Color Extraction is a task in computer vision that involves the extraction and analysis of colors from images or videos. The objective of this task is to identify and isolate specific colors, or color ranges present in the visual data.

Background Removal is an image processing technique, used to separate the main object from the background of a photo. Removing the background helps highlight the product, subject, or character, bringing a professional and aesthetically pleasing look to the image.

The goal of Image to Anime was to create a new version of the image that would possess the same clean lines and evoke the characteristic feel found in anime productions, capturing the unique artistry, and aesthetics associated with this style.

Face mesh detection, also known as facial landmark detection or face pose estimation, is the task of identifying and localizing specific keypoints or landmarks on a human face. It involves detecting the positions of facial features, such as eyes, eyebrows, nose, mouth, and jawline, in an image or video.

Face detection is a computer vision technique that involves identifying and locating human faces within an image or video. The goal of face detection is to detect the presence of faces, and draw bounding boxes around them, without necessarily identifying specific facial features or landmarks.

Background Replacement is a powerful tool that enables users to easily change the background of their images, opening up endless possibilities for creative transformations, and visual enhancements.

Video to Canny Edge is the process of converting a video into a Canny edge representation, where edges in the video are emphasized and separated. Canny Edge is a popular algorithm in image processing and is often used to detect edges in images and videos.

Semantic-Guided Low-Light Network is a task that integrates semantic information into the process of enhancing the quality of images captured in low-light conditions. By incorporating semantic guidance, this task aims to improve the accuracy and effectiveness of enhancing low-light images by considering the context and content of the scene.

Model for Face Anti-Spoofing Challenge

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Dataset for Face Anti-Spoofing Challenge

Dataset for Spaceship Titanic Challenge

DUVINE PTOJECT

Radiology images are an essential part of clinical decision making and population screening, e.g., for cancer. Automated systems could help clinicians cope with large amounts of images by answering questions about the image contents. An emerging area of artificial intelligence, Visual Question Answering (VQA) in the medical domain explores approaches to this form of clinical decision support. Success of such machine learning tools hinges on availability and design of collections composed of medical images augmented with question-answer pairs directed at the content of the image. We introduce VQA-RAD, the first manually constructed dataset where clinicians asked naturally occurring questions about radiology images and provided reference answers. Manual categorization of images and questions provides insight into clinically relevant tasks and the natural language to phrase them.