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Found 20 papers, 6 papers with code
Diverse Representation Embedding for Lifelong Person Re-Identification
KU strategy enhances the adaptive learning ability of learner models for new information under the adjustment model prior, and KP strategy preserves old knowledge operated by representation-level alignment and logit-level supervision in limited old task datasets while guaranteeing the adaptive learning information capacity of the LReID model.
Low-Tubal-Rank Tensor Recovery via Factorized Gradient Descent
This paper considers the problem of recovering a tensor with an underlying low-tubal-rank structure from a small number of corrupted linear measurements.
Residual Denoising Diffusion Models
We propose residual denoising diffusion models (RDDM), a novel dual diffusion process that decouples the traditional single denoising diffusion process into residual diffusion and noise diffusion.
A Shadow Imaging Bilinear Model and Three-branch Residual Network for Shadow Removal
Thus, our network ensures the fidelity of nonshadow areas and restores the light intensity of shadow areas through three-branch collaboration.
Review helps learn better: Temporal Supervised Knowledge Distillation
Reviewing plays an important role when learning knowledge.
A Decoupled Multi-Task Network for Shadow Removal
Last, these features are converted to a target shadow-free image, affiliated shadow matte, and shadow image, supervised by multi-task joint loss functions.
CCR: Facial Image Editing with Continuity, Consistency and Reversibility
In this work, we put forward three concepts and corresponding definitions: editing continuity, consistency, and reversibility.
Effective Tensor Completion via Element-wise Weighted Low-rank Tensor Train with Overlapping Ket Augmentation
Accordingly, a novel formulation for tensor completion and an effective optimization algorithm, called as tensor completion by parallel weighted matrix factorization via tensor train (TWMac-TT), is proposed.
Local Label Point Correction for Edge Detection of Overlapping Cervical Cells
The qualitative and quantitative experimental results show that our LLPC can improve the quality of manual labels and the accuracy of overlapping cell edge detection.
Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets
State-of-the-art multi-object tracking~(MOT) methods follow the tracking-by-detection paradigm, where object trajectories are obtained by associating per-frame outputs of object detectors.
One Point, One Object: Simultaneous 3D Object Segmentation and 6-DOF Pose Estimation
Due to the lack of training data for many objects, the recently proposed 2D detection methods try to generate training data by using rendering engine and achieve good results.
Dynamic Spatio-temporal Graph-based CNNs for Traffic Prediction
To capture the graph dynamics, we use the graph prediction stream to predict the dynamic graph structures, and the predicted structures are fed into the flow prediction stream.
Fusing Crowd Density Maps and Visual Object Trackers for People Tracking in Crowd Scenes
While people tracking has been greatly improved over the recent years, crowd scenes remain particularly challenging for people tracking due to heavy occlusions, high crowd density, and significant appearance variation.
Tensor RPCA by Bayesian CP Factorization With Complex Noise
In this paper, we propose a tensor RPCA model based on CP decomposition and model data noise by Mixture of Gaussians (MoG).
DeshadowNet: A Multi-Context Embedding Deep Network for Shadow Removal
Two levels of features are derived from the global network and transferred to two parallel networks.
Video Desnowing and Deraining Based on Matrix Decomposition
The existing snow/rain removal methods often fail for heavy snow/rain and dynamic scene.
A General Model for Robust Tensor Factorization with Unknown Noise
We provide two versions of the algorithm with different tensor factorization operations, i. e., CP factorization and Tucker factorization.
RGBD Salient Object Detection via Deep Fusion
Numerous efforts have been made to design different low level saliency cues for the RGBD saliency detection, such as color or depth contrast features, background and color compactness priors.
Ranked #25 on
RGB-D Salient Object Detection
on NJU2K
Robust Tensor Factorization With Unknown Noise
However, real data are often corrupted by noise with an unknown distribution.
Pixel-wise Orthogonal Decomposition for Color Illumination Invariant and Shadow-free Image
In this paper, we propose a novel, effective and fast method to obtain a color illumination invariant and shadow-free image from a single outdoor image.
