J0nasW/paperswithcode huggingface dataset:
Task: Data Free Quantization
Area: computer-vision

**Data Free Quantization** is a technique to achieve a highly accurate quantized model without accessing any training data.

<span class="description-source">Source: [Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples](https://arxiv.org/abs/2111.02625)</span>
Task: 3D Point Cloud Part Segmentation
Area: computer-vision

3D point cloud part segmentation on datasets like ShapeNet Part, etc.
Task: Affordance Detection
Area: computer-vision

Affordance detection refers to identifying the potential action possibilities of objects in an image, which is an important ability for robot perception and manipulation.

Image source: [Object-Based Affordances Detection with Convolutional Neural Networks and Dense Conditional Random Fields](https://dkanou.github.io/publ/P15__Nguyen_Kanoulas_Caldwell_Tsagarakis__2017__Object-Based_Affordances_Detection_with_Convolutional_Neural_Networks_and__Dense_Conditional_Random_Fields.pdf)

Unlike other visual or physical properties that mainly describe the object alone, affordances indicate functional interactions of object parts with humans.
Task: Ad-hoc video search
Area: computer-vision

The Ad-hoc search task ended a 3 year cycle from 2016-2018 with a goal to model the end user search use-case, who is searching (using textual sentence queries) for segments of video containing persons, objects, activities, locations, etc. and combinations of the former. While the Internet Archive (IACC.3) dataset was adopted between 2016 to 2018, starting in 2019 a new data collection based on Vimeo Creative Commons (V3C) will be adopted to support the task for at least 3 more years.

Given the test collection (V3C1 or IACC.3), master shot boundary reference, and set of Ad-hoc queries (approx. 30 queries) released by NIST, return for each query a list of at most 1000 shot IDs from the test collection ranked according to their likelihood of containing the target query.
Task: Unsupervised Landmark Detection
Area: computer-vision

The discovery of object landmarks on a set of images depicting objects of the same category, directly from raw images without using any manual annotations.
Task: MULTI-VIEW LEARNING
Area: computer-vision

**Multi-View Learning** is a machine learning framework where data are represented by multiple distinct feature groups, and each feature group is referred to as a particular view.


<span class="description-source">Source: [Dissimilarity-based representation for radiomics applications ](https://arxiv.org/abs/1803.04460)</span>
Task: Action Localization
Area: computer-vision

Action Localization is finding the spatial and temporal co ordinates for an action in a video. An action localization model will identify which frame an action start and ends in video and return the x,y coordinates of an action. Further the co ordinates will change  when the object performing action  undergoes a displacement.
Task: Artistic style classification
Area: computer-vision

Classify the artistic style of an artwork image
Task: 3D Semantic Segmentation
Area: computer-vision

**3D Semantic Segmentation** is a computer vision task that involves dividing a 3D point cloud or 3D mesh into semantically meaningful parts or regions. The goal of 3D semantic segmentation is to identify and label different objects and parts within a 3D scene, which can be used for applications such as robotics, autonomous driving, and augmented reality.
Task: Handwritten Word Segmentation
Area: computer-vision

Handwritten Word Segmentation from Document Images.
Task: Novel View Synthesis
Area: computer-vision

Synthesize a target image with an arbitrary target camera pose from given source images and their camera poses.

<span style="color:grey; opacity: 0.6">( Image credit: [Multi-view to Novel view: Synthesizing novel views with Self-Learned Confidence](https://github.com/shaohua0116/Multiview2Novelview) )</span>
Task: Sparse Representation-based Classification
Area: computer-vision

Sparse Representation-based Classification is the task based on the description of the data as a linear combination of few building blocks - atoms - taken from a pre-defined dictionary of such fundamental elements.
Task: Composed Image Retrieval (CoIR)
Area: computer-vision

**Composed Image Retrieval (CoIR)** is the task involves retrieving images from a large database based on a query composed of multiple elements, such as text, images, and sketches. The goal is to develop algorithms that can understand and combine multiple sources of information to accurately retrieve images that match the query, extending the user’s expression ability.
Task: Spatial Token Mixer
Area: computer-vision

