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Found 9 papers, 5 papers with code
SalM$2$: An Extremely Lightweight Saliency Mamba Model for Real-Time Cognitive Awareness of Driver Attention
Driver attention recognition in driving scenarios is a popular direction in traffic scene perception technology.
ConSense: Continually Sensing Human Activity with WiFi via Growing and Picking
The framework leverages the transformer architecture and involves dynamic model expansion and selective retraining to preserve previously learned knowledge while integrating new information.
AIM 2024 Challenge on Video Saliency Prediction: Methods and Results
The goal of the participants was to develop a method for predicting accurate saliency maps for the provided set of video sequences.
Rethinking the Detection Head Configuration for Traffic Object Detection
The first is the detection head and object distribution matching strategy, which guides the rational configuration of detection head, so as to leverage multi-scale features to effectively detect objects at vastly different scales.
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.
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.
AiAds: Automated and Intelligent Advertising System for Sponsored Search
Sponsored search has more than 20 years of history, and it has been proven to be a successful business model for online advertising.
How Do Drivers Allocate Their Potential Attention? Driving Fixation Prediction via Convolutional Neural Networks
Based on the multiple drivers’ attention allocation dataset, we propose a convolutional-deconvolutional neural network (CDNN) to predict the drivers’ eye fixations.
Multidimensional Data Tensor Sensing for RF Tomographic Imaging
The recently proposed transform-based tensor model is more appropriate for sensory data processing, as it helps exploit the geometric structures of the three-dimensional target and improve the recovery precision.
