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Found 22 papers, 5 papers with code
Correlation Preserving Sparse Coding Over Multi-level Dictionaries for Image Denoising
In this letter, we propose a novel image denoising method based on correlation preserving sparse coding.
Blind restoration for non-uniform aerial images using non-local Retinex model and shearlet-based higher-order regularization
Aerial images are often degraded by space-varying motion blur and simultaneous uneven illumination.
Learning a collaborative multiscale dictionary based on robust empirical mode decomposition
The multiscale dictionary is considered as the product of oscillating dictionary and tolerance dictionary.
Bayer Demosaicking Using Optimized Mean Curvature over RGB channels
Color artifacts of demosaicked images are often found at contours due to interpolation across edges and cross-channel aliasing.
Noise Level Estimation for Overcomplete Dictionary Learning Based on Tight Asymptotic Bounds
In this letter, we address the problem of estimating Gaussian noise level from the trained dictionaries in update stage.
Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking
In this paper, we propose a novel tracklet processing method to cleave and re-connect tracklets on crowd or long-term occlusion by Siamese Bi-Gated Recurrent Unit (GRU).
Ranked #20 on
Multi-Object Tracking
on MOT16
Attention Driven Person Re-identification
Person re-identification (ReID) is a challenging task due to arbitrary human pose variations, background clutters, etc.
Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior
Blind image deblurring is a challenging problem in computer vision, which aims to restore both the blur kernel and the latent sharp image from only a blurry observation.
FFA-Net: Feature Fusion Attention Network for Single Image Dehazing
The FFA-Net architecture consists of three key components: 1) A novel Feature Attention (FA) module combines Channel Attention with Pixel Attention mechanism, considering that different channel-wise features contain totally different weighted information and haze distribution is uneven on the different image pixels.
Ranked #1 on
Image Dehazing
on KITTI
Correlating Edge, Pose with Parsing
Compared with the existing practice of feature concatenation, we find that uncovering the correlation among the three factors is a superior way of leveraging the pivotal contextual cues provided by edges and poses.
SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising With Self-Supervised Perceptual Loss Network
We combine these two methods and demonstrate their effectiveness on both CNN-based neural networks and WGAN-based neural networks with comprehensive experiments.
The Layout Generation Algorithm of Graphic Design Based on Transformer-CVAE
This paper implemented the Transformer model and conditional variational autoencoder (CVAE) to the graphic design layout generation task.
BBA-net: A bi-branch attention network for crowd counting
Our method can integrate the pedestrian's head and body information to enhance the feature expression ability of the density map.
Enhancing and Dissecting Crowd Counting By Synthetic Data
In this article, we propose a simulated crowd counting dataset CrowdX, which has a large scale, accurate labeling, parameterized realization, and high fidelity.
Open-Vocabulary 3D Detection via Image-level Class and Debiased Cross-modal Contrastive Learning
Current point-cloud detection methods have difficulty detecting the open-vocabulary objects in the real world, due to their limited generalization capability.
Multi-Agent Automated Machine Learning
In this paper, we propose multi-agent automated machine learning (MA2ML) with the aim to effectively handle joint optimization of modules in automated machine learning (AutoML).
Towards Blind Watermarking: Combining Invertible and Non-invertible Mechanisms
The CIN is composed of the invertible part to achieve high imperceptibility and the non-invertible part to strengthen the robustness against strong noise attacks.
Open-Vocabulary Point-Cloud Object Detection without 3D Annotation
In this paper, we address open-vocabulary 3D point-cloud detection by a dividing-and-conquering strategy, which involves: 1) developing a point-cloud detector that can learn a general representation for localizing various objects, and 2) connecting textual and point-cloud representations to enable the detector to classify novel object categories based on text prompting.
PM-DETR: Domain Adaptive Prompt Memory for Object Detection with Transformers
PDM comprehensively leverages the prompt memory to extract domain-specific knowledge and explicitly constructs a long-term memory space for the data distribution, which represents better domain diversity compared to existing methods.
COLE: A Hierarchical Generation Framework for Multi-Layered and Editable Graphic Design
Our COLE system comprises multiple fine-tuned Large Language Models (LLMs), Large Multimodal Models (LMMs), and Diffusion Models (DMs), each specifically tailored for design-aware layer-wise captioning, layout planning, reasoning, and the task of generating images and text.
FM-OV3D: Foundation Model-based Cross-modal Knowledge Blending for Open-Vocabulary 3D Detection
In this work, we propose FM-OV3D, a method of Foundation Model-based Cross-modal Knowledge Blending for Open-Vocabulary 3D Detection, which improves the open-vocabulary localization and recognition abilities of 3D model by blending knowledge from multiple pre-trained foundation models, achieving true open-vocabulary without facing constraints from original 3D datasets.
A Dataset and Benchmark for Copyright Protection from Text-to-Image Diffusion Models
Copyright is a legal right that grants creators the exclusive authority to reproduce, distribute, and profit from their creative works.
