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Found 10 papers, 3 papers with code
NightHaze: Nighttime Image Dehazing via Self-Prior Learning
By increasing the noise values to approach as high as the pixel intensity values of the glow and light effect blended images, our augmentation becomes severe, resulting in stronger priors.
NightRain: Nighttime Video Deraining via Adaptive-Rain-Removal and Adaptive-Correction
However, the intricacies of the real world, particularly with the presence of light effects and low-light regions affected by noise, create significant domain gaps, hampering synthetic-trained models in removing rain streaks properly and leading to over-saturation and color shifts.
Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution
In this paper, we enhance the visibility from a single nighttime haze image by suppressing glow and enhancing low-light regions.
DyGait: Exploiting Dynamic Representations for High-performance Gait Recognition
This is the first framework on gait recognition that is designed to focus on the extraction of dynamic features.
Uncertainty-aware Gait Recognition via Learning from Dirichlet Distribution-based Evidence
Existing gait recognition frameworks retrieve an identity in the gallery based on the distance between a probe sample and the identities in the gallery.
GaitGL: Learning Discriminative Global-Local Feature Representations for Gait Recognition
GFR extractor aims to extract contextual information, e. g., the relationship among various body parts, and the mask-based LFR extractor is presented to exploit the detailed posture changes of local regions.
Gait Recognition in the Wild: A Large-scale Benchmark and NAS-based Baseline
To the best of our knowledge, this is the first large-scale dataset for gait recognition in the wild.
Gait Recognition with Mask-based Regularization
Also, we design an Inception-like ReverseMask Block, which has three branches composed of a global branch, a feature dropping branch, and a feature scaling branch.
Ranked #2 on
Gait Recognition
on OUMVLP
GaitStrip: Gait Recognition via Effective Strip-based Feature Representations and Multi-Level Framework
ECM consists of the Spatial-Temporal feature extractor (ST), the Frame-Level feature extractor (FL) and SPB, and has two obvious advantages: First, each branch focuses on a specific representation, which can be used to improve the robustness of the network.
Gait Recognition via Effective Global-Local Feature Representation and Local Temporal Aggregation
Towards this goal, we take advantage of both global visual information and local region details and develop a Global and Local Feature Extractor (GLFE).
