J0nasW/paperswithcode huggingface dataset:
Task: Raw reconstruction
Area: computer-vision

Reconstruct RAW camera sensor readings from the corresponding sRGB images
Task: Surgical tool detection
Area: computer-vision

Presence detection of various classes of surgical instruments in endoscopy videos.
Task: Event-based vision
Area: computer-vision

An event camera, also known as a neuromorphic camera, silicon retina or dynamic vision sensor, is an imaging sensor that responds to local changes in brightness. Event cameras do not capture images using a shutter as conventional cameras do. Instead, each pixel inside an event camera operates independently and asynchronously, reporting changes in brightness as they occur and staying silent otherwise. Modern event cameras have microsecond temporal resolution, 120 dB dynamic range, and less under/overexposure and motion blur than frame cameras.
Task: Multi-Person Pose Estimation
Area: computer-vision

Multi-person pose estimation is the task of estimating the pose of multiple people in one frame.

<span style="color:grey; opacity: 0.6">( Image credit: [Human Pose Estimation with TensorFlow
](https://github.com/eldar/pose-tensorflow) )</span>
Task: person reposing
Area: computer-vision

Person reposing describes the task of changing the pose of a human in a given image to any desired target pose.
Task: 3D Object Detection From Stereo Images
Area: computer-vision

Estimating oriented 3D bounding boxes from Stereo Cameras only.

Image: [You et al](https://openreview.net/pdf?id=BJedHRVtPB)
Task: Unsupervised 3D Point Cloud Linear Evaluation
Area: computer-vision

Training a linear classifier(e.g. SVM) on the representations learned in an unsupervised manner on the pretrained(e.g. ShapeNet) dataset.
Task: Defect Detection
Area: computer-vision

For automatic detection of surface defects in various products
Task: Cryogenic Electron Microscopy (cryo-EM)
Area: computer-vision

Analysis of images and videos from transmission electron microscopes, including single-particle cryogenic electron microscopy and cryogenic electron tomography (cryo-ET).
https://en.wikipedia.org/wiki/Cryogenic_electron_microscopy
Task: Unsupervised Video Object Segmentation
Area: computer-vision

The unsupervised scenario assumes that the user does not interact with the algorithm to obtain the segmentation masks. Methods should provide a set of object candidates with no overlapping pixels that span through the whole video sequence. This set of objects should contain at least the objects that capture human attention when watching the whole video sequence i.e objects that are more likely to be followed by human gaze.
Task: Sign Language Translation
Area: computer-vision

Given a video containing sign language, the task is to predict the translation into (written) spoken language.

Image credit: [How2Sign](https://how2sign.github.io/)
Task: Image Relighting
Area: computer-vision

Image relighting involves changing the illumination settings of an image.
Task: Image Compression
Area: computer-vision

**Image Compression** is an application of data compression for digital images to lower their storage and/or transmission requirements.


<span class="description-source">Source: [Variable Rate Deep Image Compression With a Conditional Autoencoder ](https://arxiv.org/abs/1909.04802)</span>
Task: Multimodal fashion image editing
Area: computer-vision

Given a target model image, a sketch and a textual description of a garment, the aim is to generate a new image of the same model, wearing a new garment that follows the user input.
Task: Talking Face Generation
Area: computer-vision

Talking face generation aims to synthesize a sequence of face images that correspond to given speech semantics


<span style="color:grey; opacity: 0.6">( Image credit: [Talking Face Generation by Adversarially Disentangled Audio-Visual Representation](https://github.com/Hangz-nju-cuhk/Talking-Face-Generation-DAVS) )</span>
Task: Image Classification with Differential Privacy
Area: computer-vision

**Image Classification with Differential Privacy** is an improved version of the image classification task whereby the final classification output only describe the patterns of groups within the dataset while withholding information about individuals in the dataset.
Task: Facial Beauty Prediction
Area: computer-vision

Facial beauty prediction is the task of predicting the attractiveness of a face.

