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Found 40 papers, 13 papers with code
SSL: A Self-similarity Loss for Improving Generative Image Super-resolution
Based on the fact that natural images exhibit high self-similarities, i. e., a local patch can have many similar patches to it in the whole image, in this work we propose a simple yet effective self-similarity loss (SSL) to improve the performance of generative Real-ISR models, enhancing the hallucination of structural and textural details while reducing the unpleasant visual artifacts.
LORTSAR: Low-Rank Transformer for Skeleton-based Action Recognition
The complexity of state-of-the-art Transformer-based models for skeleton-based action recognition poses significant challenges in terms of computational efficiency and resource utilization.
ALPHAGMUT: A Rationale-Guided Alpha Shape Graph Neural Network to Evaluate Mutation Effects
In this study, we introduce a novel rationale-guided graph neural network AlphaGMut to evaluate mutation effects and to distinguish pathogenic mutations from neutral mutations.
NTIRE 2024 Restore Any Image Model (RAIM) in the Wild Challenge
In this paper, we review the NTIRE 2024 challenge on Restore Any Image Model (RAIM) in the Wild.
When Fuzzing Meets LLMs: Challenges and Opportunities
Fuzzing, a widely-used technique for bug detection, has seen advancements through Large Language Models (LLMs).
A Comprehensive Study of Multimodal Large Language Models for Image Quality Assessment
While Multimodal Large Language Models (MLLMs) have experienced significant advancement in visual understanding and reasoning, their potential to serve as powerful, flexible, interpretable, and text-driven models for Image Quality Assessment (IQA) remains largely unexplored.
Improving the Stability and Efficiency of Diffusion Models for Content Consistent Super-Resolution
Specifically, we propose a non-uniform timestep sampling strategy in the first stage.
Perception-Distortion Balanced Super-Resolution: A Multi-Objective Optimization Perspective
High perceptual quality and low distortion degree are two important goals in image restoration tasks such as super-resolution (SR).
Guided Image Restoration via Simultaneous Feature and Image Guided Fusion
Currently, joint image filtering-inspired deep learning-based methods represent the state-of-the-art for GIR tasks.
MRGazer: Decoding Eye Gaze Points from Functional Magnetic Resonance Imaging in Individual Space
Compared to the previous method, the proposed framework skips the fMRI co-registration step, simplifies the processing protocol and achieves end-to-end eye gaze regression.
Fast and High-Performance Learned Image Compression With Improved Checkerboard Context Model, Deformable Residual Module, and Knowledge Distillation
Then we only encode non-zero channels in the encoding and decoding process, which can greatly reduce the encoding and decoding time.
Enhanced Residual SwinV2 Transformer for Learned Image Compression
In particular, our method achieves comparable results while reducing model complexity by 56% compared to these recent methods.
ROI-based Deep Image Compression with Swin Transformers
Encoding the Region Of Interest (ROI) with better quality than the background has many applications including video conferencing systems, video surveillance and object-oriented vision tasks.
Human Guided Ground-truth Generation for Realistic Image Super-resolution
A human guided GT image dataset with both positive and negative samples is then constructed, and a loss function is proposed to train the Real-ISR models.
Asymmetric Learned Image Compression with Multi-Scale Residual Block, Importance Map, and Post-Quantization Filtering
Based on this observation, we design an asymmetric paradigm, in which the encoder employs three stages of MSRBs to improve the learning capacity, whereas the decoder only needs one stage of MSRB to yield satisfactory reconstruction, thereby reducing the decoding complexity without sacrifcing performance.
Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution
Specifically, a tiny regression network is employed to predict the degradation parameters of the input image, while several convolutional experts with the same topology are jointly optimized to specify the network parameters via a non-linear mixture of experts.
Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution
In this paper, we demonstrate that it is possible to train a GAN-based SISR model which can stably generate perceptually realistic details while inhibiting visual artifacts.
SeqMapPDB: A Standalone Pipeline to Identify Representative Structures of Protein Sequences and Mapping Residue Indices in Real-Time at Proteome Scale
Furthermore, SeqMapPDB provides the flexibility of a stand-alone pipeline for large scale mapping of in-house sequence and structure data.
Auto-Weighted Layer Representation Based View Synthesis Distortion Estimation for 3-D Video Coding
Experimental results show that the VSD can be accurately estimated with the weights learnt by the nonlinear mapping function once its associated S-VSDs are available.
