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Found 30 papers, 15 papers with code
SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation
The Vision Transformer (ViT) has achieved notable success in computer vision, with its variants extensively validated across various downstream tasks, including semantic segmentation.
Degradation Oriented and Regularized Network for Blind Depth Super-Resolution
Recent RGB-guided depth super-resolution methods have achieved impressive performance under the assumption of fixed and known degradation (e. g., bicubic downsampling).
Efficient Semantic Segmentation via Lightweight Multiple-Information Interaction Network
Recently, the integration of the local modeling capabilities of Convolutional Neural Networks (CNNs) with the global dependency strengths of Transformers has created a sensation in the semantic segmentation community.
HazyDet: Open-source Benchmark for Drone-view Object Detection with Depth-cues in Hazy Scenes
By observing the significant variations in object scale and clarity under different depth and haze conditions, we designed a Depth Conditioned Detector (DeCoDet) to incorporate this prior knowledge.
ReviveDiff: A Universal Diffusion Model for Restoring Images in Adverse Weather Conditions
Images captured in challenging environments--such as nighttime, foggy, rainy weather, and underwater--often suffer from significant degradation, resulting in a substantial loss of visual quality.
Microscopic-Mamba: Revealing the Secrets of Microscopic Images with Just 4M Parameters
In the field of medical microscopic image classification (MIC), CNN-based and Transformer-based models have been extensively studied.
OCTAMamba: A State-Space Model Approach for Precision OCTA Vasculature Segmentation
Optical Coherence Tomography Angiography (OCTA) is a crucial imaging technique for visualizing retinal vasculature and diagnosing eye diseases such as diabetic retinopathy and glaucoma.
Attention-Guided Multi-scale Interaction Network for Face Super-Resolution
Recently, CNN and Transformer hybrid networks demonstrated excellent performance in face super-resolution (FSR) tasks.
HAFormer: Unleashing the Power of Hierarchy-Aware Features for Lightweight Semantic Segmentation
In this paper, we introduce HAFormer, a model that combines the hierarchical features extraction ability of CNNs with the global dependency modeling capability of Transformers to tackle lightweight semantic segmentation challenges.
Scene Prior Filtering for Depth Super-Resolution
Specifically, we design an All-in-one Prior Propagation that computes the similarity between multi-modal scene priors, i. e., RGB, normal, semantic, and depth, to reduce the texture interference.
Survey on Deep Face Restoration: From Non-blind to Blind and Beyond
We also discuss notable benchmarks commonly utilized in the field.
EWT: Efficient Wavelet-Transformer for Single Image Denoising
Transformer-based image denoising methods have achieved encouraging results in the past year.
PFT-SSR: Parallax Fusion Transformer for Stereo Image Super-Resolution
Stereo image super-resolution aims to boost the performance of image super-resolution by exploiting the supplementary information provided by binocular systems.
Lightweight Real-time Semantic Segmentation Network with Efficient Transformer and CNN
In the past decade, convolutional neural networks (CNNs) have shown prominence for semantic segmentation.
Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid Network
Lightweight image super-resolution aims to reconstruct high-resolution images from low-resolution images using low computational costs.
Cross-receptive Focused Inference Network for Lightweight Image Super-Resolution
Recently, Transformer-based methods have shown impressive performance in single image super-resolution (SISR) tasks due to the ability of global feature extraction.
Multiple Degradation and Reconstruction Network for Single Image Denoising via Knowledge Distillation
Single image denoising (SID) has achieved significant breakthroughs with the development of deep learning.
Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer
Single-image super-resolution (SISR) has achieved significant breakthroughs with the development of deep learning.
CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution
Then, we design an efficient Feature Refinement Module (FRM) to enhance the encoded features.
Feature Distillation Interaction Weighting Network for Lightweight Image Super-Resolution
Convolutional neural networks based single-image super-resolution (SISR) has made great progress in recent years.
A Systematic Survey of Deep Learning-based Single-Image Super-Resolution
This is an exhaustive survey of SISR, which can help researchers better understand SISR and inspire more exciting research in this field.
FBSNet: A Fast Bilateral Symmetrical Network for Real-Time Semantic Segmentation
Specifically, FBSNet employs a symmetrical encoder-decoder structure with two branches, semantic information branch and spatial detail branch.
Leaning Compact and Representative Features for Cross-Modality Person Re-Identification
This paper pays close attention to the cross-modality visible-infrared person re-identification (VI Re-ID) task, which aims to match pedestrian samples between visible and infrared modes.
Cross-Modality Person Re-identification
Knowledge Distillation
+2
Hierarchical Deep CNN Feature Set-Based Representation Learning for Robust Cross-Resolution Face Recognition
(i) To learn more robust and discriminative features, we desire to adaptively fuse the contextual features from different layers.
JDSR-GAN: Constructing An Efficient Joint Learning Network for Masked Face Super-Resolution
In this work, we treat the mask occlusion as image noise and construct a joint and collaborative learning network, called JDSR-GAN, for the masked face super-resolution task.
MSCFNet: A Lightweight Network With Multi-Scale Context Fusion for Real-Time Semantic Segmentation
In recent years, how to strike a good trade-off between accuracy and inference speed has become the core issue for real-time semantic segmentation applications, which plays a vital role in real-world scenarios such as autonomous driving systems and drones.
Lightweight Image Super-Resolution with Multi-scale Feature Interaction Network
Recently, the single image super-resolution (SISR) approaches with deep and complex convolutional neural network structures have achieved promising performance.
Cross-View Image Synthesis with Deformable Convolution and Attention Mechanism
This is even more laborious when generating images with very different views.
LEDNet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation
LEDNet: A Lightweight Encoder-Decoder Network for Real-time Semantic Segmentation
Ranked #30 on
Real-Time Semantic Segmentation
on Cityscapes test
