J0nasW/paperswithcode huggingface dataset:
Task: Hyperspectral Image Classification
Area: computer-vision

**Hyperspectral Image Classification** is a task in the field of remote sensing and computer vision. It involves the classification of pixels in hyperspectral images into different classes based on their spectral signature. Hyperspectral images contain information about the reflectance of objects in hundreds of narrow, contiguous wavelength bands, making them useful for a wide range of applications, including mineral mapping, vegetation analysis, and urban land-use mapping. The goal of this task is to accurately identify and classify different types of objects in the image, such as soil, vegetation, water, and buildings, based on their spectral properties.

<span style="color:grey; opacity: 0.6">( Image credit: [Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image Classification](https://arxiv.org/pdf/1810.12563v1.pdf) )</span>
Task: Image to Video Generation
Area: computer-vision

**Image to Video Generation** refers to the task of generating a sequence of video frames based on a single still image or a set of still images. The goal is to produce a video that is coherent and consistent in terms of appearance, motion, and style, while also being temporally consistent, meaning that the generated video should look like a coherent sequence of frames that are temporally ordered. This task is typically tackled using deep generative models, such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), that are trained on large datasets of videos. The models learn to generate plausible video frames that are conditioned on the input image, as well as on any other auxiliary information, such as a sound or text track.
Task: Zero Shot Skeletal Action Recognition
Area: computer-vision

Zero-Shot Learning for 3D skeletal action recognition
Task: RGB Salient Object Detection
Area: computer-vision

RGB Salient object detection is a task-based on a visual attention mechanism, in which algorithms aim to explore objects or regions more attentive than the surrounding areas on the scene or RGB images.

<span style="color:grey; opacity: 0.6">( Image credit: [Attentive Feedback Network for Boundary-Aware Salient Object Detection](http://openaccess.thecvf.com/content_CVPR_2019/papers/Feng_Attentive_Feedback_Network_for_Boundary-Aware_Salient_Object_Detection_CVPR_2019_paper.pdf) )</span>
Task: Image Retrieval
Area: computer-vision

**Image Retrieval** is a computer vision task that involves searching for images in a large database that are similar to a given query image. The goal of image retrieval is to enable users to find images that match their interests or needs, based on visual similarity or other criteria.

<span style="color:grey; opacity: 0.6">( Image credit: [DELF](https://github.com/tensorflow/models/tree/master/research/delf) )</span>
Task: Video Object Tracking
Area: computer-vision

Video Object Detection aims to detect targets in videos using both spatial and temporal information. It's usually deeply integrated with tasks such as Object Detection and Object Tracking.
Task: Blind Image Deblurring
Area: computer-vision

**Blind Image Deblurring** is a classical problem in image processing and computer vision, which aims to recover a latent image from a blurred input.


<span class="description-source">Source: [Learning a Discriminative Prior for Blind Image Deblurring ](https://arxiv.org/abs/1803.03363)</span>
Task: 3D Canonicalization
Area: computer-vision

3D Canonicalization is the process of estimating a transformation-invariant  feature for classification and part segmentation tasks.
Task: Multi-Label Classification
Area: computer-vision

**Multi-Label Classification** is the supervised learning problem where an instance may be associated with multiple labels. This is an extension of single-label classification (i.e., multi-class, or binary) where each instance is only associated with a single class label.


<span class="description-source">Source: [Deep Learning for Multi-label Classification ](https://arxiv.org/abs/1502.05988)</span>
Task: Transparent Object Depth Estimation
Area: computer-vision

Estimating the 3D shape of transparent objects
Task: Human Parsing
Area: computer-vision

Human parsing is the task of segmenting a human image into different fine-grained semantic parts such as head, torso, arms and legs.

