J0nasW/paperswithcode huggingface dataset:
Task: Object Recognition
Area: computer-vision

Object recognition is a computer vision technique for detecting + classifying objects in images or videos. Since this is a combined task of object detection plus image classification, the state-of-the-art tables are recorded for each component task [here](https://www.paperswithcode.com/task/object-detection) and  [here](https://www.paperswithcode.com/task/image-classification2).

<span style="color:grey; opacity: 0.6">( Image credit: [Tensorflow Object Detection API
](https://github.com/tensorflow/models/tree/master/research/object_detection) )</span>
Task: Partially Relevant Video Retrieval
Area: computer-vision

In the Partially Relevant Video Retrieval (PRVR) task,  an untrimmed video is considered to be partially relevant w.r.t. a given textual query if it contains a moment relevant to the query. PRVR aims to retrieve such partially relevant videos from a large collection of untrimmed videos.
Task: Face Reenactment
Area: computer-vision

**Face Reenactment** is an emerging conditional face synthesis task that aims at fulfilling two goals simultaneously: 1) transfer a source face shape to a target face; while 2) preserve the appearance and the identity of the target face.


<span class="description-source">Source: [One-shot Face Reenactment ](https://arxiv.org/abs/1908.03251)</span>
Task: Synthetic Image Attribution
Area: computer-vision

Determine the source or origin of a generated image, such as identifying the model or tool used to create it. This information can be useful for detecting copyright infringement or for investigating digital crimes.
Task: Scene Graph Generation
Area: 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.


<span class="description-source">Source: [Scene Graph Generation by Iterative Message Passing ](https://arxiv.org/abs/1701.02426)</span>
Task: Multiple Affordance Detection
Area: computer-vision

Affordance detection is the task of detecting objects that are usable (or graspable) by a human.

<span style="color:grey; opacity: 0.6">( Image credit: [What can I do here? Leveraging Deep 3D saliency and geometry for fast and scalable multiple affordance detection](https://github.com/eduard626/deep-interaction-tensor) )</span>
Task: Handwriting generation
Area: computer-vision

The inverse of handwriting recognition. From text generate and image of handwriting (offline) of trajectory of handwriting (online).
Task: Emotion Recognition
Area: computer-vision

**Emotion Recognition** is an important area of research to enable effective human-computer interaction. Human emotions can be detected using speech signal, facial expressions, body language, and electroencephalography (EEG). <span class="description-source">Source: [Using Deep Autoencoders for Facial Expression Recognition ](https://arxiv.org/abs/1801.08329)</span>
Task: Image Denoising
Area: computer-vision

**Image Denoising** is a computer vision task that involves removing noise from an image. Noise can be introduced into an image during acquisition or processing, and can reduce image quality and make it difficult to interpret. Image denoising techniques aim to restore an image to its original quality by reducing or removing the noise, while preserving the important features of the image.

<span style="color:grey; opacity: 0.6">( Image credit: [Wide Inference Network for Image Denoising via
Learning Pixel-distribution Prior](https://arxiv.org/pdf/1707.05414v5.pdf) )</span>
Task: Video Question Answering
Area: computer-vision

Video Question Answering (VideoQA) aims to answer natural language questions according to the
given videos. Given a video and a question in natural language, the model produces accurate answers according
to the content of the video.
Task: Facial Inpainting
Area: computer-vision

Facial inpainting (or face completion) is the task of generating plausible facial structures for missing pixels in a face image.

