ENEAS: Embedding-guided Neural Ensemble for Adaptive Segmentation
Quick summary
arXiv:2609.03756v1 Announce Type: cross Abstract: 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
Key takeaways
- arXiv:2609.03756v1 Announce Type: cross Abstract: 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
Why it matters
The importance of “ENEAS: Embedding-guided Neural Ensemble for Adaptive Segmentation” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
Member comments