Dynamic People Tracking by Service Robots Using Diffusion Models Academic Background Tracking dynamic people in cluttered and crowded human-centered environments is a challenging problem in robotics. Due to intraclass variations such as occlusions, pose deformations, and lighting changes, traditional tracking methods often struggle to accurately id...

Academic Background and Problem Statement Multi-View Stereo (MVS) is a fundamental task in 3D computer vision that aims to recover the 3D geometry of a scene from multiple posed images. This technology has broad applications in robotics, scene understanding, augmented reality, and more. In recent years, learning-based MVS methods have achieved sign...

Academic Background and Problem Statement In recent years, deep neural networks have made significant progress in the field of computer vision, particularly in tasks such as image recognition, object detection, and semantic segmentation. However, even the most advanced deep models struggle when faced with long-tailed distribution data, where the nu...

Academic Background and Problem Statement With the increasing prominence of data privacy issues, Federated Learning (FL) has emerged as a decentralized machine learning paradigm, allowing multiple clients to collaboratively train a global model without sharing data, thereby protecting data privacy. However, existing FL methods typically assume that...

Academic Background and Problem Statement In recent years, deep learning models (Deep Neural Networks, DNNs) have achieved remarkable success in computer vision tasks. However, the training of these models relies heavily on large amounts of annotated data. When models are applied to new, unlabeled target domains, their generalization ability often ...

Report on the Paper “PICK: Predict and Mask for Semi-Supervised Medical Image Segmentation” Academic Background Accurate segmentation of medical images is crucial in clinical practice, as it provides vital insights into organ/tumor characteristics such as volume, location, and shape. Recent studies have highlighted the significant potential of data...

Robust Sequential Deepfake Detection Academic Background With the rapid development of deep generative models (such as GANs), generating photorealistic facial images has become increasingly easy. However, the misuse of this technology has raised significant security concerns, particularly with the rise of Deepfake technology. Deepfake technology ca...

Academic Background and Problem Statement Underwater images have significant application value in fields such as marine exploration, underwater robotics, and marine life identification. However, due to the refraction and absorption of light by water, underwater images often suffer from low contrast and color distortion, which severely impacts the a...

Exploring Content Fidelity Perceptibility via Quality Adversarial Learning Academic Background Image Quality Assessment (IQA) is a fundamental problem in the field of computer vision, aiming to evaluate the fidelity of visual content in images. IQA has significant applications in areas such as image compression and restoration. Traditional IQA meth...

Report on the Paper “RepsNet: A Nucleus Instance Segmentation Model Based on Boundary Regression and Structural Re-parameterization” Academic Background Pathological diagnosis is the gold standard for tumor diagnosis, and nucleus instance segmentation is a key step in digital pathology analysis and pathological diagnosis. However, the computational...