Spatial Token Mixer (STM) is a module for vision transformers that aims to improve the efficiency of token mixing. STM is a type of depthwise convolution that operates on the spatial dimension of the tokens. STM is a drop-in replacement for the token mixing layers in vision transformers.
Task: Part-aware Panoptic Segmentation
Area: computer-vision

Panoptic segmentation with part-aware predictions.
Task: Blind Face Restoration
Area: computer-vision

Blind face restoration aims at recovering high-quality faces from the low-quality counterparts suffering from unknown degradation, such as low-resolution, noise, blur, compression artifacts, etc. When applied to real-world scenarios, it becomes more challenging, due to more complicated degradation, diverse poses and expressions.


Description source: [Towards Real-World Blind Face Restoration with Generative Facial Prior](https://paperswithcode.com/paper/towards-real-world-blind-face-restoration)

Image source: [Towards Real-World Blind Face Restoration with Generative Facial Prior](https://paperswithcode.com/paper/towards-real-world-blind-face-restoration)
Task: MRI Reconstruction
Area: computer-vision

In its most basic form, MRI reconstruction consists in retrieving a complex-valued image from its under-sampled Fourier coefficients. 
Besides, it can be addressed as a encoder-decoder task, in which the normative model in the latent space will only capture the relevant information without noise or corruptions. Then, we decode the latent space in order to have a reconstructed MRI.
Task: Unsupervised Long Term Person Re-Identificationa
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: 3D Facial Landmark Localization
Area: computer-vision

Image: [Zhang et al](https://arxiv.org/pdf/1801.09242v1.pdf)
Task: Content-Based Image Retrieval
Area: computer-vision

**Content-Based Image Retrieval** is a well studied problem in computer vision, with retrieval problems generally divided into two groups: category-level retrieval and instance-level retrieval. Given a query image of the Sydney Harbour bridge, for instance, category-level retrieval aims to find any bridge in a given dataset of images, whilst instance-level retrieval must find the Sydney Harbour bridge to be considered a match.


<span class="description-source">Source: [Camera Obscurer: Generative Art for Design Inspiration ](https://arxiv.org/abs/1903.02165)</span>
Task: Photo geolocation estimation
Area: computer-vision

**Photo geolocation estimation** is task of estimate or classify the geolocation from photos on world map.
Task: Homography Estimation
Area: computer-vision

**Homography estimation** is a technique used in computer vision and image processing to find the relationship between two images of the same scene, but captured from different viewpoints. It is used to align images, correct for perspective distortions, or perform image stitching. In order to estimate the homography, a set of corresponding points between the two images must be found, and a mathematical model must be fit to these points. There are various algorithms and techniques that can be used to perform homography estimation, including direct methods, RANSAC, and machine learning-based approaches.
Task: Driver Attention Monitoring
Area: computer-vision

Driver attention monitoring is the task of monitoring the attention of a driver.

<span style="color:grey; opacity: 0.6">( Image credit: [Predicting Driver Attention in Critical Situations](https://arxiv.org/pdf/1711.06406v3.pdf) )</span>
Task: Group Activity Recognition
Area: computer-vision

**Group Activity Recognition** is a subset of human activity recognition problem which focuses on the collective behavior of a group of people, resulted from the individual actions of the persons and their interactions. Collective activity recognition is a basic task for automatic human behavior analysis in many areas like surveillance or sports videos.