<span style="color:grey; opacity: 0.6">( Image credit: [Multi-Human-Parsing (MHP)
](https://github.com/ZhaoJ9014/Multi-Human-Parsing) )</span>
Task: Few-Shot Image Classification
Area: computer-vision

**Few-Shot Image Classification** is a computer vision task that involves training machine learning models to classify images into predefined categories using only a few labeled examples of each category (typically < 6 examples). The goal is to enable models to recognize and classify new images with minimal supervision and limited data, without having to train on large datasets. (typically < 6 examples)

<span style="color:grey; opacity: 0.6">( Image credit: [Learning Embedding Adaptation for Few-Shot Learning](https://github.com/Sha-Lab/FEAT) )</span>
Task: Sar Image Despeckling
Area: computer-vision

Despeckling is the task of suppressing speckle from Synthetic Aperture Radar (SAR) acquisitions.

Image credits: GRD Sentinel-1 SAR image despeckled with [SAR2SAR-GRD](https://arxiv.org/abs/2102.00692)
Task: Face Transfer
Area: computer-vision

**Face Transfer** is a method for mapping face performances of one individual to facial animations of another one. It uses facial expressions and head poses from the video of a source actor to generate a video of a target character. Face Transfer is a special case of image-to-image translation tasks.


<span class="description-source">Source: [Face Transfer with Generative Adversarial Network ](https://arxiv.org/abs/1710.06090)</span>
Task: RF-based Gesture Recognition
Area: computer-vision

RF-based gesture sensing and recognition has increasingly attracted intense academic and industrial interest due to its various device-free applications in daily life, such as elder monitoring, mobile games. State-of-the-art approaches achieved accurate gesture sensing by using fine-grained RF signatures (such as CSI, Doppler effect) while could not achieve the same accuracy with coarse-grained RF signatures such as received signal strength (RSS).

See e.g.

Project Soli in depth: How radar-detected gestures could set the Pixel 4 apart
An experimental Google project may finally be ready to make its way into the real world — and the implications could be enormous. https://www.computerworld.com/article/3402019/google-project-soli-pixel-4.html

<span style="color:grey; opacity: 0.6">( Image credit: [Accurate Human Gesture Sensing With
Coarse-Grained RF Signatures](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8737967) )</span>
Task: Multi-Label Image Classification
Area: computer-vision

The Multi-Label Image Classification focuses on predicting labels for images in a multi-class classification problem where each image may belong to more than one class.
Task: Super-Resolution
Area: computer-vision

**Super-Resolution** is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

<span style="color:grey; opacity: 0.6">( Credit: [MemNet](https://github.com/tyshiwo/MemNet) )</span>
Task: Autonomous Driving
Area: computer-vision

Autonomous driving is the task of driving a vehicle without human conduction. 

Many of the state-of-the-art results can be found at more general task pages such as [3D Object Detection](https://paperswithcode.com/task/3d-object-detection) and [Semantic Segmentation](https://paperswithcode.com/task/semantic-segmentation).

<span style="color:grey; opacity: 0.5">(Image credit: [Exploring the Limitations of Behavior Cloning for Autonomous Driving](https://arxiv.org/pdf/1904.08980v1.pdf))</span>
Task: Weakly-Supervised Action Recognition
Area: computer-vision

Action recognition with single-point annotations in time (there are no action start/stop time annotations)
Task: Person Search
Area: computer-vision

**Person Search** is a task which aims at matching a specific person among a great number of whole scene images.


<span class="description-source">Source: [Re-ID Driven Localization Refinement for Person Search ](https://arxiv.org/abs/1909.08580)</span>
Task: Online Action Detection
Area: computer-vision

Online action detection is the task of predicting the action as soon as it happens in a streaming video without access to video frames in the future.
Task: Crowd Counting
Area: computer-vision

**Crowd Counting** is a task to count people in image. It is mainly used in real-life for automated public monitoring such as surveillance and traffic control. Different from object detection, Crowd Counting aims at recognizing arbitrarily sized targets in various situations including sparse and cluttering scenes at the same time.


<span class="description-source">Source: [Deep Density-aware Count Regressor ](https://arxiv.org/abs/1908.03314)</span>
Task: Object Localization
Area: computer-vision

**Object Localization** is the task of locating an instance of a particular object category in an image, typically by specifying a tightly cropped bounding box centered on the instance. An object proposal specifies a candidate bounding box, and an object proposal is said to be a correct localization if it sufficiently overlaps a human-labeled “ground-truth” bounding box for the given object. In the literature, the “Object Localization” task is to locate one instance of an object category, whereas “object detection” focuses on locating all instances of a category in a given image.