<span style="color:grey; opacity: 0.6">( Image credit: [SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction](https://github.com/HCIILAB/SCUT-FBP5500-Database-Release) )</span>
Task: Weakly Supervised Action Segmentation (Transcript)
Area: computer-vision

Action Segmentation from weak (transcript) supervision.
Task: Text-to-Image Generation
Area: computer-vision

**Text-to-Image Generation** is a task in computer vision and natural language processing where the goal is to generate an image that corresponds to a given textual description. This involves converting the text input into a meaningful representation, such as a feature vector, and then using this representation to generate an image that matches the description.
Task: Surgical phase recognition
Area: computer-vision

The first 40 videos are used for training, the last 40 videos are used for testing.
Task: Pupil Dilation
Area: computer-vision

Phenomenon on which the human iris reacts to illumination changes and drugs; thus, changing the aperture of the pupil.
Task: Semi-Supervised Semantic Segmentation
Area: computer-vision

Models that are trained with a small number of labeled examples and a large number of unlabeled examples and whose aim is to learn to segment an image (i.e. assign a class to every pixel).
Task: Autonomous Flight (Dense Forest)
Area: computer-vision

Number of interventions during autonomous flight under the forest canopy.
Task: Object Skeleton Detection
Area: computer-vision

Object skeleton detection is the task of detecting the skeleton of an object in an image.

<span style="color:grey; opacity: 0.6">( Image credit: [DeepFlux for Skeletons in the Wild](https://arxiv.org/pdf/1811.12608v1.pdf) )</span>
Task: Face Generation
Area: computer-vision

Face generation is the task of generating (or interpolating) new faces from an existing dataset.

The state-of-the-art results for this task are located in the Image Generation parent.

<span style="color:grey; opacity: 0.6">( Image credit: [Progressive Growing of GANs for Improved Quality, Stability, and Variation
](https://arxiv.org/pdf/1710.10196v3.pdf) )</span>
Task: human-scene contact detection
Area: computer-vision

detecting contact between human bodies and scenes
Task: 3D Action Recognition
Area: computer-vision

Image: [Rahmani et al](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Rahmani_3D_Action_Recognition_CVPR_2016_paper.pdf)
Task: Burst Image Super-Resolution
Area: computer-vision

Reconstruct a high-resolution image from a set of low-quality images, very like the multi-frame super-resolution task.
Task: Fine-Grained Image Classification
Area: computer-vision

**Fine-Grained Image Classification** is a task in computer vision where the goal is to classify images into subcategories within a larger category. For example, classifying different species of birds or different types of flowers. This task is considered to be fine-grained because it requires the model to distinguish between subtle differences in visual appearance and patterns, making it more challenging than regular image classification tasks.

<span style="color:grey; opacity: 0.6">( Image credit: [Looking for the Devil in the Details](https://arxiv.org/pdf/1903.06150v2.pdf) )</span>
Task: Face Anti-Spoofing
Area: computer-vision

Facial anti-spoofing is the task of preventing false facial verification by using a photo, video, mask or a different substitute for an authorized person’s face. Some examples of attacks:

- **Print attack**: The attacker uses someone’s photo. The image is printed or displayed on a digital device.

- **Replay/video attack**: A more sophisticated way to trick the system, which usually requires a looped video of a victim’s face. This approach ensures behaviour and facial movements to look more ‘natural’ compared to holding someone’s photo.

- **3D mask attack**: During this type of attack, a mask is used as the tool of choice for spoofing. It’s an even more sophisticated attack than playing a face video. In addition to natural facial movements, it enables ways to deceive some extra layers of protection such as depth sensors.


<span style="color:grey; opacity: 0.6">( Image credit: [Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing](https://github.com/XgTu/GFA-CNN) )</span>
Task: 3D Holography
Area: computer-vision

The images that are presented here are multiplanar images that were reconstructed using a holographic display. For more details, please see: https://complightlab.com/publications/realistic_defocus_cgh/
Task: Wildly Unsupervised Domain Adaptation
Area: computer-vision

Transferring knowledge from a noisy source domain to unlabeled target domain.
Task: 3D Shape Reconstruction
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: eXtreme-Video-Frame-Interpolation
Area: computer-vision

Type of Video Frame Interpolation (VFI) that interpolates an intermediate frame on X4K1000FPS dataset containing 4K videos of 1000 fps with the extreme motion. The dataset has a wide variety of textures, extremely large motions, zoomings and occlusions, which have never been seen in the previous VFI benchmark datasets.
Task: Micro-Expression Recognition
Area: computer-vision

Facial Micro-Expression Recognition is a challenging task in identifying suppressed emotion in a high-stake environment, often comes in very brief duration and subtle changes.
Task: Breast Cancer Histology Image Classification (20% labels)
Area: computer-vision

Model's breast cancer histology image classification performance on BreakHis dataset with limited training data labels of 20%.
Task: Scene Flow Estimation
Area: computer-vision

**Scene Flow Estimation** is the task of obtaining 3D structure and 3D motion of dynamic scenes, which is crucial to environment perception, e.g., in the context of autonomous navigation.