<span style="color:grey; opacity: 0.6">( Image credit: [SymmFCNet](https://github.com/csxmli2016/SymmFCNet) )</span>
Task: Vehicle Speed Estimation
Area: computer-vision

Vehicle speed estimation is the task of detecting and tracking vehicles whose real-world speeds are then estimated. The task is usually evaluated with recall and precision of the detected vehicle tracks as well as the mean or median errors of the estimated vehicle speeds.
Task: Laminar-Turbulent Flow Localisation
Area: computer-vision

It is a segmentation task on thermographic measurement images in order to separate laminar and turbulent flow regions on flight body parts.
Task: Neural Radiance Caching
Area: 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 Expression Recognition (FER)
Area: computer-vision

**Facial Expression Recognition (FER)** is a computer vision task aimed at identifying and categorizing emotional expressions depicted on a human face. The goal is to automate the process of determining emotions in real-time, by analyzing the various features of a face such as eyebrows, eyes, mouth, and other features, and mapping them to a set of emotions such as anger, fear, surprise, sadness and happiness.

<span style="color:grey; opacity: 0.6">( Image credit: [DeXpression](https://arxiv.org/pdf/1509.05371v2.pdf) )</span>
Task: Camera shot segmentation
Area: computer-vision

Camera shot temporal segmentation consists in classifying each video frame according to the type of camera used to record said frame. This task is introduced with the SoccerNet-v2 dataset, where 13 camera classes are considered (main camera, behind the goal, corner camera, etc.).
Task: Local Color Enhancement
Area: computer-vision

Enhancement techniques for improving the contrast between lesion and background skin on dermatological macro-images are limited in the literature. To fill this gap, a modified sigmoid transform is applied in the HSV color space. The crossover point in the modified sigmoid transform that divides the macro-image into lesion and background is predicted using a modified EfficientNet regressor to exclude manual intervention and subjectivity.
Task: Video Reconstruction
Area: computer-vision

<span class="description-source">Source: [Deep-SloMo](https://github.com/avinashpaliwal/Deep-SloMo)</span>
Task: VGSI
Area: computer-vision

Given a textual goal and multiple images representing candidate events, a model must choose one image which constitutes a reason- able step towards the given goal. 
A model should correctly recognize not only the specific action illustrated in an image (e.g., “turning on the oven”), but also the intent of the action (“baking fish”).
Task: Video Instance Segmentation
Area: computer-vision

The goal of video instance segmentation is simultaneous detection, segmentation and tracking of instances in videos. In words, it is the first time that the image instance segmentation problem is extended to the video domain.

To facilitate research on this new task, a large-scale benchmark called YouTube-VIS, which consists of 2,883 high-resolution YouTube videos, a 40-category label set and 131k high-quality instance masks is built.
Task: Camera Relocalization
Area: computer-vision

"Camera relocalization, or image-based localization is a fundamental problem in robotics and computer vision. It refers to the process of determining camera pose from the visual scene representation and it is essential for many applications such as navigation of autonomous vehicles, structure from motion (SfM), augmented reality (AR) and simultaneous localization and mapping (SLAM)." ([Source](https://paperswithcode.com/paper/camera-relocalization-by-computing-pairwise))
Task: Denoising
Area: computer-vision

**Denoising** is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.

<span style="color:grey; opacity: 0.6">( Image credit: [Beyond a Gaussian Denoiser](https://arxiv.org/pdf/1608.03981v1.pdf) )</span>
Task: Few-Shot Semantic Segmentation
Area: computer-vision

Few-shot semantic segmentation (FSS) learns to segment target objects in query image given few pixel-wise annotated support image.
Task: 6D Pose Estimation using RGBD
Area: computer-vision

Image: [Zeng et al](https://arxiv.org/pdf/1609.09475v3.pdf)
Task: Image Similarity Search
Area: computer-vision

Image credit: [The 2021 Image Similarity Dataset and Challenge](https://paperswithcode.com/paper/the-2021-image-similarity-dataset-and)
Task: Self-Supervised Person Re-Identification
Area: computer-vision

Currently, self-supervised representation learning is mainly tested on image classification tasks, which is not insufficient to verify its effectiveness. It should also be tested in the visual matching task, and pedestrian re-recognition is just such an appropriate task.
Task: Online Clustering
Area: computer-vision

Models that learn to label each image (i.e. cluster the dataset into its ground truth classes) without seeing the ground truth labels. Under the online scenario, data is in the form of streams, i.e., the whole dataset could not be accessed at the same time and the model should be able to make cluster assignments for new data without accessing the former data.

