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Found 12 papers, 9 papers with code
Binarized 3D Whole-body Human Mesh Recovery
In this work, we propose a Binarized Dual Residual Network (BiDRN), a novel quantization method to estimate the 3D human body, face, and hands parameters efficiently.
Image Super-Resolution with Text Prompt Diffusion
Specifically, we first design a text-image generation pipeline to integrate text into the SR dataset through the text degradation representation and degradation model.
Natural Language based Context Modeling and Reasoning for Ubiquitous Computing with Large Language Models: A Tutorial
Recently, with the rise of LLMs and their improved natural language understanding and reasoning capabilities, it has become feasible to model contexts using natural language and perform context reasoning by interacting with LLMs such as ChatGPT and GPT-4.
Toward Reproducing Network Research Results Using Large Language Models
Reproducing research results in the networking community is important for both academia and industry.
SwapMoE: Efficient Memory-Constrained Serving of Large Sparse MoE Models via Dynamic Expert Pruning and Swapping
The main idea of SwapMoE is to keep a small dynamic set of important experts, namely Virtual Experts, in the main memory for inference, while seamlessly maintaining how the Virtual Experts map to the actual experts.
Dual Aggregation Transformer for Image Super-Resolution
Based on the above idea, we propose a novel Transformer model, Dual Aggregation Transformer (DAT), for image SR. Our DAT aggregates features across spatial and channel dimensions, in the inter-block and intra-block dual manner.
Ranked #6 on
Image Super-Resolution
on Manga109 - 4x upscaling
Hierarchical Integration Diffusion Model for Realistic Image Deblurring
Specifically, we perform the DM in a highly compacted latent space to generate the prior feature for the deblurring process.
Xformer: Hybrid X-Shaped Transformer for Image Denoising
The channel-wise Transformer block performs direct global context interactions across tokens defined by channel dimension.
Recursive Generalization Transformer for Image Super-Resolution
In this work, we propose the Recursive Generalization Transformer (RGT) for image SR, which can capture global spatial information and is suitable for high-resolution images.
Ranked #5 on
Image Super-Resolution
on Manga109 - 4x upscaling
Cross Aggregation Transformer for Image Restoration
The core of our CAT is the Rectangle-Window Self-Attention (Rwin-SA), which utilizes horizontal and vertical rectangle window attention in different heads parallelly to expand the attention area and aggregate the features cross different windows.
Accurate Image Restoration with Attention Retractable Transformer
This is considered as a dense attention strategy since the interactions of tokens are restrained in dense regions.
TGAN: Deep Tensor Generative Adversarial Nets for Large Image Generation
Secondly, we propose TGAN that integrates deep convolutional generative adversarial networks and tensor super-resolution in a cascading manner, to generate high-quality images from random distributions.