<span class="description-source">Source: [Self-Supervised Monocular Scene Flow Estimation ](https://arxiv.org/abs/2004.04143)</span>
Task: Generalized Zero Shot skeletal action recognition
Area: computer-vision

Generalized Zero Shot Learning for 3d Skeletal Action Recognition
Task: Robust 3D Object Detection
Area: computer-vision

3D Object Detection under Out-of-Distribution Scenarios
Task: Polyp Segmentation
Area: computer-vision

The goal of the project is to develop a computer-aided detection and diagnosis system for automatic polyp segmentation and detection.
Task: Transparent Object Detection
Area: computer-vision

Detecting transparent objects in 2D or 3D
Task: 3D Object Retrieval
Area: computer-vision

Source: [He et al](https://arxiv.org/pdf/1803.06189v1.pdf)
Task: Point Cloud Segmentation
Area: computer-vision

3D point cloud segmentation is the process of
classifying point clouds into multiple homogeneous regions, the
points in the same region will have the same properties. The
segmentation is challenging because of high redundancy, uneven
sampling density, and lack explicit structure of point cloud
data. This problem has many applications in robotics such as
intelligent vehicles, autonomous mapping and navigation.

Source: [3D point cloud segmentation: A survey](https://doi.org/10.1109/RAM.2013.6758588)
Task: Loop Closure Detection
Area: computer-vision

Loop closure detection is the process of detecting whether an agent has returned to a previously visited location.

<span style="color:grey; opacity: 0.6">( Image credit: [Backtracking Regression Forests for Accurate Camera Relocalization](https://github.com/LiliMeng/btrf) )</span>
Task: Thermal Infrared Pedestrian Detection
Area: computer-vision

Thermal Infrared Pedestrian Detection under low-light condition
Task: Scale Generalisation
Area: computer-vision

Scale generalisation implies that learning is performed at some scale(s) and testing at other scales.
Task: Phrase Extraction and Grounding (PEG)
Area: computer-vision

PEG requires a model to extract phrases from text and locate objects from images simultaneously.
Task: Face Verification
Area: computer-vision

**Face Verification** is a machine learning task in computer vision that involves determining whether two facial images belong to the same person or not. The task involves extracting features from the facial images, such as the shape and texture of the face, and then using these features to compare and verify the similarity between the images.

<span style="color:grey; opacity: 0.6">( Image credit: [Pose-Robust Face Recognition via Deep Residual Equivariant Mapping](https://arxiv.org/pdf/1803.00839v1.pdf) )</span>
Task: Learning with noisy labels
Area: computer-vision

Learning with noisy labels means When we say "noisy labels," we mean that an adversary has intentionally messed up the labels, which would have come from a "clean" distribution otherwise. This setting can also be used to cast learning from only positive and unlabeled data.
Task: Visual Place Recognition
Area: computer-vision

**Visual Place Recognition** is the task of matching a view of a place with a different view of the same place taken at a different time.

<span class="description-source">Source: [Visual place recognition using landmark distribution descriptors ](https://arxiv.org/abs/1608.04274)</span>

Image credit: [Visual place recognition using landmark distribution descriptors](https://arxiv.org/pdf/1608.04274.pdf)
Task: Temporal Action Localization
Area: computer-vision

Temporal Action Localization aims to detect activities in the video stream and  output beginning and end timestamps. It is closely related to  Temporal Action Proposal Generation.
Task: Constrained Lip-synchronization
Area: computer-vision

This task deals with lip-syncing a video (or) an image to the desired target speech. Approaches in this task work only for a specific (limited set) of identities, languages, speech/voice. See also: Unconstrained lip-synchronization - https://paperswithcode.com/task/lip-sync
Task: Continual Semantic Segmentation
Area: computer-vision

Continual learning in semantic segmentation.
Task: Im2Spec
Area: computer-vision

Predicting spectra from images (and vice versa)
Task: 3D Multi-Object Tracking
Area: computer-vision

