WCCNet: Wavelet-context Cooperative Network for Efficient Multispectral Pedestrian Detection
Xingjian Wang, Li Chai, Jiming Chen, Zhiguo Shi

TL;DR
WCCNet is an efficient multispectral pedestrian detection framework that uses asymmetric feature extraction and a novel fusion module to improve accuracy while reducing computational costs.
Contribution
The paper introduces WCCNet, featuring a dual-stream asymmetric backbone, Mixture of Wavelet Experts, and a crossmodal fusion module for enhanced multispectral pedestrian detection.
Findings
Outperforms state-of-the-art methods on KAIST and FLIR benchmarks.
Achieves higher accuracy with lower computational cost.
Effectively fuses multispectral features for improved detection.
Abstract
Multispectral pedestrian detection is essential to various tasks especially autonomous driving, for which both the accuracy and computational cost are of paramount importance. Most existing approaches treat RGB and infrared modalities equally. They typically adopt two symmetrical backbones for multimodal feature extraction, which ignore the substantial differences between modalities and bring great difficulty for the reduction of the computational cost as well as effective crossmodal fusion. In this work, we propose a novel and efficient framework named Wavelet-context Cooperative Network (WCCNet), which differentially extracts complementary features across spectra with low computational cost and further fuses these diverse features based on their spatially relevant cross-modal semantics. WCCNet explores an asymmetric but cooperative dual-stream backbone, in which WCCNet utilizes…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsVideo Surveillance and Tracking Methods · Remote-Sensing Image Classification · Advanced Chemical Sensor Technologies
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