Learned Image Compression with Gaussian-Laplacian-Logistic Mixture Model and Concatenated Residual Modules
However, due to the vast diversity of images, it is not optimal to use one model for all images, even different regions within one image.
PPR10K: A Large-Scale Portrait Photo Retouching Dataset with Human-Region Mask and Group-Level Consistency
HRP requires that more attention should be paid to human regions, while GLC requires that a group of portrait photos should be retouched to a consistent tone.
High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network
Existing image-to-image translation (I2IT) methods are either constrained to low-resolution images or long inference time due to their heavy computational burden on the convolution of high-resolution feature maps.
Ranked #1 on
Photo Retouching
on MIT-Adobe 5k (1080p)
Exact Probability Landscapes of Stochastic Phenotype Switching in Feed-Forward Loops: Phase Diagrams of Multimodality
Feed-forward loops (FFLs) are among the most ubiquitously found motifs of reaction networks in nature.
Exact Topology of Dynamic Probability Surface of an Activated Process by Persistent Homology
To gain insight into reaction mechanism of activated processes, we introduce an exact approach for quantifying the topology of high-dimensional probability surfaces of the underlying dynamic processes.
Mathematical Physics Mathematical Physics
Learned Multi-Resolution Variable-Rate Image Compression with Octave-based Residual Blocks
Recently deep learning-based image compression has shown the potential to outperform traditional codecs.
A Multi-task Joint Framework for Real-time Person Search
To solve these problems, we propose a Multi-task Joint Framework for real-time person search (MJF), which optimizes the person detection, feature extraction and identity comparison respectively.
Generalized Octave Convolutions for Learned Multi-Frequency Image Compression
Learned image compression has recently shown the potential to outperform the standard codecs.
Deep Learning-based Image Compression with Trellis Coded Quantization
Recently many works attempt to develop image compression models based on deep learning architectures, where the uniform scalar quantizer (SQ) is commonly applied to the feature maps between the encoder and decoder.
Learned Variable-Rate Image Compression with Residual Divisive Normalization
Recently it has been shown that deep learning-based image compression has shown the potential to outperform traditional codecs.
Improved Hybrid Layered Image Compression using Deep Learning and Traditional Codecs
Recently deep learning-based methods have been applied in image compression and achieved many promising results.
Simultaneous Subspace Clustering and Cluster Number Estimating based on Triplet Relationship
In this paper we propose a unified framework to simultaneously discover the number of clusters and group the data points into them using subspace clustering.
Sub-GAN: An Unsupervised Generative Model via Subspaces
The recent years have witnessed significant growth in constructing robust generative models to capture informative distributions of natural data.
EnFuzz: From Ensemble Learning to Ensemble Fuzzing
For evaluation, we implement EnFuzz , a prototype basing on four strong open-source fuzzers (AFL, AFLFast, AFLGo, FairFuzz), and test them on Google's fuzzing test suite, which consists of widely used real-world applications.
Software Engineering
DSSLIC: Deep Semantic Segmentation-based Layered Image Compression
A compact representation of the input image is also generated and encoded as the first enhancement layer.
Semi-Recurrent CNN-based VAE-GAN for Sequential Data Generation
A semi-recurrent hybrid VAE-GAN model for generating sequential data is introduced.
Clinical Skin Lesion Diagnosis Using Representations Inspired by Dermatologist Criteria
Accordingly, we design six medical representations considering different criteria for the recognition of skin lesions, and construct a diagnosis system for clinical skin disease images.
Simultaneously Color-Depth Super-Resolution with Conditional Generative Adversarial Network
Firstly, given the low-resolution depth image and low-resolution color image, a generative network is proposed to leverage mutual information of color image and depth image to enhance each other in consideration of the geometry structural dependency of color-depth image in the same scene.
Local Activity-tuned Image Filtering for Noise Removal and Image Smoothing
Both frameworks employ the division of gradient and the local activity measurement to achieve noise removal.
Scalable Compression of Deep Neural Networks
Deep neural networks generally involve some layers with mil- lions of parameters, making them difficult to be deployed and updated on devices with limited resources such as mobile phones and other smart embedded systems.
On the Sampling Strategy for Evaluation of Spectral-spatial Methods in Hyperspectral Image Classification
Spectral-spatial processing has been increasingly explored in remote sensing hyperspectral image classification.