Image: [Weng et al](https://arxiv.org/pdf/1907.03961v4.pdf)
Task: Replay Grounding
Area: computer-vision

Replay grounding is introduced in SoccerNet-v2 in the case of videos of soccer games. Given a replay shot of a soccer action, the objective is to retrieve when said action occurs within the whole live game.
Task: Visual Keyword Spotting
Area: computer-vision

Spot a given query keyword in a silent talking face video
Task: Motion Forecasting
Area: computer-vision

Motion forecasting is the task of predicting the location of a tracked object in the future
Task: Image to Point Cloud Registration
Area: computer-vision

Given a query image and a scene of point cloud, get the camera pose according to them.
Task: Anomaly Detection at Various Anomaly Percentages
Area: computer-vision

Unsupervised anomaly detection at 10% anomaly
Task: Panoptic Scene Graph Generation
Area: computer-vision

PSG task abstracts the given image with a scene graph, where nodes are grounded by panoptic segmentation
Task: Fast Vehicle Detection
Area: computer-vision

Fast vehicle detection is the task of detecting fast or speeding vehicles from video footage.
Task: Unsupervised Few-Shot Learning
Area: computer-vision

In contrast to supervised few-shot learning, only the unlabeled dataset is available in the pre-training
or meta-training stage for unsupervised few-shot learning.
Task: RF-based Visual Tracking
Area: computer-vision

From mID:
https://doi.org/10.1109/DCOSS.2019.00028

"The key to offering personalised services in smart spaces is knowing where a particular person is with a high degree of accuracy. Visual tracking is one such solution, but concerns arise around the potential leakage of raw video information and many people are not comfortable accepting cameras in their homes or workplaces. We propose a human tracking and identification system (mID) based on millimeter wave radar which has a high tracking accuracy, without being visually compromising. Unlike competing techniques based on WiFi Channel State Information (CSI), it is capable of tracking and identifying multiple people simultaneously. Using a lowcost, commercial, off-the-shelf radar, we first obtain sparse point clouds and form temporally associated trajectories."

<span style="color:grey; opacity: 0.6">( Image credit: [mID: Tracking and Identifying People with Millimeter Wave Radar](http://www.cs.ox.ac.uk/files/10889/%5BDCOSS19%5DmID.pdf) )</span>
Task: HYPERVIEW Challenge
Area: computer-vision

The objective of this challenge is to advance the state of the art for soil parameter retrieval from hyperspectral data in view of the upcoming Intuition-1 mission. A campaign took place in March 2021 over agricultural areas in Poland with extensive ground samplings collocated with airborne hyperspectral measurements from imagers mounted onboard an airplane. The hyperspectral data contains 150 contiguous hyperspectral bands (462-942 nm, with a spectral resolution of 3.2 nm), which reflects the spectral range of the hyperspectral imaging sensor deployed on-board Intuition-1.
Task: Image Matching
Area: computer-vision

Image Matching or wide multiple baseline stereo (WxBS) is a process of establishing a sufficient number of pixel or region correspondences from two or more images depicting the same scene to estimate the geometric relationship between cameras, which produced these images.

Source: [The Role of Wide Baseline Stereo in the Deep Learning World](https://ducha-aiki.github.io/wide-baseline-stereo-blog/2020/03/27/intro.html)

( Image credit: [Kornia](https://github.com/kornia/kornia) )
Task: Real-Time Multi-Object Tracking
Area: computer-vision

Online and Real-time Multi-Object Tracking would achieve the real-time speed over 30 frames per second with online approach.
Task: Sequential Image Classification
Area: computer-vision

Sequential image classification is the task of classifying a sequence of images.

<span style="color:grey; opacity: 0.6">( Image credit: [TensorFlow-101](https://github.com/sjchoi86/tensorflow-101/blob/master/notebooks/rnn_mnist_simple.ipynb) )</span>
Task: Pedestrian Density Estimation
Area: computer-vision

Pedestrian density estimation is the task of estimating the density of pedestrians from cameras.
Task: Fake Image Detection
Area: computer-vision

<span style="color:grey; opacity: 0.6">( Image credit: [FaceForensics++](https://github.com/ondyari/FaceForensics) )</span>
Task: Multi-Person Pose Estimation and Tracking
Area: computer-vision

Joint multi-person pose estimation and tracking following the PoseTrack benchmark. 
https://posetrack.net/

<span style="color:grey; opacity: 0.6">( Image credit: [PoseTrack](https://github.com/iqbalu/PoseTrack-CVPR2017) )</span>
Task: Weakly-supervised 3D Human Pose Estimation
Area: computer-vision

This task targets at 3D Human Pose Estimation with fewer 3D annotation.
Task: Image Instance Retrieval
Area: computer-vision

**Image Instance Retrieval** is the problem of retrieving images from a database representing the same object or scene as the one depicted in a query image.


