSL)** is a computer vision task in which the goal is to recognize unseen compositions fromed from seen state and object during training. The key challenge in CZSL is the inherent entanglement between the state and object within the context of an image. Some example benchmarks for this task are MIT-states, UT-Zappos, and C-GQA. Models are usually evaluated with the Accuracy for both seen and unseen compositions, as well as their Harmonic Mean(HM).

<span style="color:grey; opacity: 0.6">( Image credit: [Heosuab](https://hellopotatoworld.tistory.com/24) )</span>
Task: 3D Semantic Scene Completion
Area: 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: Amodal Panoptic Segmentation
Area: computer-vision

The goal of this task is to simultaneously predict the pixel-wise semantic segmentation labels of the visible regions of stuff classes and the instance segmentation labels of both the visible and occluded regions of thing classes.
Task: 3D Shape Modeling
Area: computer-vision

Image: [Gkioxari et al](https://arxiv.org/pdf/1906.02739v2.pdf)
Task: Visual Grounding
Area: computer-vision

Visual Grounding (VG) aims to locate the most relevant object or region in an image, based on a natural language query. The query can be a phrase, a sentence, or even a multi-round dialogue. There are three main challenges in
VG: 

* What is the main focus in a query? 
* How to understand an image? 
* How to locate an object?
Task: Referring Expression
Area: computer-vision

Referring expressions places a bounding box around
the instance corresponding to the provided description and
image.
Task: Document Layout Analysis
Area: computer-vision

"**Document Layout Analysis** is performed to determine physical structure of a document, that is, to determine document components. These document components can consist of single connected components-regions [...] of
pixels that are adjacent to form single regions [...] , or group
of text lines. A text line is a group of characters, symbols,
and words that are adjacent, “relatively close” to each other
and through which a straight line can be drawn (usually with
horizontal or vertical orientation)."  L. O'Gorman, "The document spectrum for page layout analysis," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1162-1173, Nov. 1993.

Image credit: [PubLayNet: largest dataset ever for document layout analysis](https://arxiv.org/pdf/1908.07836v1.pdf)
Task: Feature Compression
Area: computer-vision

Compress data for machine interpretability to perform downstream tasks, rather than for human perception.
Task: Visual Prompting
Area: computer-vision

Visual Prompting is the task of streamlining computer vision processes by harnessing the power of prompts,
inspired by the breakthroughs of text prompting in NLP. This innovative approach involves using a few visual
prompts to swiftly convert an unlabeled dataset into a deployed model, significantly reducing development time
for both individual projects and enterprise solutions.
Task: Unsupervised Text Recognition
Area: computer-vision

Decompose a text into the letters / tokens that are used to write it.
Task: 3D Pose Estimation
Area: computer-vision

Image credit: [GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision
, ECCV'20](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123600511.pdf)
Task: Video Super-Resolution
Area: computer-vision

**Video Super-Resolution** is a computer vision task that aims to increase the resolution of a video sequence, typically from lower to higher resolutions. The goal is to generate high-resolution video frames from low-resolution input, improving the overall quality of the video.

<span style="color:grey; opacity: 0.6">( Image credit: [Detail-revealing Deep Video Super-Resolution](https://github.com/jiangsutx/SPMC_VideoSR) )</span>
Task: Point Cloud Super Resolution
Area: 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: Image-Variation
Area: computer-vision

Given an image, generate variations of the image
Task: Gaze Target Estimation
Area: computer-vision

Gaze Target Estimation refers to predicting the image 2D gaze location of a person in the image.
Task: Online surgical phase recognition
Area: computer-vision

Online surgical phase recognition: the first 40 videos to train, the last 40 videos to test.
Task: Image Outpainting
Area: computer-vision

Predicting the visual context of an image beyond its boundary.

Image credit: [NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis](https://paperswithcode.com/paper/nuwa-infinity-autoregressive-over?from=n35)
Task: Object Tracking
Area: computer-vision

**Object tracking** is the task of taking an initial set of object detections, creating a unique ID for each of the initial detections, and then tracking each of the objects as they move around frames in a video, maintaining the ID assignment. State-of-the-art methods involve fusing data from RGB and event-based cameras to produce more reliable object tracking. CNN-based models using only RGB images as input are also effective. The most popular benchmark is OTB. There are several evaluation metrics specific to object tracking, including HOTA, MOTA, IDF1, and Track-mAP. 

<span style="color:grey; opacity: 0.6">( Image credit: [Towards-Realtime-MOT
](https://github.com/Zhongdao/Towards-Realtime-MOT) )</span>
Task: 3D Point Cloud Classification
Area: computer-vision

Image: [Qi et al](https://arxiv.org/pdf/1612.00593v2.pdf)
Task: Photo-To-Caricature Translation
Area: computer-vision

Photo-to-caricature translation is the task of adapting a photo to a cartoon or sketch.

