Towards Visual Re-Identification of Fish using Fine-Grained Classification for Electronic Monitoring in Fisheries

A new automatic system has been developed for predicting heart failure using a combination of Modal Decomposition and Masked Autoencoders, addressing the critical need for early detection in heart disease, which is responsible for approximately 18 million deaths annually according to the WHO. This innovative approach transforms echocardiography video sequences into annotated images suitable for machine learning applications.

A new framework called Cross-modal Explainable Framework for Melanoma (CEFM) has been introduced, utilizing contrastive learning to enhance interpretability in melanoma diagnosis by aligning clinical criteria with visual features through Vision Transformer embeddings.

A new study introduces a masked autoencoder pretraining strategy applied to simulated strong-lensing images, aiming to classify dark matter models and enhance low-resolution images through super-resolution techniques. This method utilizes a Vision Transformer encoder trained with a masked image modeling objective, demonstrating improved performance in both tasks compared to traditional training methods.

MuSASplat has been introduced as an innovative framework for efficient sparse-view 3D Gaussian splatting, significantly reducing the computational demands of training pose-free feed-forward models while maintaining high rendering quality. This method leverages a lightweight Multi-Scale Adapter to fine-tune Vision Transformer architectures with fewer parameters, addressing the limitations of previous full-model adaptation techniques.