Showing 1 open source project for "optical flow"

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  • Attack Surface Management | Criminal IP ASM Icon
    Attack Surface Management | Criminal IP ASM

    For security operations, threat-intelligence and risk teams wanting a tool to get access to auto-monitored assets exposed to attack surfaces

    Criminal IP’s Attack Surface Management (ASM) is a threat-intelligence–driven platform that continuously discovers, inventories, and monitors every internet-connected asset associated with an organization, including shadow and forgotten resources, so teams see their true external footprint from an attacker’s perspective. The solution combines automated asset discovery with OSINT techniques, AI enrichment and advanced threat intelligence to surface exposed hosts, domains, cloud services, IoT endpoints and other Internet-facing vectors, capture evidence (screenshots and metadata), and correlate findings to known exploitability and attacker tradecraft. ASM prioritizes exposures by business context and risk, highlights vulnerable components and misconfigurations, and provides real-time alerts and dashboards to speed investigation and remediation.
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  • anny is an all-in-one platform for managing hybrid workplaces and shared resources. Icon
    anny is an all-in-one platform for managing hybrid workplaces and shared resources.

    For Businesses looking for a flexible solution for internal and external bookings

    Enable your employees to easily book desks, meeting rooms, parking spots, equipment, and more – all in one place. With flexible rules and group permissions, you stay in full control of who can access what.
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  • 1
    I3D models trained on Kinetics

    I3D models trained on Kinetics

    Convolutional neural network model for video classification

    ...The I3D model extends the 2D convolutional structure of Inception-v1 into 3D, allowing it to capture spatial and temporal information from videos for action recognition. This repository includes pretrained I3D models on the Kinetics dataset, with both RGB and optical flow input streams. The models have achieved state-of-the-art results on benchmark datasets such as UCF101 and HMDB51, and also won first place in the CVPR 2017 Charades Challenge. The project provides TensorFlow and Sonnet-based implementations, pretrained checkpoints, and example scripts for evaluating or fine-tuning models. It also offers sample data, including preprocessed video frames and optical flow arrays, to demonstrate how to run inference and visualize outputs.
    Downloads: 1 This Week
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