Official implementation of I2I-Mamba, an image-to-image translation model based on selective state spaces

[ACM MM'24 Oral] RainMamba: Enhanced Locality Learning with State Space Models for Video Deraining

[WACV2025] SUM: Saliency Unification through Mamba for Visual Attention Modeling

[NeurIPS 2024] Official implementation of the paper "MambaLRP: Explaining Selective State Space Sequence Models".

Official repository for Mamba-based Segmentation Model for Speaker Diarization

MambaMIM: Pre-training Mamba with State Space Token-interpolation

Library for Federated Emergence & Foundation Models

This evaluation explores the In-context learning (ICL) capabilities of pre-trained language models on arithmetic tasks and sentiment analysis using synthetic datasets. The goal is to use different prompting strategies—zero-shot, few-shot, and chain-of-thought—to assess the performance of these models on the given tasks.