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Found 13 papers, 9 papers with code
MoE-TinyMed: Mixture of Experts for Tiny Medical Large Vision-Language Models
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.
Joint Visual and Text Prompting for Improved Object-Centric Perception with Multimodal Large Language Models
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.
How Powerful Potential of Attention on Image Restoration?
Our designs provide a closer look at the attention mechanism and reveal that some simple operations can significantly affect the model performance.
NightHaze: Nighttime Image Dehazing via Self-Prior Learning
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.
SeD: Semantic-Aware Discriminator for Image Super-Resolution
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.
Polyp-DAM: Polyp segmentation via depth anything model
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
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.
Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution
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
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.
DeS3: Adaptive Attention-driven Self and Soft Shadow Removal using ViT Similarity
Most existing methods rely on binary shadow masks, without considering the ambiguous boundaries of soft and self shadows.
Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal
Few existing image defogging or dehazing methods consider dense and non-uniform particle distributions, which usually happen in smoke, dust and fog.
Ranked #1 on
Image Dehazing
on O-Haze
Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects Suppression
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.
Ranked #23 on
Low-Light Image Enhancement
on LOL
DC-ShadowNet: Single-Image Hard and Soft Shadow Removal Using Unsupervised Domain-Classifier Guided Network
To address the problem, in this paper, we propose an unsupervised domain-classifier guided shadow removal network, DC-ShadowNet.
Ranked #2 on
Shadow Removal
on SRD