<span class="description-source">Source: [Fast On-Line Kernel Density Estimation for Active Object Localization ](https://arxiv.org/abs/1611.05369)</span>
Task: Age-Invariant Face Recognition
Area: computer-vision

Age-invariant face recognition is the task of performing face recognition that is invariant to differences in age.

<span style="color:grey; opacity: 0.6">( Image credit: [Look Across Elapse](https://arxiv.org/pdf/1809.00338v2.pdf) )</span>
Task: Video Object Segmentation
Area: computer-vision

Video object segmentation is a binary labeling problem aiming to separate foreground object(s) from the background region of a video.

For leaderboards please refer to the different subtasks.
Task: Semi-Supervised Sketch Based Image Retrieval
Area: computer-vision

Whilst the number of photos can be easily scaled, each corresponding sketch still needs to be individually produced for fine-grained sketch-based image retrieval. The objective is to mitigate such an upper-bound on sketch data, and study whether unlabelled photos alone (of which they are many) can be cultivated for performance gain.
Task: Facial Landmark Detection
Area: computer-vision

**Facial Landmark Detection** is a computer vision task that involves detecting and localizing specific points or landmarks on a face, such as the eyes, nose, mouth, and chin. The goal is to accurately identify these landmarks in images or videos of faces in real-time and use them for various applications, such as face recognition, facial expression analysis, and head pose estimation.

<span style="color:grey; opacity: 0.6">( Image credit: [Style Aggregated Network for Facial Landmark Detection](https://arxiv.org/pdf/1803.04108v4.pdf) )</span>
Task: Point-Supervised Instance Segmentation
Area: computer-vision

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

**Image Generation** (synthesis) is the task of generating new images from an existing dataset.

- **Unconditional generation** refers to generating samples unconditionally from the dataset, i.e. $p(y)$
- **[Conditional image generation](/task/conditional-image-generation)** (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.

In this section, you can find state-of-the-art leaderboards for **unconditional generation**. For conditional  generation, and other types of image generations, refer to the subtasks.

<span style="color:grey; opacity: 0.6">( Image credit: [StyleGAN](https://github.com/NVlabs/stylegan) )</span>
Task: Video Prediction
Area: computer-vision

**Video Prediction** is the task of predicting future frames given past video frames.


<span class="description-source">Gif credit: [MAGVIT ](https://magvit.cs.cmu.edu/)</span>

<span class="description-source">Source: [Photo-Realistic Video Prediction on Natural Videos of Largely Changing Frames ](https://arxiv.org/abs/2003.08635)</span>
Task: Reference-based Super-Resolution
Area: computer-vision

Reference-based Super-Resolution aims to recover high-resolution images by utilizing external reference images containing similar content to generate rich textures.
Task: Aggregate xView3 Metric
Area: computer-vision

The aggregate xView3 metric is the combination of five metrics: object detection F1 score, close-to-shore object detection F1 score, vessel/not vessel classification F1 score, fishing/not fishing classification F1 score, and vessel length estimation percent error regression.
Task: Vehicle 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: Persuasion Strategies
Area: computer-vision

Prediction of Persuasion Strategy in Advertisements
Task: Video Story QA
Area: computer-vision

MCQ about clips from movies/tvshows/etc
Task: Multiple Object Forecasting
Area: computer-vision

<span style="color:grey; opacity: 0.6">( Image credit: [Multiple Object Forecasting](https://github.com/olly-styles/Multiple-Object-Forecasting) )</span>
Task: Monocular Depth Estimation
Area: computer-vision

**Monocular Depth Estimation** is the task of estimating the depth value (distance relative to the camera) of each pixel given a single (monocular) RGB image. This challenging task is a key prerequisite for determining scene understanding for applications such as 3D scene reconstruction, autonomous driving, and AR. State-of-the-art methods usually fall into one of two categories: designing a complex network that is powerful enough to directly regress the depth map, or splitting the input into bins or windows to reduce computational complexity.  The most popular benchmarks are the KITTI and NYUv2 datasets. Models are typically evaluated using RMSE or absolute relative error. 

