1

Space-Time Guided Association Learning For Unsupervised Person Re-Identification

Person re-identification (Re-ID) aims to match images of the same person across distinct camera views. In this paper, we propose the Space-Time Guided Association Learning (STGAL) for unsupervised Re-ID without ground truth identity nor image …

Orientation-aware Vehicle Re-identification with Semantics-guided Part Attention Network

Vehicle re-identification (re-ID) focuses on matching images of the same vehicle across different cameras. It is fundamentally challenging because differences between vehicles are sometimes subtle. While several studies incorporate spatial-attention …

Constraint-Aware Importance Estimation for Global Filter Pruning Under Multiple Resource Constraints

Filter pruning is an efficient way to structurally remove the redundant parameters in convolutional neural network, where at the same time reduces the computation, memory storage and transfer cost. Recent state-of-the-art methods globally estimate …

Spatially and Temporally Efficient Non-local Attention Network for Video-based Person Re-Identification

Video-based person re-identification (Re-ID) aims at matching video sequences of pedestrians across non-overlapping cameras. It is a practical yet challenging task of how to embed spatial and temporal information of a video into its feature …

Supervised Joint Domain Learning for Vehicle Re-Identification

in conjunction with 2019 NVIDIA AI City Challenge

Vehicle Re-identification with the Space-Time Prior

Vehicle re-identification (Re-ID) is fundamentally challenging due to the difficulties in data labeling, visual domain mismatch between datasets and diverse appearance of the same vehicle. We propose the adaptive feature learning technique based on …

Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal

Deep Convolutional Neural Networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many kernels to extract the knowledge behind it. However, with the depth of convolutional layers …