Search Results for author:
Found 16 papers, 5 papers with code
QID: Efficient Query-Informed ViTs in Data-Scarce Regimes for OCR-free Visual Document Understanding
In Visual Document Understanding (VDU) tasks, fine-tuning a pre-trained Vision-Language Model (VLM) with new datasets often falls short in optimizing the vision encoder to identify query-specific regions in text-rich document images.
Temporal-Consistent Video Restoration with Pre-trained Diffusion Models
Video restoration (VR) aims to recover high-quality videos from degraded ones.
PCL: Prompt-based Continual Learning for User Modeling in Recommender Systems
Inspired by the success of prompt tuning in Pretrained Language Models (PLMs), we propose PCL, a Prompt-based Continual Learning framework for user modeling, which utilizes position-wise prompts as external memory for each task, preserving knowledge and mitigating catastrophic forgetting.
Q-Tuning: Queue-based Prompt Tuning for Lifelong Few-shot Language Learning
This paper introduces \textbf{Q-tuning}, a novel approach for continual prompt tuning that enables the lifelong learning of a pre-trained language model.
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results
This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained.
Learning Degradation-Independent Representations for Camera ISP Pipelines
Image signal processing (ISP) pipeline plays a fundamental role in digital cameras, which converts raw Bayer sensor data to RGB images.
Parameter-Efficient Tuning Makes a Good Classification Head
In this paper, we find that parameter-efficient tuning makes a good classification head, with which we can simply replace the randomly initialized heads for a stable performance gain.
Self-Supervised Face Image Restoration with a One-Shot Reference
For image restoration, methods leveraging priors from generative models have been proposed and demonstrated a promising capacity to robustly restore photorealistic and high-quality results.
Functional Neural Networks for Parametric Image Restoration Problems
In this work, we propose a novel system called functional neural network (FuncNet) to solve a parametric image restoration problem with a single model.
Data Acquisition and Preparation for Dual-reference Deep Learning of Image Super-Resolution
The resulting LR$\sim$HR image pairs can be aligned at very high sub-pixel precision by a novel spatial-frequency dual-domain registration method, and hence they provide more appropriate training data for the learning task of super-resolution.
Graph Based Link Prediction between Human Phenotypes and Genes
Methods: In this study, we developed a framework to predict links between human phenotype ontology (HPO) and genes.
Deep Multi-modality Soft-decoding of Very Low Bit-rate Face Videos
We propose a novel deep multi-modality neural network for restoring very low bit rate videos of talking heads.
Multiple Convolutional Neural Network for Skin Dermoscopic Image Classification
Melanoma classification is a serious stage to identify the skin disease.
DeepEMGNet: An Application for Efficient Discrimination of ALS and Normal EMG Signals
The obtained results are also compared with other methods, which show the superiority of the proposed method.
Ranked #1 on
ALS Detection
on ALS EMG (University of Copenhagen)
Authorship attribution of source code by using back propagation neural network based on particle swarm optimization
Then these metrics are input to neural network for supervised learning, the weights of which are output by PSO and BP hybrid algorithm.