<span class="description-source">Source: [Defocus Deblurring Using Dual-Pixel Data ](https://arxiv.org/abs/2005.00305)</span>
Task: Multi-Object Tracking
Area: computer-vision

**Multi-Object Tracking** is a task in computer vision that involves detecting and tracking multiple objects within a video sequence. The goal is to identify and locate objects of interest in each frame and then associate them across frames to keep track of their movements over time. This task is challenging due to factors such as occlusion, motion blur, and changes in object appearance, and is typically solved using algorithms that integrate object detection and data association techniques.
Task: Lane Detection
Area: computer-vision

**Lane Detection** is a computer vision task that involves identifying the boundaries of driving lanes in a video or image of a road scene. The goal is to accurately locate and track the lane markings in real-time, even in challenging conditions such as poor lighting, glare, or complex road layouts. 

Lane detection is an important component of advanced driver assistance systems (ADAS) and autonomous vehicles, as it provides information about the road layout and the position of the vehicle within the lane, which is crucial for navigation and safety. The algorithms typically use a combination of computer vision techniques, such as edge detection, color filtering, and Hough transforms, to identify and track the lane markings in a road scene.

<span style="color:grey; opacity: 0.6">( Image credit: [End-to-end Lane Detection
](https://github.com/wvangansbeke/LaneDetection_End2End) )</span>
Task: Multimodal Unsupervised Image-To-Image Translation
Area: computer-vision

Multimodal unsupervised image-to-image translation is the task of producing multiple translations to one domain from a single image in another domain.

<span style="color:grey; opacity: 0.6">( Image credit: [MUNIT: Multimodal UNsupervised Image-to-image Translation](https://github.com/NVlabs/MUNIT) )</span>
Task: Abnormal Event Detection In Video
Area: computer-vision

**Abnormal Event Detection In Video** is a challenging task in computer vision, as the definition of what an abnormal event looks like depends very much on the context. For instance, a car driving by on the street is regarded as a normal event, but if the car enters a pedestrian area, this is regarded as an abnormal event. A person running on a sports court (normal event) versus running outside from a bank (abnormal event) is another example. Although what is considered abnormal depends on the context, we can generally agree that abnormal events should be unexpected events that occur less often than familiar (normal) events


<span class="description-source">Source: [Unmasking the abnormal events in video ](https://arxiv.org/abs/1705.08182)</span>

Image: [Ravanbakhsh et al](https://arxiv.org/pdf/1708.09644v1.pdf)
Task: Fake Image Attribution
Area: computer-vision

Attribute the origin (model/architecture) of fake images.
Task: Pose Retrieval
Area: computer-vision

Retrieval of similar human poses from images or videos
Task: Low-light Pedestrian Detection
Area: computer-vision

Pedestrian Detection in low-light condition
Task: Action Recognition In Videos
Area: computer-vision

**Action Recognition in Videos** is a task in computer vision and pattern recognition where the goal is to identify and categorize human actions performed in a video sequence. The task involves analyzing the spatiotemporal dynamics of the actions and mapping them to a predefined set of action classes, such as running, jumping, or swimming.
Task: Scanpath prediction
Area: computer-vision

Learning to Predict Sequences of Human Fixations.
Task: Weakly-Supervised Semantic Segmentation
Area: computer-vision

The semantic segmentation task is to assign a label from a label set to each pixel in an image. In the case of fully supervised setting, the dataset  consists of images and their corresponding
pixel-level class-specific annotations (expensive pixel-level annotations). However, in the
weakly-supervised setting, the dataset consists of images and corresponding annotations that
are relatively easy to obtain, such as tags/labels of objects present in the image.