Image Credit: [Online Clustering by Penalized Weighted GMM](https://arxiv.org/pdf/1902.02544v1.pdf)
Task: Human-Object Interaction Concept Discovery
Area: 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: Video Boundary Captioning
Area: computer-vision

Provided with the timestamp of a boundary inside a video, the machine is required to generate sentences describing the status change at the boundary.
Task: Superpixel Image Classification
Area: computer-vision

A **Superpixel Image classification** can be classified the  group of pixels that share common characteristics (like pixel intensity ) or segementize the common pixel value in to one group.
Task: Medical Image Deblurring
Area: computer-vision

Medical image deblurring aims to remove blurs from medical images
Task: Pose-Guided Image Generation
Area: computer-vision

Pose-guided image generation is the task of generating a new image of a person with guidance from pose information that the new image should synthesise around.

<span style="color:grey; opacity: 0.6">( Image credit: [Coordinate-based Texture Inpainting for Pose-Guided Human Image Generation](https://arxiv.org/pdf/1811.11459v2.pdf) )</span>
Task: On-the-Fly Sketch Based Image Retrieval
Area: computer-vision

Start retrieving as the user starts drawing.
Task: Hand Pose Estimation
Area: computer-vision

Hand pose estimation is the task of finding the joints of the hand from an image or set of video frames.

<span style="color:grey; opacity: 0.6">( Image credit: [Pose-REN](https://github.com/xinghaochen/Pose-REN) )</span>
Task: Missing Markers Reconstruction
Area: computer-vision

Reconstructing missing markers in the motion caption 3d poses
Task: Unsupervised Object Segmentation
Area: computer-vision

Image credit: [ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation](https://paperswithcode.com/paper/clevrtex-a-texture-rich-benchmark-for)
Task: Video Retrieval
Area: computer-vision

The objective of video retrieval is as follows: given a text query and a pool of candidate videos, select the video which corresponds to the text query.  Typically, the videos are returned as a ranked list of candidates and scored via document retrieval metrics.
Task: Displaced People Recognition
Area: computer-vision

Recognise displaced people from images.

<span style="color:grey; opacity: 0.6">( Image credit: [DisplaceNet: Recognising Displaced People from Images by Exploiting Dominance Level](https://arxiv.org/pdf/1905.02025v1.pdf) )</span>
Task: Human fMRI response prediction
Area: computer-vision

The task is: Given a) the set of videos of everyday events and b) the corresponding brain responses recorded while human participants viewed those videos, use computational models to predict brain responses for videos.
Task: Depth Completion
Area: computer-vision

The **Depth Completion** task is a sub-problem of depth estimation. In the sparse-to-dense depth completion problem, one wants to infer the dense depth map of a 3-D scene given an RGB image and its corresponding sparse reconstruction in the form of a sparse depth map obtained either from computational methods such as SfM (Strcuture-from-Motion) or active sensors such as lidar or structured light sensors. 

<span class="description-source">Source: [LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Imagery ](https://arxiv.org/abs/1905.02744)</span>,
<span class="description-source">[Unsupervised Depth Completion from Visual Inertial Odometry](https://arxiv.org/abs/1905.08616)</span>
Task: Font Recognition
Area: 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: Retinal OCT Disease Classification
Area: computer-vision

Classifying different Retinal degeneration from Optical Coherence Tomography Images (OCT).
Task: Image Stylization
Area: 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: Fundus to Angiography Generation
Area: computer-vision

Generating Retinal Fluorescein Angiography from Retinal Fundus Image using Generative Adversarial Networks.
Task: Situation Recognition
Area: computer-vision

