Search Results for author:
Found 8 papers, 3 papers with code
CCExpert: Advancing MLLM Capability in Remote Sensing Change Captioning with Difference-Aware Integration and a Foundational Dataset
In this paper, we propose a novel model, CCExpert, based on a new, advanced multimodal large model framework.
SRMAE: Masked Image Modeling for Scale-Invariant Deep Representations
Our method also achieves an accuracy of 74. 84% on the task of recognizing low-resolution facial expressions, surpassing the current state-of-the-art FMD by 9. 48%.
Generalized Expectation Maximization Framework for Blind Image Super Resolution
Learning-based methods for blind single image super resolution (SISR) conduct the restoration by a learned mapping between high-resolution (HR) images and their low-resolution (LR) counterparts degraded with arbitrary blur kernels.
Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report
The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing.
Category-Adaptive Domain Adaptation for Semantic Segmentation
Unsupervised domain adaptation (UDA) becomes more and more popular in tackling real-world problems without ground truth of the target domain.
deGraphCS: Embedding Variable-based Flow Graph for Neural Code Search
With the rapid increase in the amount of public code repositories, developers maintain a great desire to retrieve precise code snippets by using natural language.
G-DARTS-A: Groups of Channel Parallel Sampling with Attention
Differentiable Architecture Search (DARTS) provides a baseline for searching effective network architectures based gradient, but it is accompanied by huge computational overhead in searching and training network architecture.
Small-footprint Keyword Spotting Using Deep Neural Network and Connectionist Temporal Classifier
Mainly for the sake of solving the lack of keyword-specific data, we propose one Keyword Spotting (KWS) system using Deep Neural Network (DNN) and Connectionist Temporal Classifier (CTC) on power-constrained small-footprint mobile devices, taking full advantage of general corpus from continuous speech recognition which is of great amount.