<span style="color:grey; opacity: 0.6">( Image credit: [Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing](http://openaccess.thecvf.com/content_cvpr_2018/papers/Huang_Weakly-Supervised_Semantic_Segmentation_CVPR_2018_paper.pdf) )</span>
Task: Image Inpainting
Area: computer-vision

**Image Inpainting** is a task of reconstructing missing regions in an image. It is an important problem in computer vision and an essential functionality in many imaging and graphics applications, e.g. object removal, image restoration, manipulation, re-targeting, compositing, and image-based rendering.

<span class="description-source">Source: [High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling ](https://arxiv.org/abs/2005.11742)</span>

Image source: [High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling](https://arxiv.org/pdf/2005.11742.pdf)
Task: Visibility Estimation from Point Cloud
Area: computer-vision

Estimate the point-wise visibility of each point from a given point of view (a point, or a view frustum).
Task: Weakly Supervised Object Detection
Area: computer-vision

Weakly Supervised Object Detection (WSOD) is the task of training object detectors with only image tag supervisions.

<span style="color:grey; opacity: 0.6">( Image credit: [Soft Proposal Networks for Weakly Supervised Object Localization](https://arxiv.org/pdf/1709.01829v1.pdf) )</span>
Task: 6D Pose Estimation
Area: computer-vision

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

**Boundary Detection** is a vital part of extracting information encoded in images, allowing for the computation of quantities of interest including density, velocity, pressure, etc.


<span class="description-source">Source: [A Locally Adapting Technique for Boundary Detection using Image Segmentation ](https://arxiv.org/abs/1707.09030)</span>
Task: Class-Incremental Semantic Segmentation
Area: computer-vision

Semantic segmentation with continous increments of classes.
Task: Visual Object Tracking
Area: computer-vision

**Visual Object Tracking** is an important research topic in computer vision, image understanding and pattern recognition. Given the initial state (centre location and scale) of a target in the first frame of a video sequence, the aim of Visual Object Tracking is to automatically obtain the states of the object in the subsequent video frames.


<span class="description-source">Source: [Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Object Tracking ](https://arxiv.org/abs/1807.11348)</span>
Task: Weakly-supervised Temporal Action Localization
Area: computer-vision

Temporal Action Localization with weak supervision where only video-level labels are given for training
Task: Handwritten Chinese Text Recognition
Area: computer-vision

Handwritten Chinese text recognition is the task of interpreting handwritten Chinese input, e.g., from images of documents or scans.
Task: Autonomous Navigation
Area: computer-vision

Autonomous navigation is the task of autonomously navigating a vehicle or robot to or around a location without human guidance.

<span style="color:grey; opacity: 0.6">( Image credit: [Approximate LSTMs for Time-Constrained Inference:
Enabling Fast Reaction in Self-Driving Cars](https://arxiv.org/pdf/1905.00689v2.pdf) )</span>
Task: Facial Makeup Transfer
Area: computer-vision

Facial makeup transfer aims to translate the **makeup style** from a given *reference* makeup face image to another non-makeup one while *preserving face identity*.
Task: 3D Multi-Person Pose Estimation
Area: computer-vision

This task aims to solve root-relative 3D multi-person pose estimation. No human bounding box and root joint coordinate groundtruth are used in testing time.

<span style="color:grey; opacity: 0.6">( Image credit: [RootNet](https://github.com/mks0601/3DMPPE_ROOTNET_RELEASE) )</span>
Task: Tumor Segmentation
Area: computer-vision

Tumor Segmentation is the task of identifying the spatial location of a tumor. It is a pixel-level prediction where each pixel is classified as a tumor or background. The most popular benchmark for this task is the BraTS dataset. The models are typically evaluated with the Dice Score metric.
Task: Unbiased Scene Graph Generation
Area: computer-vision

Unbiased Scene Graph Generation (Unbiased SGG) aims to predict more informative scene graphs composed of more "tail predicates" *(in contrast to "head predicates" in terms of class frequencies) by dealing with the skewed, long-tailed predicate class distribution. (Definition from Chiou et al. "Recovering the Unbiased Scene Graphs from the Biased Ones")
Task: Irregular Text Recognition
Area: computer-vision

To read a text from an image might be difficult due to the improper angle of the text inside the image or due to surprising font. Hence, to recognize the text data from the image, Irregular Text Recognition is used.