<span class="description-source">Source: [Compression of Deep Neural Networks for Image Instance Retrieval ](https://arxiv.org/abs/1701.04923)</span>
Task: Motion Estimation
Area: computer-vision

**Motion Estimation** is used to determine the block-wise or pixel-wise motion vectors between two frames.


<span class="description-source">Source: [MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement ](https://arxiv.org/abs/1810.08768)</span>
Task: Inverse Rendering
Area: computer-vision

**Inverse Rendering** is the task of recovering the properties of a scene, such as shape, material, and lighting, from an image or a video. The goal of inverse rendering is to determine the properties of a scene given an observation of it, and to generate new images or videos based on these properties.
Task: Animal Action Recognition
Area: computer-vision

Cross-species (intra-class, inter-class) action recognition
Task: Generalizable Person Re-identification
Area: computer-vision

Generalizable person re-identification refers to methods trained on a source dataset but directly evaluated on a target dataset without domain adaptation or transfer learning.
Task: Multimodal Sentiment Analysis
Area: computer-vision

Multimodal sentiment analysis is the task of performing sentiment analysis with multiple data sources - e.g. a camera feed of someone's face and their recorded speech.

<span style="color:grey; opacity: 0.6">( Image credit: [ICON: Interactive Conversational Memory Network
for Multimodal Emotion Detection](https://www.aclweb.org/anthology/D18-1280.pdf) )</span>
Task: Multimodal Emotion Recognition
Area: computer-vision

This is a leaderboard for multimodal emotion recognition on the IEMOCAP dataset. The modality abbreviations are
A: Acoustic
T: Text
V: Visual

Please include the modality in the bracket after the model name.

All models must use standard five emotion categories and are evaluated in standard leave-one-session-out (LOSO). See the papers for references.
Task: Wireframe Parsing
Area: computer-vision

Detect Line Segments and their connecting Junctions in a single perspective image.
Task: Few-Shot Transfer Learning for Saliency Prediction
Area: computer-vision

Saliency prediction aims to predict important locations in a visual scene. It is a per-pixel regression task with predicted values ranging from 0 to 1.

Benefiting from deep learning research and large-scale datasets, saliency prediction has achieved significant success in the past decade. However, it still remains challenging to predict saliency maps on images in new domains that lack sufficient data for data-hungry models.
Task: CYCLoPs Accuracy
Area: computer-vision

The WT2 dataset from the CYCLoPs database consists of 27,058 single-cell images of yeast cells. The task is to classify the subcellular localization of a fluoresced protein, given
two channels staining for the protein of interest and the cytosol.
Task: Unsupervised Image Classification
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.

<span style="color:grey; opacity: 0.6"> Image credit: ImageNet clustering results of [SCAN: Learning to Classify Images without Labels (ECCV 2020)](https://arxiv.org/abs/2005.12320) </span>
Task: Code Search
Area: computer-vision

The goal of **Code Search** is to retrieve code fragments from a large code corpus that most closely match a developer’s intent, which is expressed in natural language.


<span class="description-source">Source: [When Deep Learning Met Code Search ](https://arxiv.org/abs/1905.03813)</span>
Task: Unsupervised Few-Shot Image Classification
Area: computer-vision

In contrast to (supervised) few-shot image classification, only the unlabeled dataset is available in the pre-training or meta-training stage for unsupervised few-shot image classification.
Task: Image Cropping
Area: computer-vision

**Image Cropping** is a common photo manipulation process, which improves the overall composition by removing unwanted regions. Image Cropping is widely used in photographic, film processing, graphic design, and printing businesses.


<span class="description-source">Source: [Listwise View Ranking for Image Cropping ](https://arxiv.org/abs/1905.05352)</span>
Task: Multiview Detection
Area: computer-vision

Incorporating multiple camera views for detection in heavily occluded scenarios.