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Found 11 papers, 4 papers with code
Effectively Enhancing Vision Language Large Models by Prompt Augmentation and Caption Utilization
may change model output and make the output hallucinate again.
One Model for Two Tasks: Cooperatively Recognizing and Recovering Low-Resolution Scene Text Images by Iterative Mutual Guidance
On the other hand, as the STISR and STR models are jointly optimized, to pursue high recognition accuracy, the fidelity of SR images may be spoiled.
CIEM: Contrastive Instruction Evaluation Method for Better Instruction Tuning
Nowadays, the research on Large Vision-Language Models (LVLMs) has been significantly promoted thanks to the success of Large Language Models (LLM).
Privacy-Preserving Face Recognition Using Random Frequency Components
The ubiquitous use of face recognition has sparked increasing privacy concerns, as unauthorized access to sensitive face images could compromise the information of individuals.
HiREN: Towards Higher Supervision Quality for Better Scene Text Image Super-Resolution
Observing the quality issue of HR images, in this paper we propose a novel idea to boost STISR by first enhancing the quality of HR images and then using the enhanced HR images as supervision to do STISR.
Keyword-Based Diverse Image Retrieval by Semantics-aware Contrastive Learning and Transformer
On the one hand, standard retrieval systems are usually biased to common semantics and seldom exploit diversity-aware regularization in training, which makes it difficult to promote diversity by post-processing.
C3-STISR: Scene Text Image Super-resolution with Triple Clues
In this paper, we present a novel method C3-STISR that jointly exploits the recognizer's feedback, visual and linguistical information as clues to guide super-resolution.
EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classification
However, to the best of our knowledge, most existing methods consider only either the diversity or the quality of augmented data, thus cannot fully mine the potential of DA for NLP.
Recursive Fusion and Deformable Spatiotemporal Attention for Video Compression Artifact Reduction
A number of deep learning based algorithms have been proposed to recover high-quality videos from low-quality compressed ones.
Boosting the Performance of Video Compression Artifact Reduction with Reference Frame Proposals and Frequency Domain Information
Many deep learning based video compression artifact removal algorithms have been proposed to recover high-quality videos from low-quality compressed videos.
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.
