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Found 5 papers, 1 papers with code
Efficient Diffusion Models: A Comprehensive Survey from Principles to Practices
As one of the most popular and sought-after generative models in the recent years, diffusion models have sparked the interests of many researchers and steadily shown excellent advantage in various generative tasks such as image synthesis, video generation, molecule design, 3D scene rendering and multimodal generation, relying on their dense theoretical principles and reliable application practices.
Neural Residual Diffusion Models for Deep Scalable Vision Generation
The most advanced diffusion models have recently adopted increasingly deep stacked networks (e. g., U-Net or Transformer) to promote the generative emergence capabilities of vision generation models similar to large language models (LLMs).
Transformer-Enhanced Motion Planner: Attention-Guided Sampling for State-Specific Decision Making
EISE and MPT are collaboratively trained, enabling EISE to autonomously learn and extract patterns from environmental data, thereby forming semantic representations that MPT could more effectively interpret and utilize for motion planning.
NWPU-MOC: A Benchmark for Fine-grained Multi-category Object Counting in Aerial Images
Considering the absence of a dataset for this task, a large-scale Dataset (NWPU-MOC) is collected, consisting of 3, 416 scenes with a resolution of 1024 $\times$ 1024 pixels, and well-annotated using 14 fine-grained object categories.
TWEETSUM: Event oriented Social Summarization Dataset
In this paper, we construct TWEETSUM, a new event-oriented dataset for social summarization.
