ENEAS：嵌入引導的神經整合用於自適應分割

摘要

我們提出ENEAS，這是一個統一、可文字提示的方法，用於實例追蹤與語意發現。可文字提示的分割模型，包括最新的基礎模型如SAM 3，仍然存在時間性幻覺、空間碎片化與語意誤分類的缺陷：當物體離開視野時，它們無法回報目標消失；在極端特寫中，它們分割局部紋理而非完整物體；且它們將視覺特徵置於本體論真實之上，因此雕像、畫作或倒影等視覺上相似的物件會被分割為目標實體。 ENEAS 以單一方法發揮雙重作用：一方面對單一實例進行精確追蹤與高品質分割，另一方面進行開放概念的發現——找出文字查詢所指稱的每一個實例，並由語意驗證層加以解析。在追蹤方面，我們在原本僅限於基於點互動的幾何穩健SeC架構上，擴充了一個文字提示介接器，並利用其時間記憶，使目標即使在消失期間仍能保持鎖定，不會漂移至干擾物；即使目標填滿整個視野，也仍能維持完整。在發現方面，驗證層結合高速視覺嵌入比對與條件式VLM精修，僅對模糊候選者啟動語意推理，過濾掉僅靠視覺模型無法區分的本體論錯誤，同時保持低延遲。ENEAS的設計以3D重建為考量——在該情境中，單一誤分類的干擾物便會破壞資產——因此它能對影片、大型資料庫，以及時間或空間上無序的資料集合，實現高品質的語意追蹤與分割；同時具備分辨真實實例與其「分身」——那些看起來相似但實際不同的事物——的判別能力。程式碼與模型已公開於 https://github.com/speridlabs/eneas

English

We present ENEAS, a unified, text-promptable method for instance tracking and semantic discovery. Text-promptable segmentation models, including the latest foundation models such as SAM 3, still suffer from temporal hallucinations, spatial fragmentation, and semantic misclassification: they fail to report target absence when an object leaves the field of view, segment local textures instead of the complete object during extreme close-ups, and prioritize visual features over ontological reality, so that visually similar artifacts such as statues, paintings, or reflections are segmented as target entities. ENEAS works two ways from a single method: precise tracking and high-quality segmentation of a unique instance, and open-concept discovery of every instance a text query names, resolved by a semantic verification layer. For tracking, we extend the geometrically robust SeC architecture, previously limited to point interactions, with a text-prompting adapter and leverage its temporal memory, so that the target is held through disappearance without drifting to distractors and kept whole even when it fills the entire view. For discovery, the verification layer combines high-speed visual embedding matching with conditional VLM refinement, invoking semantic reasoning only for ambiguous candidates, which filters out the ontological errors that visual-only models cannot distinguish while keeping latency low. Designed with 3D reconstruction in mind, where a single misclassified distractor corrupts the asset, ENEAS unlocks high-quality semantic tracking and segmentation of video, of broad libraries, and of collections of temporally or spatially unordered data, together with the discrimination to tell true instances from their doppelgangers: things that look alike but are not the same. The code and models are available at https://github.com/speridlabs/eneas