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ENEAS: Embedding-guided Neural Ensemble for Adaptive Segmentation
ENEAS adds text prompting and semantic verification to video segmentation to keep tracking targets through occlusion and reject lookalike distractors.
ENEAS is a unified text-promptable method for instance tracking and open-concept semantic discovery in video, designed to fix temporal hallucinations, spatial fragmentation, and semantic misclassification seen in SAM 3-class foundation models. It extends the geometrically robust SeC architecture with a text-prompting adapter and temporal memory, and uses a verification layer combining fast visual embedding matching with conditional VLM refinement for ambiguous candidates. It targets 3D reconstruction pipelines where a single misclassified distractor corrupts the asset. Code and models are open-sourced.
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Deep Packet Inspection challenges for telecom and security vendors
Enea's DPI survey of telecom and security vendors finds traffic visibility is increasingly critical yet harder as 5G, cloud, IoT, and TLS 1.3 spread.
Enea's survey of high-tech product managers at telecom and security vendors found that real-time application-level traffic visibility is considered essential as cloud adoption, 5G, remote work, and IoT dissolve traditional network perimeters. Abnormal traffic detection was the top DPI use case, 70% of respondents require classification of connected devices in enterprise and IoT/industrial networks, and TLS 1.3 (mandatory in 5G) threatens payload-based visibility. Most vendors report having or developing cloud solutions, with half planning SASE offerings integrating security and networking.
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