Research on Adaptive Middle-Modality Alignment Learning for Visible-Infrared Cross-Modality Learning Background and Problem Statement Driven by the needs of intelligent surveillance systems, visible-infrared person re-identification (VIReID) has gradually become a prominent research topic. This task aims to achieve around-the-clock person recogniti...

Feature Matching Based on Graph Clustering: Implementation and Application of Local Affine Consensus Academic Background and Motivation Feature matching is a fundamental problem in computer vision, playing a critical role in various tasks such as 3D reconstruction, image retrieval, image registration, and simultaneous localization and mapping (SLAM...

Achieving Procedure-Aware Instructional Video Correlation Learning Under Weak Supervision: Summary and Evaluation In recent years, instructional videos have garnered significant attention due to their goal-driven characteristics and intrinsic connections to human learning processes. Compared to general videos, instructional videos contain multiple ...

Academic Paper Report: Open World Object Detection Framework Using SAM Background As the importance of ecosystem monitoring grows, the observation of wildlife and plant populations has become a crucial aspect of ecological conservation and agricultural development. These monitoring tasks include estimating population sizes, identifying species, stu...

Interpretation of “MassiveFold: Unveiling AlphaFold’s Hidden Potential with Optimized and Parallelized Massive Sampling” Background and Research Questions Protein structure prediction is a crucial area in life sciences, vital for understanding fundamental mechanisms in molecular biology. Recently, DeepMind’s AlphaFold achieved revolutionary progres...

A New Perspective on Domain Adaptive Semantic Segmentation: T2S-DA Study Background and Significance Semantic segmentation plays a crucial role in computer vision, but its performance often relies on extensive labeled data. However, acquiring labeled data is costly, especially in complex scenarios. To address this, many studies turn to synthetic da...

One-shot Generative Domain Adaptation in 3D GANs In recent years, Generative Adversarial Networks (GANs) have achieved remarkable progress in the field of image generation. While traditional 2D generative models exhibit impressive performance across various tasks, extending this technology to 3D domains (3D-aware image generation) remains challengi...

Deep Learning Explainability Research: A Perturbation-Based Evaluation Method for Attribution Maps Background and Motivation With the remarkable success of deep learning models across various tasks, there is growing attention on the interpretability and transparency of these models. However, while these models excel in accuracy, their decision-maki...

Report on the Study of Deep Learning-Based Video Stabilization Methods and the SynthStab Synthetic Dataset Background Introduction Digital video stabilization technology, which removes unnecessary vibrations and camera motion artifacts through software, is a critical component in modern video processing, particularly for amateur video shooting. How...

Multi-View Transformation Network (MVTN): New Advances in 3D Understanding Research Background and Motivation Recent advancements in deep learning for 3D data in computer vision have achieved significant success, particularly in tasks like classification, segmentation, and retrieval. However, effectively utilizing 3D shape information remains a cha...