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Found 34 papers, 12 papers with code
Attentive Contextual Attention for Cloud Removal
By integrating the AC-Attention module into the DSen2-CR cloud removal framework, we significantly improve the model's ability to capture essential distant information, leading to more effective cloud removal.
Semantic-aware Representation Learning for Homography Estimation
In our work, we seek another way to use the semantic information, that is semantic-aware feature representation learning framework. Based on this, we propose SRMatcher, a new detector-free feature matching method, which encourages the network to learn integrated semantic feature representation. Specifically, to capture precise and rich semantics, we leverage the capabilities of recently popularized vision foundation models (VFMs) trained on extensive datasets.
Camera-aware Label Refinement for Unsupervised Person Re-identification
Unsupervised person re-identification aims to retrieve images of a specified person without identity labels.
Single-Shot and Multi-Shot Feature Learning for Multi-Object Tracking
In this paper, we propose a simple yet effective two-stage feature learning paradigm to jointly learn single-shot and multi-shot features for different targets, so as to achieve robust data association in the tracking process.
Understanding the Overfitting of the Episodic Meta-training
Specifically, we select the teacher model as the one with the best validation accuracy during meta-training and restrict the symmetric Kullback-Leibler (SKL) divergence between the output distribution of the linear classifier of the teacher model and that of the student model.
Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planning
Growing interest in autonomous driving (AD) and intelligent vehicles (IVs) is fueled by their promise for enhanced safety, efficiency, and economic benefits.
T-former: An Efficient Transformer for Image Inpainting
And based on this attention, a network called $T$-former is designed for image inpainting.
Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors
Our work is divided into 3 independent articles and the first part is a Survey of Surveys (SoS) for total technologies of AD and IVs that involves the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions.
Pseudo Labels Refinement with Intra-camera Similarity for Unsupervised Person Re-identification
Unsupervised person re-identification (Re-ID) aims to retrieve person images across cameras without any identity labels.
Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys
Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits.
Auxiliary Loss Reweighting for Image Inpainting
TPL and TSL are supersets of perceptual and style losses and release the auxiliary potential of standard perceptual and style losses.
Memory-Free Generative Replay For Class-Incremental Learning
With the absence of old task images, they often assume that old knowledge is well preserved if the classifier produces similar output on new images.
Teacher-Student Asynchronous Learning with Multi-Source Consistency for Facial Landmark Detection
The radical student uses multi-source supervision signals from the same task to update parameters, while the calm teacher uses a single-source supervision signal to update parameters.
End-to-End Multi-Object Tracking with Global Response Map
Most existing Multi-Object Tracking (MOT) approaches follow the Tracking-by-Detection paradigm and the data association framework where objects are firstly detected and then associated.
Meta Corrupted Pixels Mining for Medical Image Segmentation
However the training of deep neural network requires a large amount of samples with high-quality annotations.
STH: Spatio-Temporal Hybrid Convolution for Efficient Action Recognition
Such a design enables efficient spatio-temporal modeling and maintains a small model scale.
Multiple Object Tracking by Flowing and Fusing
Most of Multiple Object Tracking (MOT) approaches compute individual target features for two subtasks: estimating target-wise motions and conducting pair-wise Re-Identification (Re-ID).
Collaborative Attention Network for Person Re-identification
Jointly utilizing global and local features to improve model accuracy is becoming a popular approach for the person re-identification (ReID) problem, because previous works using global features alone have very limited capacity at extracting discriminative local patterns in the obtained feature representation.
Distributed Generative Adversarial Net
Recently the Generative Adversarial Network has become a hot topic.
Particle reconstruction of volumetric particle image velocimetry with strategy of machine learning
Three-dimensional particle reconstruction with limited two-dimensional projections is an under-determined inverse problem that the exact solution is often difficult to be obtained.
Frame-wise Motion and Appearance for Real-time Multiple Object Tracking
The main challenge of Multiple Object Tracking (MOT) is the efficiency in associating indefinite number of objects between video frames.
SE2Net: Siamese Edge-Enhancement Network for Salient Object Detection
In this paper, we propose a simple yet effective Siamese Edge-Enhancement Network (SE2Net) to preserve the edge structure for salient object detection.
Video Smoke Detection Based on Deep Saliency Network
The deep feature map is combined with the saliency map to predict the existence of smoke in an image.
Discriminative Feature Learning with Foreground Attention for Person Re-Identification
Specifically, a novel foreground attentive subnetwork is designed to drive the network's attention, in which a decoder network is used to reconstruct the binary mask by using a novel local regression loss function, and an encoder network is regularized by the decoder network to focus its attention on the foreground persons.
Deep Self-Paced Learning for Person Re-Identification
In this paper, we propose a novel deep self-paced learning (DSPL) algorithm to alleviate this problem, in which we apply a self-paced constraint and symmetric regularization to help the relative distance metric training the deep neural network, so as to learn the stable and discriminative features for person Re-ID.
Tracking Persons-of-Interest via Unsupervised Representation Adaptation
Multi-face tracking in unconstrained videos is a challenging problem as faces of one person often appear drastically different in multiple shots due to significant variations in scale, pose, expression, illumination, and make-up.
Domain Adaptation from Synthesis to Reality in Single-model Detector for Video Smoke Detection
The existed researches mainly work on the adaptation to samples extracted from original annotated samples.
Large Margin Learning in Set to Set Similarity Comparison for Person Re-identification
The class-identity term keeps the intra-class samples within each camera view gathering together, the relative distance term maximizes the distance between the intra-class class set and inter-class set across different camera views, and the regularization term smoothness the parameters of deep convolutional neural network (CNN).
Deep Ranking Model by Large Adaptive Margin Learning for Person Re-identification
In this paper, we present a novel deep ranking model with feature learning and fusion by learning a large adaptive margin between the intra-class distance and inter-class distance to solve the person re-identification problem.
Point to Set Similarity Based Deep Feature Learning for Person Re-Identification
One of the key issues for deep learning based person Re-ID is the selection of proper similarity comparison criteria, and the performance of learned features using existing criterion based on pairwise similarity is still limited, because only P2P distances are mostly considered.
Deep Domain Adaptation Based Video Smoke Detection using Synthetic Smoke Images
Considering that the appearance gap (dataset bias) between synthetic and real smoke images degrades significantly the performance of the trained model on the test set composed fully of real images, we build deep architectures based on domain adaptation to confuse the distributions of features extracted from synthetic and real smoke images.
Single Image Super Resolution - When Model Adaptation Matters
The internal contents of the low resolution input image is neglected with deep modeling despite the earlier works showing the power of using such internal priors.
Single Image Super-resolution via a Lightweight Residual Convolutional Neural Network
To tackle with the second problem, a lightweight CNN architecture which has carefully designed width, depth and skip connections was proposed.
Person Re-Identification by Multi-Channel Parts-Based CNN With Improved Triplet Loss Function
Person re-identification across cameras remains a very challenging problem, especially when there are no overlapping fields of view between cameras.