<span class="description-source">Source: [A Multi-Stream Convolutional Neural Network Framework for Group Activity Recognition ](https://arxiv.org/abs/1812.10328)</span>
Task: UNET Segmentation
Area: computer-vision

U-Net is an architecture for semantic segmentation. It consists of a contracting path (Up to down) and an expanding path (Down to up). During the contraction, the spatial information is reduced while feature information is increased.
The contracting path follows the typical architecture of a convolutional network. It consists of the repeated application of two 3x3 convolutions (unpadded convolutions), each followed by a rectified linear unit (ReLU) and a 2x2 max pooling operation with stride 2 for downsampling. At each downsampling step, we double the number of feature channels. Every step in the expansive path consists of an upsampling of the feature map followed by a 2x2 convolution (“up-convolution”) that halves the number of feature channels, a concatenation with the correspondingly cropped feature map from the contracting path, and two 3x3 convolutions, each followed by a ReLU. The cropping is necessary due to the loss of border pixels in every convolution. At the final layer, a 1x1 convolution is used to map each 64-component feature vector to the desired number of classes. In total the network has 23 convolutional layers.
Task: General Action Video Anomaly Detection
Area: computer-vision

Detecting if an entire short clip of a any action features an anomalous motion - another action class not seen during training.
Task: Video Harmonization
Area: computer-vision

Video harmonization aims to adjust the foreground of a composite video to make it compatible with the background.
Task: 6D Pose Estimation using RGB
Area: computer-vision

**6D Pose Estimation using RGB** refers to the task of determining the six degree-of-freedom (6D) pose of an object in 3D space based on RGB images. This involves estimating the position and orientation of an object in a scene, and is a fundamental problem in computer vision and robotics. In this task, the goal is to estimate the 6D pose of an object given an RGB image of the object and the scene, which can be used for tasks such as robotic manipulation, augmented reality, and scene reconstruction.

<span style="color:grey; opacity: 0.6">( Image credit: [Segmentation-driven 6D Object Pose Estimation](https://github.com/cvlab-epfl/segmentation-driven-pose) )</span>
Task: Compressive Sensing
Area: computer-vision

**Compressive Sensing** is a new signal processing framework for efficiently acquiring and reconstructing a signal that have a sparse representation in a fixed linear basis.


<span class="description-source">Source: [Sparse Estimation with Generalized Beta Mixture and the Horseshoe Prior ](https://arxiv.org/abs/1411.2405)</span>
Task: Video Frame Interpolation
Area: computer-vision

The goal of **Video Frame Interpolation** is to synthesize several frames in the middle of two adjacent frames of the original video. Video Frame Interpolation can be applied to generate slow motion video, increase video frame rate, and frame recovery in video streaming.


<span class="description-source">Source: [Reducing the X-ray radiation exposure frequency in cardio-angiography via deep-learning based video interpolation ](https://arxiv.org/abs/2006.00781)</span>
Task: No-Reference Image Quality Assessment
Area: computer-vision

An Image Quality Assessment approach where no reference image information is available to the model.
Task: Face Identification
Area: computer-vision

Face identification is the task of matching a given face image to one in an existing database of faces. It is the second part of face recognition (the first part being detection). It is a one-to-many mapping: you have to find an unknown person in a database to find who that person is.
Task: Person-centric Visual Grounding
Area: computer-vision

Person-centric visual grounding is the problem of linking between people named in a caption and people pictured in an image. Introduced in "Who's Waldo? Linking People Across Text and Images" (Cui et al, ICCV 2021).
Task: Image Captioning
Area: computer-vision

**Image Captioning** is the task of describing the content of an image in words. This task lies at the intersection of computer vision and natural language processing. Most image captioning systems use an encoder-decoder framework, where an input image is encoded into an intermediate representation of the information in the image, and then decoded into a descriptive text sequence. The most popular benchmarks are nocaps and COCO, and models are typically evaluated according to a BLEU or CIDER metric.

( Image credit: [Reflective Decoding Network for Image Captioning, ICCV'19](https://openaccess.thecvf.com/content_ICCV_2019/papers/Ke_Reflective_Decoding_Network_for_Image_Captioning_ICCV_2019_paper.pdf))
Task: Few-Shot Object Detection
Area: computer-vision

**Few-Shot Object Detection** is a computer vision task that involves detecting objects in images with limited training data. The goal is to train a model on a few examples of each object class and then use the model to detect objects in new images.
Task: Color Mismatch Correction
Area: computer-vision

