Search Results for author:
Found 8 papers, 6 papers with code
SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-Resolution
In this paper, we propose the SAM-DiffSR model, which can utilize the fine-grained structure information from SAM in the process of sampling noise to improve the image quality without additional computational cost during inference.
DenseMamba: State Space Models with Dense Hidden Connection for Efficient Large Language Models
Large language models (LLMs) face a daunting challenge due to the excessive computational and memory requirements of the commonly used Transformer architecture.
Data-efficient Large Vision Models through Sequential Autoregression
Training general-purpose vision models on purely sequential visual data, eschewing linguistic inputs, has heralded a new frontier in visual understanding.
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Recent advancements in large language models have sparked interest in their extraordinary and near-superhuman capabilities, leading researchers to explore methods for evaluating and optimizing these abilities, which is called superalignment.
Operation Scheme Optimizations to Achieve Ultra-high Endurance (1010) in Flash Memory with Robust Reliabilities
Flash memory has been widely adopted as stand-alone memory and embedded memory due to its robust reliability.
Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism
In the past years, YOLO-series models have emerged as the leading approaches in the area of real-time object detection.
ST-former for short-term passenger flow prediction during COVID-19 in urban rail transit system
How to dynamically model the complex spatiotemporal dependencies of passenger flow is the main issue in achieving accurate passenger flow prediction during the epidemic.
Real-Time Super-Resolution System of 4K-Video Based on Deep Learning
In order to pursue faster VSR processing ability up to 4K resolution, this paper tries to choose lightweight network structure and efficient upsampling method to reduce the computation required by EGVSR network under the guarantee of high visual quality.
Ranked #2 on
Video Super-Resolution
on MSU Video Upscalers: Quality Enhancement
(VMAF metric)
