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Found 10 papers, 1 papers with code
In-Sensor & Neuromorphic Computing are all you need for Energy Efficient Computer Vision
Although the overhead for the first layer MACs with direct encoding is negligible for deep SNNs and the CV processing is efficient using SNNs, the data transfer between the image sensors and the downstream processing costs significant bandwidth and may dominate the total energy.
Hoyer regularizer is all you need for ultra low-latency spiking neural networks
Spiking Neural networks (SNN) have emerged as an attractive spatio-temporal computing paradigm for a wide range of low-power vision tasks.
Artificial Intelligence Security Competition (AISC)
The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems.
FeatureBooster: Boosting Feature Descriptors with a Lightweight Neural Network
We introduce a lightweight network to improve descriptors of keypoints within the same image.
Enabling ISP-less Low-Power Computer Vision
However, direct inference on the raw images degrades the test accuracy due to the difference in covariance of the raw images captured by the image sensors compared to the ISP-processed images used for training.
A multi view multi stage and multi window framework for pulmonary artery segmentation from CT scans
In addition, in order to improve the segmentation performance, we adopt multi-view and multi-window level method, at the same time we employ a fine-tune strategy to mitigate the impact of inconsistent labeling.
P2M-DeTrack: Processing-in-Pixel-in-Memory for Energy-efficient and Real-Time Multi-Object Detection and Tracking
The designs also reduce the sensor and total energy (obtained from in-house circuit simulations at Globalfoundries 22nm technology node) per frame by 5. 7x and 1. 14x, respectively.
Risk Assessment with Generic Energy Storage under Exogenous and Endogenous Uncertainty
Current risk assessment ignores the stochastic nature of energy storage availability itself and thus lead to potential risk during operation.
StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation
Inspired by the early works on indoor modeling, we leverage the structural regularities exhibited in indoor scenes, to train a better depth network.
Every Corporation Owns Its Image: Corporate Credit Ratings via Convolutional Neural Networks
Credit rating is an analysis of the credit risks associated with a corporation, which reflect the level of the riskiness and reliability in investing.