Color mismatch correction is the task of transferring color from one view of a stereopair to corresponding areas in another where the colors differ incorrectly. Color mismatches can decrease the overall video quality and may cause viewer discomfort and headaches. Similar tasks are color transfer and example-based colorization.
Task: Dichotomous Image Segmentation
Area: computer-vision

Currently, existing image segmentation tasks mainly focus on segmenting objects with specific characteristics, e.g., salient, camouflaged, meticulous, or specific categories. Most of them have the same input/output formats, and barely use exclusive mechanisms designed for segmenting targets in their models, which means almost all tasks are dataset-dependent. Thus, it is very promising to formulate a category-agnostic DIS task for accurately segmenting objects with different structure complexities, regardless of their characteristics. Compared with semantic segmentation, the proposed DIS task usually focuses on images with single or a few targets, from which getting richer accurate details of each target is more feasible.
Task: Perpetual View Generation
Area: computer-vision

**Perpetual View Generation** is the task of generating long-range novel views by flying into a given image.
Task: 3D Absolute Human Pose Estimation
Area: computer-vision

This task aims to solve absolute (camera-centric not root-relative) 3D human pose estimation. 

<span style="color:grey; opacity: 0.6">( Image credit: [RootNet](https://github.com/mks0601/3DMPPE_ROOTNET_RELEASE) )</span>
Task: Small Object Detection
Area: computer-vision

**Small Object Detection** is a computer vision task that involves detecting and localizing small objects in images or videos. This task is challenging due to the small size and low resolution of the objects, as well as other factors such as occlusion, background clutter, and variations in lighting conditions.

<span style="color:grey; opacity: 0.6">( Image credit: [Feature-Fused SSD](https://arxiv.org/pdf/1709.05054v3.pdf) )</span>
Task: Lip to Speech Synthesis
Area: computer-vision

Given a silent video of a speaker, generate the corresponding speech that matches the lip movements.
Task: Parking Space Occupancy
Area: computer-vision

Image credit: [https://github.com/martin-marek/parking-space-occupancy](https://github.com/martin-marek/parking-space-occupancy)
Task: Instance Segmentation
Area: computer-vision

**Instance Segmentation** is a computer vision task that involves identifying and separating individual objects within an image, including detecting the boundaries of each object and assigning a unique label to each object. The goal of instance segmentation is to produce a pixel-wise segmentation map of the image, where each pixel is assigned to a specific object instance.

Image Credit: [Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers, CVPR'21](https://github.com/lkeab/BCNet)
Task: Image-level Supervised Instance Segmentation
Area: computer-vision

Weakly-Supervised Instance Segmentation using Image-level Labels
Task: Infinite Image Generation
Area: computer-vision

**Infinite Image Generation** refers to the task of generating an unlimited number of images that belong to a specific distribution or category. It is a challenging task, as it requires the model to capture the underlying patterns and distributions in the data, and generate images that are diverse, yet still follow the same patterns. There are various techniques and algorithms that can be used to perform infinite image generation, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Convolutional Neural Networks (CNNs).
Task: Visual Dialog
Area: computer-vision

Visual Dialog requires an AI agent to hold a meaningful dialog with humans in natural, conversational language about visual content. Specifically, given an image, a dialog history, and a follow-up question about the image, the task is to answer the question.
Task: 3D Point Cloud Interpolation
Area: computer-vision

Point cloud interpolation is a fundamental problem for 3D computer vision. Given a low temporal resolution (frame rate) point cloud sequence, the target of interpolation is to generate a smooth point cloud sequence with high temporal resolution (frame rate).
Task: Hindi Image Captioning
Area: computer-vision

The main goal of this task is to generate a caption for an input image in a native langugae Hindi.
Task: Referring Image Matting (Expression-based)
Area: computer-vision

Expression-based referring image matting, taking an image and a flowery expression as the input.
Task: Multi-Frame Super-Resolution
Area: computer-vision

When multiple images of the same view are taken from slightly different positions, perhaps also at different times, then they collectively contain more information than any single image on its own. Multi-Frame Super-Resolution fuses these low-res inputs into a composite high-res image that can reveal some of the original detail that cannot be recovered from any low-res image alone.

