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Found 7 papers, 1 papers with code
Lightweight Delivery Detection on Doorbell Cameras
Despite recent advances in video-based action recognition and robust spatio-temporal modeling, most of the proposed approaches rely on the abundance of computational resources to afford running huge and computation-intensive convolutional or transformer-based neural networks to obtain satisfactory results.
Scalable Vehicle Re-Identification via Self-Supervision
As Computer Vision technologies become more mature for intelligent transportation applications, it is time to ask how efficient and scalable they are for large-scale and real-time deployment.
Scalable and Real-time Multi-Camera Vehicle Detection, Re-Identification, and Tracking
Multi-camera vehicle tracking is one of the most complicated tasks in Computer Vision as it involves distinct tasks including Vehicle Detection, Tracking, and Re-identification.
Identification of Attack-Specific Signatures in Adversarial Examples
The adversarial attack literature contains a myriad of algorithms for crafting perturbations which yield pathological behavior in neural networks.
GANs with Variational Entropy Regularizers: Applications in Mitigating the Mode-Collapse Issue
To tackle this issue, we take an information-theoretic approach and maximize a variational lower bound on the entropy of the generated samples to increase their diversity.
The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identification
In recent years, the research community has approached the problem of vehicle re-identification (re-id) with attention-based models, specifically focusing on regions of a vehicle containing discriminative information.
A Dual-Path Model With Adaptive Attention For Vehicle Re-Identification
In this paper, we present a novel dual-path adaptive attention model for vehicle re-identification (AAVER).
Vehicle Key-Point and Orientation Estimation
Vehicle Re-Identification
