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Found 13 papers, 7 papers with code
GaitGCI: Generative Counterfactual Intervention for Gait Recognition
Gait is one of the most promising biometrics that aims to identify pedestrians from their walking patterns.
MetaGait: Learning to Learn an Omni Sample Adaptive Representation for Gait Recognition
Towards this goal, MetaGait injects meta-knowledge, which could guide the model to perceive sample-specific properties, into the calibration network of the attention mechanism to improve the adaptiveness from the omni-scale, omni-dimension, and omni-process perspectives.
Dissolving Is Amplifying: Towards Fine-Grained Anomaly Detection
Interestingly, apart from the remarkable image generation abilities of diffusion models, we observed that diffusion models can dissolve image details for a given image, resulting in generalized feature representations.
Ultra Fast Deep Lane Detection with Hybrid Anchor Driven Ordinal Classification
With the help of the anchor-driven representation, we then reformulate the lane detection task as an ordinal classification problem to get the coordinates of lanes.
Multitask Identity-Aware Image Steganography via Minimax Optimization
The key issue of the direct recognition is to preserve identity information of secret images into container images and make container images look similar to cover images at the same time.
VersatileGait: A Large-Scale Synthetic Gait Dataset Towards in-the-Wild Simulation
To diversify the extrinsic factors of gait, we build a complicated scene with a dense camera layout.
VersatileGait: A Large-Scale Synthetic Gait Dataset with Fine-GrainedAttributes and Complicated Scenarios
With the motivation of practical gait recognition applications, we propose to automatically create a large-scale synthetic gait dataset (called VersatileGait) by a game engine, which consists of around one million silhouette sequences of 11, 000 subjects with fine-grained attributes in various complicated scenarios.
FcaNet: Frequency Channel Attention Networks
With the proof, we naturally generalize the compression of the channel attention mechanism in the frequency domain and propose our method with multi-spectral channel attention, termed as FcaNet.
DRR4Covid: Learning Automated COVID-19 Infection Segmentation from Digitally Reconstructed Radiographs
The infection-aware DRR generator is able to produce DRRs with adjustable strength of radiological signs of COVID-19 infection, and generate pixel-level infection annotations that match the DRRs precisely.
Learning Diagnosis of COVID-19 from a Single Radiological Image
To address this problem, we explore the feasibility of learning deep models for COVID-19 diagnosis from a single radiological image by resorting to synthesizing diverse radiological images.
ACCL: Adversarial constrained-CNN loss for weakly supervised medical image segmentation
In the new paradigm, prior knowledge is encoded and depicted by reference masks, and is further employed to impose constraints on segmentation outputs through adversarial learning with reference masks.
A Survey on Deep Learning of Small Sample in Biomedical Image Analysis
In order to accelerate the clinical usage of biomedical image analysis based on deep learning techniques, we intentionally expand this survey to include the explanation methods for deep models that are important to clinical decision making.
SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications
Drones or general Unmanned Aerial Vehicles (UAVs), endowed with computer vision function by on-board cameras and embedded systems, have become popular in a wide range of applications.
