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Found 13 papers, 8 papers with code
It Takes Two: Accurate Gait Recognition in the Wild via Cross-granularity Alignment
In particular, the GCM aims to enhance the quality of parsing features by leveraging global features from silhouettes, while the PCM aligns the dynamics of human parts between silhouette and parsing features using the high information entropy in parsing sequences.
Quality-aware Selective Fusion Network for V-D-T Salient Object Detection
Therefore, we propose a quality-aware selective fusion network (QSF-Net) to conduct VDT salient object detection, which contains three subnets including the initial feature extraction subnet, the quality-aware region selection subnet, and the region-guided selective fusion subnet.
Gait Recognition in the Wild with Multi-hop Temporal Switch
Current methods that obtain state-of-the-art performance on in-the-lab benchmarks achieve much worse accuracy on the recently proposed in-the-wild datasets because these methods can hardly model the varied temporal dynamics of gait sequences in unconstrained scenes.
TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition
Despite significant improvement in gait recognition with deep learning, existing studies still neglect a more practical but challenging scenario -- unsupervised cross-domain gait recognition which aims to learn a model on a labeled dataset then adapts it to an unlabeled dataset.
Evolutionary Multi-objective Architecture Search Framework: Application to COVID-19 3D CT Classification
We propose a new objective, namely potential, which can help exploit promising models to indirectly reduce the number of models involved in weights training, thus alleviating search instability.
Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans
The experimental results show that our automatically searched models (CovidNet3D) outperform the baseline human-designed models on the three datasets with tens of times smaller model size and higher accuracy.
AdvExpander: Generating Natural Language Adversarial Examples by Expanding Text
In this paper, we present AdvExpander, a method that crafts new adversarial examples by expanding text, which is complementary to previous substitution-based methods.
Lane detection in complex scenes based on end-to-end neural network
So we combine the advantages of spatial convolution in spatial information processing and the efficiency of ERFNet in semantic segmentation, propose an end-to-end network to lane detection in a variety of complex scenes.
Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization
Existing methods usually concentrate on mining the fine-grained feature of the geographic target in the image center, but underestimate the contextual information in neighbor areas.
Ranked #3 on
Drone navigation
on University-1652
Lane Detection Model Based on Spatio-Temporal Network With Double Convolutional Gated Recurrent Units
One is used to extract the information of the most likely low-level features of lane markings.
A Biologically Inspired Feature Enhancement Framework for Zero-Shot Learning
Most of the Zero-Shot Learning (ZSL) algorithms currently use pre-trained models as their feature extractors, which are usually trained on the ImageNet data set by using deep neural networks.
Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue Systems
Natural language generation (NLG) is an essential component of task-oriented dialogue systems.
3D Graph Convolutional Networks with Temporal Graphs: A Spatial Information Free Framework For Traffic Forecasting
(2) We propose an original 3D graph convolution model to model the spatio-temporal data more accurately.
Ranked #2 on
Traffic Prediction
on PeMS-M