Situation Recognition aims to produce the structured image summary which describes the primary activity (verb), and its relevant entities (nouns).
Task: Instance Search
Area: computer-vision

Visual **Instance Search** is the task of retrieving from a database of images the ones that contain an instance of a visual query. It is typically much more challenging than finding images from the database that contain objects belonging to the same category as the object in the query. If the visual query is an image of a shoe, visual Instance Search does not try to find images of shoes, which might differ from the query in shape, color or size, but tries to find images of the exact same shoe as the one in the query image. Visual Instance Search challenges image representations as the features extracted from the images must enable such fine-grained recognition despite variations in viewpoints, scale, position, illumination, etc. Whereas holistic image representations, where each image is mapped to a single high-dimensional vector, are sufficient for coarse-grained similarity retrieval, local features are needed for instance retrieval.


<span class="description-source">Source: [Dynamicity and Durability in Scalable Visual Instance Search ](https://arxiv.org/abs/1805.10942)</span>
Task: Image Registration
Area: computer-vision

Image registration is the process of transforming different sets of data into one coordinate system. Data may be multiple photographs, data from different sensors, times, depths, or viewpoints. It is used in computer vision, medical imaging, and compiling and analyzing images and data from satellites. Registration is necessary in order to be able to compare or integrate the data obtained from these different measurements.

Source: [Image registration | Wikipedia](https://en.wikipedia.org/wiki/Image_registration)

( Image credit: [Kornia](https://github.com/kornia/kornia) )
Task: 3D Object Detection From Monocular Images
Area: computer-vision

This is the task of detecting 3D objects from monocular images (as opposed to LiDAR based counterparts). It is usually associated with autonomous driving based tasks.

<span style="color:grey; opacity: 0.6">( Image credit: [Orthographic Feature Transform for Monocular 3D Object Detection](https://arxiv.org/pdf/1811.08188v1.pdf) )</span>
Task: One-Shot Face Stylization
Area: computer-vision

Image credit: ["JoJoGAN: One Shot Face Stylization"](https://arxiv.org/pdf/2112.11641v1.pdf)
Task: Prompt-driven Zero-shot Domain Adaptation
Area: computer-vision

Domain adaptation using only a single source domain and a description of the target domain in natural language (No images from target domain are available)
Task: Scene Text Detection
Area: computer-vision

**Scene Text Detection** is a computer vision task that involves automatically identifying and localizing text within natural images or videos. The goal of scene text detection is to develop algorithms that can robustly detect and and label text with bounding boxes in uncontrolled and complex environments, such as street signs, billboards, or license plates.


<span class="description-source">Source: [ContourNet: Taking a Further Step toward Accurate Arbitrary-shaped Scene Text Detection ](https://arxiv.org/abs/2004.04940)</span>
Task: Video Summarization
Area: computer-vision

**Video Summarization** aims to generate a short synopsis that summarizes the video content by selecting its most informative and important parts. The produced summary is usually composed of a set of representative video frames (a.k.a. *video key-frames*), or video fragments (a.k.a. *video key-fragments*) that have been stitched in chronological order to form a shorter video. The former type of a video summary is known as **video storyboard**, and the latter type is known as **video skim**.

<span class="description-source">Source: [Video Summarization Using Deep Neural Networks: A Survey](https://arxiv.org/abs/2101.06072)</span> <br>
<span style="color:grey; opacity: 0.6">Image credit: [iJRASET](https://www.ijraset.com/fileserve.php?FID=12932)</span>
Task: Open Vocabulary Attribute Detection
Area: computer-vision

Open-Vocabulary Attribute Detection (OVAD) is a task that aims to detect and recognize an open set of objects and their associated attributes in an image. The objects and attributes are defined by text queries during inference, without prior knowledge of the tested classes during training.
Task: Object Detection In Aerial Images
Area: computer-vision

Object Detection in Aerial Images is the task of detecting objects from aerial images.

