arXiv Computer Vision
Spiking Neural Networks for Energy-Efficient Object Detection in Forward-Looking Sonar Imagery
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv AI
Aug 28
ANTShapes Benchmarking Datasets for Event-Based Neuromorphic Object Classification
The paper introduces four new event‑based vision datasets created with the ANTShapes simulation tool, designed to support object classification research using spiking neural networks (SNNs). These datasets vary in difficulty and are benchmarked against established spiking datasets such as N‑MNIST, CIFAR10‑DVS, DVSGesture, and POKER‑DVS using a convolutional SNN. The work provides detailed, high‑quality datasets for future experiments and validates ANTShapes as a suitable tool for generating event‑based vision data.
arXiv AI
Sep 21
LoRA Enhanced Contrastive Learning with SAS Vision Transformers
The paper presents a three‑stage, parameter‑efficient approach to improve automatic target recognition (ATR) with synthetic aperture sonar (SAS) data by adapting DINOv3 Vision Transformers. Stage 1 applies Low‑Rank Adaptation (LoRA) while freezing the backbone, which significantly boosts the area under the precision‑recall curve from 0.300 to 0.679. Subsequent hard‑negative mining and supervised contrastive learning stages show negligible impact, indicating that a single LoRA adaptation is sufficient for effective underwater ATR.
arXiv Computer Vision
6d ago
MambaDSF: Multi-Scale SSM with Dilated Feature Fusion for Sonar Small Target Detection
arXiv:2605.24928v2 Announce Type: replace Abstract: Sonar imaging is the primary modality for underwater target detection, yet small targets remain difficult to detect due to insufficient pixel cover...
arXiv Computer Vision
Aug 27
STATrack: A Target-Aware Fully Spiking Neural Network for Efficient UAV Tracking
STATrack is a fully spiking neural network designed for UAV visual tracking using only RGB inputs, eliminating the need for costly event cameras. It introduces Adaptive Mutual Information Maximization (AMIM) to preserve fine-grained target information in deep spiking representations and a sample-difficulty-aware dynamic weighting strategy to adjust the mutual‑information constraint during training. Experiments on four UAV tracking benchmarks show that STATrack achieves state‑of‑the‑art performance while maintaining low theoretical energy consumption.
arXiv Computer Vision
Oct 5
FUSEye: Training-Light Fisheye Detection with Overlapping Views and Zero-Initialized Adapters
FUSEye is a lightweight training framework that adapts a frozen COCO‑pretrained YOLO26‑x detector for fisheye images by adding only about 227k parameters. It introduces three modules: GridViews to enlarge compressed boundary regions, Z‑Adapters to correct distortion‑induced feature misalignment, and AgreeFusion to fuse detections across overlapping views. On the WoodScape fisheye benchmark, FUSEye boosts YOLO26‑x mAP50 from 0.148 to 0.266 while requiring only 25% of the labeled data to achieve 0.2597 mAP50, and it also improves YOLOv8‑11 detectors.
arXiv Machine Learning
Aug 19
Spikformer V2: Join the High Accuracy Club on ImageNet with an SNN Ticket
Spikformer V2 introduces a Spiking Self‑Attention (SSA) mechanism that removes softmax and uses spike‑based Query, Key, and Value to capture sparse visual features efficiently. It also adds a Spiking Convolutional Stem (SCS) and employs self‑supervised learning (masking and reconstruction) to pre‑train the model before fine‑tuning on ImageNet. The result is the first spiking neural network to surpass 80 % accuracy on ImageNet, achieving 81.10 % with a 172 M‑parameter, 16‑layer model in just one time step.