<span style="color:grey; opacity: 0.6">( Image credit: [WarpGAN](https://arxiv.org/pdf/1811.10100v3.pdf) )</span>
Task: Camera shot boundary detection
Area: computer-vision

The objective of camera shot boundary detection is to find the transitions between the camera shots in a video and classify the type of camera transition. This task is introduced in SoccerNet-v2, where 3 types of transitions are considered (abrupt, logo, smooth).
Task: 3D Shape Reconstruction From A Single 2D Image
Area: computer-vision

Image: [Liao et al](https://arxiv.org/pdf/1811.12016v1.pdf)
Task: Robust 3D Semantic Segmentation
Area: computer-vision

3D Semantic Segmentation under Out-of-Distribution Scenarios
Task: Handwriting Verification
Area: computer-vision

The goal of handwriting verification is to find a measure of confidence whether the given handwritten samples are written by the same or different writer.
Task: Automatic Post-Editing
Area: computer-vision

Automatic post-editing (APE) is used to correct errors in the translation made by the machine translation systems.
Task: Sensor Fusion
Area: computer-vision

Sensor fusion is the process of combining sensor data or data derived from disparate sources such that the resulting information has less uncertainty than would be possible when these sources were used individually. [Wikipedia]
Task: Road Segmentation
Area: computer-vision

Road Segmentation is a pixel wise binary classification in order to extract underlying road network. Various Heuristic and data driven models are proposed. Continuity and robustness still remains one of the major challenges in the area.
Task: Keypoint Detection
Area: computer-vision

**Keypoint Detection** involves simultaneously detecting people and localizing their keypoints. Keypoints are the same thing as interest points. They are spatial locations, or points in the image that define what is interesting or what stand out in the image. They are invariant to image rotation, shrinkage, translation, distortion, and so on.

<span style="color:grey; opacity: 0.6">( Image credit: [PifPaf: Composite Fields for Human Pose Estimation](https://github.com/vita-epfl/openpifpaf); "Learning to surf" by fotologic, license: CC-BY-2.0 )</span>
Task: JPEG Decompression
Area: computer-vision

Image credit: [Palette: Image-to-Image Diffusion Models](https://paperswithcode.com/paper/palette-image-to-image-diffusion-models)
Task: Boundary Grounding
Area: computer-vision

Provided with a description of a boundary inside a video, the machine is required to locate that boundary in the video.
Task: Grounded Situation Recognition
Area: computer-vision

Grounded Situation Recognition aims to produce the structured image summary which describes the primary activity (verb), its relevant entities (nouns), and their bounding-box groundings.
Task: Sign Language Recognition
Area: computer-vision

**Sign Language Recognition** is a computer vision and natural language processing task that involves automatically recognizing and translating sign language gestures into written or spoken language. The goal of sign language recognition is to develop algorithms that can understand and interpret sign language, enabling people who use sign language as their primary mode of communication to communicate more easily with non-signers.

<span style="color:grey; opacity: 0.6">( Image credit: [Word-level Deep Sign Language Recognition from Video:
A New Large-scale Dataset and Methods Comparison](https://arxiv.org/pdf/1910.11006v1.pdf) )</span>
Task: Concept Alignment
Area: computer-vision

**Concept Alignment** aims to align the learned representations or concepts within a model with the intended or target concepts. It involves adjusting the model's parameters or training process to ensure that the learned concepts accurately reflect the underlying patterns in the data.
Task: Story Continuation
Area: computer-vision

The task involves providing an initial scene that can be obtained in real world use cases. By including this scene, a model can then copy and adapt elements from it as it generates subsequent images.

Source: [StoryDALL-E: Adapting Pretrained Text-to-Image Transformers for Story Continuation](https://paperswithcode.com/paper/storydall-e-adapting-pretrained-text-to-image)
Task: 3D Multi-Person Pose Estimation (absolute)
Area: computer-vision

This task aims to solve absolute 3D multi-person pose Estimation (camera-centric coordinates). No ground truth human bounding box and human root joint coordinates are used during testing stage.

<span style="color:grey; opacity: 0.6">( Image credit: [RootNet](https://github.com/mks0601/3DMPPE_ROOTNET_RELEASE) )</span>
Task: 3D Facial Expression Recognition
Area: computer-vision

3D facial expression recognition is the task of modelling facial expressions in 3D from an image or video.

<span style="color:grey; opacity: 0.6">( Image credit: [Expression-Net](https://github.com/fengju514/Expression-Net) )</span>