<span style="color:grey; opacity: 0.6">( Image credit: [DOTA: A Large-Scale Dataset for Object Detection in Aerial Images](http://openaccess.thecvf.com/content_cvpr_2018/papers/Xia_DOTA_A_Large-Scale_CVPR_2018_paper.pdf) )</span>
Task: Weakly Supervised Action Localization
Area: computer-vision

In this task, the training data consists of videos with a list of activities in them without any temporal boundary annotations. However, while testing, given a video, the algorithm should recognize the activities in the video and also provide the start and end time.
Task: 3D Multi-Person Pose Estimation (root-relative)
Area: computer-vision

This task aims to solve root-relative 3D multi-person pose estimation (person-centric coordinate system). 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: Object Counting
Area: computer-vision

The goal of **Object Counting** task is to count the number of object instances in a single image or video sequence. It has many real-world applications such as traffic flow monitoring, crowdedness estimation, and product counting.


<span class="description-source">Source: [Learning to Count Objects with Few Exemplar Annotations ](https://arxiv.org/abs/1905.07898)</span>
Task: Medical Object Detection
Area: computer-vision

Medical object detection is the task of identifying medical-based objects within an image.

<span style="color:grey; opacity: 0.6">( Image credit: [Liver Lesion Detection from Weakly-labeled Multi-phase CT Volumes with a Grouped Single Shot MultiBox Detector](https://github.com/L0SG/grouped-ssd-pytorch) )</span>
Task: Low-Light Image Enhancement
Area: computer-vision

**Low-Light Image Enhancement** is a computer vision task that involves improving the quality of images captured under low-light conditions. The goal of low-light image enhancement is to make images brighter, clearer, and more visually appealing, without introducing too much noise or distortion.
Task: Video Salient Object Detection
Area: computer-vision

Video salient object detection (VSOD) is significantly essential for understanding the underlying mechanism behind HVS during free-viewing in general and instrumental to a wide range of real-world applications, e.g., video segmentation, video captioning, video compression, autonomous driving, robotic interaction, weakly supervised attention. Besides its academic value and practical significance, VSOD presents great difficulties due to the challenges carried by video data (diverse motion patterns, occlusions, blur, large object deformations, etc.) and the inherent complexity of human visual attention behavior (i.e., selective attention allocation, attention shift) during dynamic scenes. Online benchmark: http://dpfan.net/davsod.

<span style="color:grey; opacity: 0.6">( Image credit: [Shifting More Attention to Video Salient Object Detection, CVPR2019-Best Paper Finalist](https://openaccess.thecvf.com/content_CVPR_2019/papers/Fan_Shifting_More_Attention_to_Video_Salient_Object_Detection_CVPR_2019_paper.pdf) )</span>
Task: Zero-Shot Object Detection
Area: computer-vision

Zero-shot object detection (ZSD) is the task of object detection where no visual training data is available for some of the target object classes.

<span style="color:grey; opacity: 0.6">( Image credit: [Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts](https://github.com/salman-h-khan/ZSD_Release) )</span>
Task: Surface Normals Estimation
Area: computer-vision

Surface normal estimation deals with the task of predicting the surface orientation of the objects present inside a scene. Refer to [Designing Deep Networks for Surface Normal Estimation (Wang et al.)](https://www.cs.cmu.edu/~xiaolonw/papers/deep3d.pdf) to get a good overview of several design choices that led to the development of a CNN-based surface normal estimator.
Task: Mistake Detection
Area: 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: Referring Image Matting (Prompt-based)
Area: computer-vision

Prompt-based referring image matting, taking an image and a prompt word as the input.
Task: 3D Shape Representation
Area: computer-vision

Image: [MeshNet](https://arxiv.org/pdf/1811.11424v1.pdf)
Task: Image Harmonization
Area: computer-vision

Image harmonization aims to modify the color of the composited region with respect to the specific background.