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Found 12 papers, 1 papers with code
ON THE USE OF CONVOLUTIONAL AUTO-ENCODER FOR INCREMENTAL CLASSIFIER LEARNING IN CONTEXT AWARE ADVERTISEMENT
This paper focuses on classifying images displayed on the websites by incremental learning classifier with Deep Convolutional Neural Network (DCNN) especially for Context Aware Advertisement (CAA) framework.
Spatiotemporal Prediction of Secondary Crashes by Rebalancing Dynamic and Static Data with Generative Adversarial Networks
In addition, the model's prediction module achieves simultaneous prediction of both the occurrence and spatiotemporal distribution of secondary crashes.
Traj-Explainer: An Explainable and Robust Multi-modal Trajectory Prediction Approach
Navigating complex traffic environments has been significantly enhanced by advancements in intelligent technologies, enabling accurate environment perception and trajectory prediction for automated vehicles.
Kaninfradet3D:A Road-side Camera-LiDAR Fusion 3D Perception Model based on Nonlinear Feature Extraction and Intrinsic Correlation
Both the camera and the LiDAR provide high-dimensional information, and employing KANs should enhance the extraction of valuable features to produce better fusion outcomes.
PEVA-Net: Prompt-Enhanced View Aggregation Network for Zero/Few-Shot Multi-View 3D Shape Recognition
Specifically, leveraging the descriptor which is effective for zero-shot inference to guide the tuning of the aggregated descriptor under the few-shot training can significantly improve the few-shot learning efficacy.
Exploiting Low-level Representations for Ultra-Fast Road Segmentation
The spatial detail branch is firstly designed to extract low-level feature representation for the road by the first stage of ResNet-18.
SCA-PVNet: Self-and-Cross Attention Based Aggregation of Point Cloud and Multi-View for 3D Object Retrieval
With deep features extracted from point clouds and multi-view images, we design two types of feature aggregation modules, namely the In-Modality Aggregation Module (IMAM) and the Cross-Modality Aggregation Module (CMAM), for effective feature fusion.
UDC 2020 Challenge on Image Restoration of Under-Display Camera: Methods and Results
The results in the paper are state-of-the-art restoration performance of Under-Display Camera Restoration.
Few-Shot Defect Segmentation Leveraging Abundant Normal Training Samples Through Normal Background Regularization and Crop-and-Paste Operation
In industrial inspection tasks, it is common to capture abundant defect-free image samples but very limited anomalous ones.
DDR-ID: Dual Deep Reconstruction Networks Based Image Decomposition for Anomaly Detection
One pivot challenge for image anomaly (AD) detection is to learn discriminative information only from normal class training images.
Machine Learning and Control Theory
Conversely Machine Learning can be used to solve large control problems.
FaultNet: Faulty Rail-Valves Detection using Deep Learning and Computer Vision
In this paper, a multi-phase deep learning based technique is proposed to perform accurate fault detection of rail-valves.
