Learning to Detect Novel Species with SAM in the Wild

Engineering, Environmental Engineering, Ocean Engineering, Information Science, Computer Science, Artificial Intelligence, Life Sciences, Botany,
object detection in the wild   semi-supervised learning   incremental learning   novelty detection   biodiversity   wildlife monitoring   plant monitoring  

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...

MassiveFold: Unveiling AlphaFold’s Hidden Potential with Optimized and Parallelized Massive Sampling

Information Science, Computer Science, Artificial Intelligence, Life Sciences, Biochemistry, Biophysics,
AlphaFold   protein structures   massive sampling   GPU optimization   parallelization   antibody-antigen   protein assemblies  

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...

Pulling Target to Source: A New Perspective on Domain Adaptive Semantic Segmentation

Information Science, Computer Science, Automation, Artificial Intelligence,
domain adaptation   semantic segmentation   domain generalization   contrastive learning   cross-domain features  

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

Information Science, Electronic Information, Computer Science, Artificial Intelligence,
3D-aware image generation   generative adversarial networks   one-shot domain adaptation   cross-domain consistency   multi-view consistency  

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...

Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach

Information Science, Electronic Information, Computer Science, Automation, Artificial Intelligence,
attribution maps   perturbation evaluation   explainability   convolutional neural networks   smoothness   monotonicity   baseline checks  

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...

Cross-Scale Co-Occurrence Local Binary Pattern for Image Classification

Information Science, Electronic Information, Computer Science, Artificial Intelligence,
image classification   local binary pattern   cross-scale   co-occurrence LBP features   scale invariant   rotation invariant  

Research on Cross-Scale Co-Occurrence Local Binary Pattern (CS-COLBP) for Image Classification Image classification is a key area in computer vision, with feature extraction being its core research focus. The Local Binary Pattern (LBP), due to its efficiency and descriptive power, has been widely used in tasks such as texture classification and fac...