Remote sensing image classification forms the foundation of various understanding tasks, serving a crucial function in remote sensing image interpretation.
We adopt a two-stage training strategy for the diffusion model, effectively binding movements with specific appearances.
Image inpainting, the process of restoring corrupted images, has seen significant advancements with the advent of diffusion models (DMs).
Contrastive Language-Image Pre-training (CLIP) has been the cornerstone for zero-shot classification, text-image retrieval, and text-image generation by aligning image and text modalities.
Surprisingly, our Japanese Math LLM achieved state-of-the-art performance on a variety of established Japanese LLM benchmarks, even surpassing models with significantly more parameters, despite not being explicitly trained for such tasks.
In this work, we address two limitations of existing conditional diffusion models: their slow inference speed due to the iterative denoising process and their reliance on paired data for model fine-tuning.
Deep features are a cornerstone of computer vision research, capturing image semantics and enabling the community to solve downstream tasks even in the zero- or few-shot regime.
Ranked #1 on Feature Upsampling on ImageNet
We present MegaBlocks, a system for efficient Mixture-of-Experts (MoE) training on GPUs.
Knowledge distillation involves transferring soft labels from a teacher to a student using a shared temperature-based softmax function.
Ranked #1 on Knowledge Distillation on CIFAR-100
The challenge is that information entropy may be a suboptimal compression metric: (i) it only leverages unidirectional context and may fail to capture all essential information needed for prompt compression; (ii) it is not aligned with the prompt compression objective.