Search Results for author:
Found 37 papers, 21 papers with code
Learning Heavily-Degraded Prior for Underwater Object Detection
Therefore, we propose a residual feature transference module (RFTM) to learn a mapping between deep representations of the heavily degraded patches of DFUI- and underwater- images, and make the mapping as a heavily degraded prior (HDP) for underwater detection.
Bilevel Generative Learning for Low-Light Vision
In this study, we propose a generic low-light vision solution by introducing a generative block to convert data from the RAW to the RGB domain.
Multi-interactive Feature Learning and a Full-time Multi-modality Benchmark for Image Fusion and Segmentation
Multi-modality image fusion and segmentation play a vital role in autonomous driving and robotic operation.
Ranked #3 on
Semantic Segmentation
on FMB Dataset
DPM-OT: A New Diffusion Probabilistic Model Based on Optimal Transport
Sampling from diffusion probabilistic models (DPMs) can be viewed as a piecewise distribution transformation, which generally requires hundreds or thousands of steps of the inverse diffusion trajectory to get a high-quality image.
OT-Net: A Reusable Neural Optimal Transport Solver
With the widespread application of optimal transport (OT), its calculation becomes essential, and various algorithms have emerged.
A Task-guided, Implicitly-searched and Meta-initialized Deep Model for Image Fusion
Qualitative and quantitative experimental results on different categories of image fusion problems and related downstream tasks (e. g., visual enhancement and semantic understanding) substantiate the flexibility and effectiveness of our TIM.
Practical Exposure Correction: Great Truths Are Always Simple
Improving the visual quality of the given degraded observation by correcting exposure level is a fundamental task in the computer vision community.
CoCoNet: Coupled Contrastive Learning Network with Multi-level Feature Ensemble for Multi-modality Image Fusion
Infrared and visible image fusion targets to provide an informative image by combining complementary information from different sensors.
Toward Fast, Flexible, and Robust Low-Light Image Enhancement
Existing low-light image enhancement techniques are mostly not only difficult to deal with both visual quality and computational efficiency but also commonly invalid in unknown complex scenarios.
Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection
This study addresses the issue of fusing infrared and visible images that appear differently for object detection.
Ranked #1 on
Object Detection
on Multispectral Dataset
Automated Learning for Deformable Medical Image Registration by Jointly Optimizing Network Architectures and Objective Functions
Deformable image registration plays a critical role in various tasks of medical image analysis.
Segment, Magnify and Reiterate: Detecting Camouflaged Objects the Hard Way
To tackle camouflaged object detection (COD), we are inspired by humans attention coupled with the coarse-to-fine detection strategy, and thereby propose an iterative refinement framework, coined SegMaR, which integrates Segment, Magnify and Reiterate in a multi-stage detection fashion.
Learning with Nested Scene Modeling and Cooperative Architecture Search for Low-Light Vision
To partially address above issues, we establish Retinex-inspired Unrolling with Architecture Search (RUAS), a general learning framework, which not only can address low-light enhancement task, but also has the flexibility to handle other more challenging downstream vision applications.
Learning Deep Context-Sensitive Decomposition for Low-Light Image Enhancement
Further, by sharing an encoder for these two components, we obtain a more lightweight version (SLiteCSDNet for short).
An Underwater Image Semantic Segmentation Method Focusing on Boundaries and a Real Underwater Scene Semantic Segmentation Dataset
Based on this dataset, we propose a semi-supervised underwater semantic segmentation network focusing on the boundaries(US-Net: Underwater Segmentation Network).
Dynamic Context-Sensitive Filtering Network for Video Salient Object Detection
Our bidirectional dynamic fusion strategy encourages the interaction of spatial and temporal information in a dynamic manner.
Ranked #12 on
Video Polyp Segmentation
on SUN-SEG-Easy (Unseen)
Optimization-Inspired Learning with Architecture Augmentations and Control Mechanisms for Low-Level Vision
Specifically, by introducing a general energy minimization model and formulating its descent direction from different viewpoints (i. e., in a generative manner, based on the discriminative metric and with optimality-based correction), we construct three propagative modules to effectively solve the optimization models with flexible combinations.
Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement
Low-light image enhancement plays very important roles in low-level vision field.
AE-OT: A NEW GENERATIVE MODEL BASED ON EXTENDED SEMI-DISCRETE OPTIMAL TRANSPORT
In order to tackle the both problems, we explicitly separate the manifold embedding and the optimal transportation; the first part is carried out using an autoencoder to map the images onto the latent space; the second part is accomplished using a GPU-based convex optimization to find the discontinuous transportation maps.
Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond
We design a new deep learning based framework to optimize a diffeomorphic model via multi-scale propagation in order to integrate advantages and avoid limitations of these two categories of approaches.
Converged Deep Framework Assembling Principled Modules for CS-MRI
Compressed Sensing Magnetic Resonance Imaging (CS-MRI) significantly accelerates MR data acquisition at a sampling rate much lower than the Nyquist criterion.
Investigating Task-driven Latent Feasibility for Nonconvex Image Modeling
Properly modeling latent image distributions plays an important role in a variety of image-related vision problems.
Underexposed Image Correction via Hybrid Priors Navigated Deep Propagation
Plenty of experimental results of underexposed image correction demonstrate that our proposed method performs favorably against the state-of-the-art methods on both subjective and objective assessments.
Mode Collapse and Regularity of Optimal Transportation Maps
This work builds the connection between the regularity theory of optimal transportation map, Monge-Amp\`{e}re equation and GANs, which gives a theoretic understanding of the major drawbacks of GANs: convergence difficulty and mode collapse.
Real-world Underwater Enhancement: Challenges, Benchmarks, and Solutions
Underwater image enhancement is such an important low-level vision task with many applications that numerous algorithms have been proposed in recent years.
A Bridging Framework for Model Optimization and Deep Propagation
Different from these existing network based iterations, which often lack theoretical investigations, we provide strict convergence analysis for PODM in the challenging nonconvex and nonsmooth scenarios.
A Theoretically Guaranteed Deep Optimization Framework for Robust Compressive Sensing MRI
Magnetic Resonance Imaging (MRI) is one of the most dynamic and safe imaging techniques available for clinical applications.
On the Convergence of Learning-based Iterative Methods for Nonconvex Inverse Problems
Moreover, there is a lack of rigorous analysis about the convergence behaviors of these reimplemented iterations, and thus the significance of such methods is a little bit vague.
User-Guided Deep Anime Line Art Colorization with Conditional Adversarial Networks
Scribble colors based line art colorization is a challenging computer vision problem since neither greyscale values nor semantic information is presented in line arts, and the lack of authentic illustration-line art training pairs also increases difficulty of model generalization.
Learning Collaborative Generation Correction Modules for Blind Image Deblurring and Beyond
Blind image deblurring plays a very important role in many vision and multimedia applications.
A Single Shot Text Detector with Scale-adaptive Anchors
Currently, most top-performing text detection networks tend to employ fixed-size anchor boxes to guide the search for text instances.
Geometric Understanding of Deep Learning
In this work, we give a geometric view to understand deep learning: we show that the fundamental principle attributing to the success is the manifold structure in data, namely natural high dimensional data concentrates close to a low-dimensional manifold, deep learning learns the manifold and the probability distribution on it.
Toward Designing Convergent Deep Operator Splitting Methods for Task-specific Nonconvex Optimization
Operator splitting methods have been successfully used in computational sciences, statistics, learning and vision areas to reduce complex problems into a series of simpler subproblems.
Self-Reinforced Cascaded Regression for Face Alignment
Cascaded regression is prevailing in face alignment thanks to its accuracy and robustness, but typically demands manually annotated examples having low discrepancy between shape-indexed features and shape updates.
Proximal Alternating Direction Network: A Globally Converged Deep Unrolling Framework
Deep learning models have gained great success in many real-world applications.
Learning Aggregated Transmission Propagation Networks for Haze Removal and Beyond
However, they may fail when their assumptions are not valid on specific images.
A Fast Ellipse Detector Using Projective Invariant Pruning
Detecting elliptical objects from an image is a central task in robot navigation and industrial diagnosis where the detection time is always a critical issue.
