ENEAS: Embedding-guided Neural Ensemble for Adaptive Segmentation

arXiv:2609.03756 · cs.CV, cs.AI · Submitted 2026-09-03 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-03

Updated: 2026-09-03

Code: https://github.com/speridlabs/eneas

Terminology

• Verification — 20× in this paper · explained in 2 episodes
• Embeddings — 13× in this paper · explained in 2 episodes
• Grounding — 13× in this paper · explained in 3 episodes
• Benchmark — 10× in this paper · explained in 2 episodes
• Robustness — 10× in this paper · explained in 5 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Foundation Model — 9× in this paper · explained in 3 episodes
• Transformer — 8× in this paper · explained in 2 episodes
• Foundation Models — 6× in this paper · explained in 7 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Hallucination — 5× in this paper · explained in 3 episodes
• Failure Modes — 4× in this paper · explained in 3 episodes
• Gaussian Splatting — 4× in this paper · explained in 4 episodes
• 3D Gaussian Splatting — 3× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Instance Segmentation — 3× in this paper · explained in 3 episodes
• Semantic Understanding — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Adaptability — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Decoupling — 2× in this paper · explained in 2 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Mask Generation — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Cognitive Load — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Ontology — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Semantic Drift — 1× in this paper · explained in 3 episodes
• Semantic Gap — 1× in this paper · explained in 2 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Two-Stage Architecture — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• Qwen3-VL Technical Report
• Qwen2.5-VL Technical Report
• PaliGemma: A versatile 3B VLM for transfer
• SAM 3: Segment Anything with Concepts
• Segment and Track Anything
• FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance
• Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks
• Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection
• The 2017 DAVIS Challenge on Video Object Segmentation
• Semantic-SAM: Segment and Recognize Anything at Any Granularity
• SpotlessSplats: Ignoring Distractors in 3D Gaussian Splatting
• Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
• Florence: A New Foundation Model for Computer Vision
• YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark
• Efficient Guided Generation for Large Language Models
• SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory
• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
• Advancing Complex Video Object Segmentation via Progressive Concept Construction

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