<span style="color:grey; opacity: 0.6">( Credit: [HighRes-net](https://github.com/ElementAI/HighRes-net) )</span>
Task: Image Restoration
Area: computer-vision

**Image Restoration** is a family of inverse problems for obtaining a high quality image from a corrupted input image. Corruption may occur due to the image-capture process (e.g., noise, lens blur), post-processing (e.g., JPEG compression), or photography in non-ideal conditions (e.g., haze, motion blur).


<span class="description-source">Source: [Blind Image Restoration without Prior Knowledge ](https://arxiv.org/abs/2003.01764)</span>
Task: Computer Vision Transduction
Area: computer-vision

Transductive learning in computer vision tasks
Task: Open World Object Detection
Area: computer-vision

Open World Object Detection is a computer vision problem where a model is tasked to: 1) identify objects that have not been introduced to it as `unknown', without explicit supervision to do so, and 2) incrementally learn these identified unknown categories without forgetting previously learned classes, when the corresponding labels are progressively received.
Task: Point-interactive Image Colorization
Area: computer-vision

__Point-interactive colorization__ is a task of colorizing images given user-guided clicks containing colors (a.k.a color hints). 
Unlike unconditional image colorization, which is an underdetermined problem by nature, point-interactive colorization aims to generate images containing specific colors given by the user. 

Point-interactive colorization is evaluated by providing simulated user hints from the groundtruth color image. 
Following the [iColoriT protocol](https://arxiv.org/abs/2207.06831), user hints have a size of 2x2 pixels and color is given as the average color within the 2x2 pixels.
Task: Temporal Defect Localization
Area: computer-vision

Closed-Circuit TeleVision (CCTV) is popular method for pipe defect
inspection. Different from short QV videos, CCTV videos are much longer and record more comprehensive content in the very distant pipe. The main task is to discover temporal locations of pipe defects in such untrimmed videos. Clearly, manual inspection is expensive, based on hundreds of hours of CCTV videos. To fill this gap, we introduce this temporal localization task, which is to find the temporal locations of pipe detects and recognizing their corresponding categories in
a long CCTV video.
Task: Semi-Supervised Image Classification (Cold Start)
Area: computer-vision

This is the same as the [semi-supervised image classification task](https://paperswithcode.com/task/semi-supervised-image-classification), with the key difference being that the labelled subset chosen needs to be selection in a class agnostic manner. This means that the standard practice in semi-supervised learning of using a random class stratified sample is "cheating" in this case, as class information is required for the whole dataset for this to be done. Rather, this challenge requires a smart cold-start or unsupervised selective labelling strategy to identify images that are most informative and result in the best performing models.
Task: Multiple Object Track and Segmentation
Area: computer-vision

Multiple object tracking and segmentation requires detecting, tracking, and segmenting objects belonging to a set of given classes. 

(Image and definition credit: [Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation](https://github.com/SysCV/pcan), NeurIPS 2021, Spotlight )
Task: Multi-target Domain Adaptation
Area: computer-vision

The idea of Multi-target Domain Adaptation is to adapt a model from a single labelled source domain to multiple unlabelled target domains.
Task: RF-based Pose Estimation
Area: computer-vision

Detect human actions through walls and occlusions, and in poor lighting conditions. Taking radio frequency (RF) signals as input (e.g. Wifi), generating 3D human skeletons as an intermediate representation, and recognizing actions and interactions.

See e.g. RF-Pose from MIT for a good illustration of the approach
http://rfpose.csail.mit.edu/

<span style="color:grey; opacity: 0.6">( Image credit: [Making the Invisible Visible](https://arxiv.org/pdf/1909.09300v1.pdf) )</span>
Task: Weakly Supervised Action Segmentation (Action Set))
Area: computer-vision

Learning an action segmentation model while the only available supervision is action set -- the set of actions happened in the video without information about their temporal locations.
Task: 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: Training-free 3D Part Segmentation
Area: computer-vision

Evaluation on target datasets for 3D Part Segmentation without any training