Search Results for author: Yeying Jin

Found 13 papers, 9 papers with code

MoE-TinyMed: Mixture of Experts for Tiny Medical Large Vision-Language Models

1 code implementation16 Apr 2024 Songtao Jiang, Tuo Zheng, Yan Zhang, Yeying Jin, Zuozhu Liu

Mixture of Expert Tuning (MoE-Tuning) has effectively enhanced the performance of general MLLMs with fewer parameters, yet its application in resource-limited medical settings has not been fully explored.

Visual Question Answering (VQA)

Joint Visual and Text Prompting for Improved Object-Centric Perception with Multimodal Large Language Models

2 code implementations6 Apr 2024 Songtao Jiang, Yan Zhang, Chenyi Zhou, Yeying Jin, Yang Feng, Jian Wu, Zuozhu Liu

In this paper, we present a novel approach, Joint Visual and Text Prompting (VTPrompt), that employs fine-grained visual information to enhance the capability of MLLMs in VQA, especially for object-oriented perception.

Object Question Answering +1

How Powerful Potential of Attention on Image Restoration?

no code implementations15 Mar 2024 Cong Wang, Jinshan Pan, Yeying Jin, Liyan Wang, Wei Wang, Gang Fu, Wenqi Ren, Xiaochun Cao

Our designs provide a closer look at the attention mechanism and reveal that some simple operations can significantly affect the model performance.

Image Restoration

NightHaze: Nighttime Image Dehazing via Self-Prior Learning

no code implementations12 Mar 2024 Beibei Lin, Yeying Jin, Wending Yan, Wei Ye, Yuan Yuan, Robby T. Tan

By increasing the noise values to approach as high as the pixel intensity values of the glow and light effect blended images, our augmentation becomes severe, resulting in stronger priors.

Image Dehazing Image Enhancement

SeD: Semantic-Aware Discriminator for Image Super-Resolution

1 code implementation29 Feb 2024 Bingchen Li, Xin Li, Hanxin Zhu, Yeying Jin, Ruoyu Feng, Zhizheng Zhang, Zhibo Chen

In particular, one discriminator is utilized to enable the SR network to learn the distribution of real-world high-quality images in an adversarial training manner.

Image Super-Resolution

Polyp-DAM: Polyp segmentation via depth anything model

no code implementations3 Feb 2024 Zhuoran Zheng, Chen Wu, Wei Wang, Yeying Jin, Xiuyi Jia

In this paper, we unfold a new perspective on polyp segmentation modeling by leveraging the Depth Anything Model (DAM) to provide depth prior to polyp segmentation models.


NightRain: Nighttime Video Deraining via Adaptive-Rain-Removal and Adaptive-Correction

no code implementations1 Jan 2024 Beibei Lin, Yeying Jin, Wending Yan, Wei Ye, Yuan Yuan, Shunli Zhang, Robby Tan

However, the intricacies of the real world, particularly with the presence of light effects and low-light regions affected by noise, create significant domain gaps, hampering synthetic-trained models in removing rain streaks properly and leading to over-saturation and color shifts.

Rain Removal

Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution

1 code implementation3 Aug 2023 Yeying Jin, Beibei Lin, Wending Yan, Yuan Yuan, Wei Ye, Robby T. Tan

In this paper, we enhance the visibility from a single nighttime haze image by suppressing glow and enhancing low-light regions.

Estimating Reflectance Layer from A Single Image: Integrating Reflectance Guidance and Shadow/Specular Aware Learning

1 code implementation27 Nov 2022 Yeying Jin, Ruoteng Li, Wenhan Yang, Robby T. Tan

To further enforce the reflectance layer to be independent of shadows and specularities in the second-stage refinement, we introduce an S-Aware network that distinguishes the reflectance image from the input image.

highlight removal Intrinsic Image Decomposition +1

DeS3: Adaptive Attention-driven Self and Soft Shadow Removal using ViT Similarity

1 code implementation15 Nov 2022 Yeying Jin, Wei Ye, Wenhan Yang, Yuan Yuan, Robby T. Tan

Most existing methods rely on binary shadow masks, without considering the ambiguous boundaries of soft and self shadows.

Image Shadow Removal Shadow Removal

Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal

1 code implementation6 Oct 2022 Yeying Jin, Wending Yan, Wenhan Yang, Robby T. Tan

Few existing image defogging or dehazing methods consider dense and non-uniform particle distributions, which usually happen in smoke, dust and fog.

Image Dehazing Image Enhancement +3

Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects Suppression

1 code implementation21 Jul 2022 Yeying Jin, Wenhan Yang, Robby T. Tan

To address this problem, we need to suppress the light effects in bright regions while, at the same time, boosting the intensity of dark regions.

Hallucination Image Restoration +1

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