Large language models (LLMs) have demonstrated extraordinary capabilities and
contributed to multiple fields, such as generating and summarizing text, language …

Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

Abstract Recurrent Neural Networks (RNNs) offer fast inference on long sequences but are
hard to optimize and slow to train. Deep state-space models (SSMs) have recently been …

State space models (SSMs) have demonstrated state-of-the-art sequence modeling
performance in some modalities, but underperform attention in language modeling …

Models using structured state space sequence (S4) layers have achieved state-of-the-art
performance on long-range sequence modeling tasks. An S4 layer combines linear state …

Driven by the goal of eradicating language barriers on a global scale, machine translation
has solidified itself as a key focus of artificial intelligence research today. However, such …

We present XMem, a video object segmentation architecture for long videos with unified
feature memory stores inspired by the Atkinson-Shiffrin memory model. Prior work on video …

Recent text-to-image diffusion models are able to generate convincing results of
unprecedented quality. However, it is nearly impossible to control the shapes of different …

In recent years, convolutional neural networks (CNNs) have drawn significant attention for
the classification of hyperspectral images (HSIs). Due to their self-attention mechanism, the …

Multi-modality fusion strategy is currently the de-facto most competitive solution for 3D
perception tasks. In this work, we present a new framework termed BEVFormer, which learns …