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ARXIV:2603.11441 · REAL-TIME DETECTION · SUBMITTED 19 MAR · 21:31 UTC · FRESHNESS STALE
ARXIV:2603.11441REAL-TIME DETECTIONSUBMITTED 19 MAR · 21:31 UTCFRESHNESS STALEarXiv
DART transforms promptable detection into a real-time multi-class system with significant speed improvements.
Opportunity summary
Pain DART transforms promptable detection into a real-time multi-class system with significant speed improvements.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence partial
DART transforms promptable detection into a real-time multi-class system with significant speed improvements. Among these, SAM3 achieves state-of-the-art accuracy by combining a ViT-H/14 backbone with cross-modal transformer decoding and learned object queries.
Recent advances in vision-language modeling have produced promptable detection and segmentation systems that accept arbitrary natural language queries at inference time. Among these, SAM3 achieves state-of-the-art accuracy by combining a ViT-H/14 backbone with cross-modal…
ScienceToStartup currently rates this 9.0/10 on the public viability pass. Among these, SAM3 achieves state-of-the-art accuracy by combining a ViT-H/14 backbone with cross-modal transformer decoding and learned object queries.
Real-Time Detection moved forward this cycle; last verified April 2026. Public score 9.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
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DART transforms promptable detection into a real-time multi-class system with significant speed improvements.
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10.48550/arXiv.2603.11441DART transforms promptable detection into a real-time multi-class system with significant speed improvements.
Abstract
Recent advances in vision-language modeling have produced promptable detection and segmentation systems that accept arbitrary natural language queries at inference time. Among these, SAM3 achieves state-of-the-art accuracy by combining a ViT-H/14 backbone with cross-modal transformer decoding and learned object queries. However, SAM3 processes a single text prompt per forward pass. Detecting N categories requires N independent executions, each dominated by the 439M-parameter backbone. We present Detect Anything in Real Time (DART), a training-free framework that converts SAM3 into a real-time multi-class detector by exploiting a structural invariant: the visual backbone is class-agnostic, producing image features independent of the text prompt. This allows the backbone computation to be shared between all classes, reducing its cost from O(N) to O(1). Combined with batched multi-class decoding, detection-only inference, and TensorRT FP16 deployment, these optimizations yield 5.6x cumulative speedup at 3 classes, scaling to 25x at 80 classes, without modifying any model weight. On COCO val2017 (5,000 images, 80 classes), DART achieves 55.8 AP at 15.8 FPS (4 classes, 1008x1008) on a single RTX 4080, surpassing purpose-built open-vocabulary detectors trained on millions of box annotations. For extreme latency targets, adapter distillation with a frozen encoder-decoder achieves 38.7 AP with a 13.9 ms backbone. Code and models are available at https://github.com/mkturkcan/DART.
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partial0 refs; 0 sources; 33% coverage.
What was readable
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Viability
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Commercial
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Dimensions overall score 9.0
PROBLEM
DART transforms promptable detection into a real-time multi-class system with significant speed improvements. Among these, SAM3 achieves state-of-the-art accuracy by combining a ViT-H/14 backbone with cross-modal transformer decoding and learned object queries.
METHOD
Recent advances in vision-language modeling have produced promptable detection and segmentation systems that accept arbitrary natural language queries at inference time. Among these, SAM3 achieves state-of-the-art accuracy by combining a ViT-H/14 backbone with cross-modal transf...
RESULT
ScienceToStartup currently rates this 9.0/10 on the public viability pass. Among these, SAM3 achieves state-of-the-art accuracy by combining a ViT-H/14 backbone with cross-modal transformer decoding and learned object queries.
WHY NOW
Real-Time Detection moved forward this cycle; last verified April 2026. Public score 9.0/10.
these optimizations yield 5.6x cumulative speedup at 3 classes, scaling to 25x at 80 classes
Implication not extracted yet.
partial
This allows the backbone computation to be shared between all classes, reducing its cost from O(N) to O(1)
Implication not extracted yet.
partial
On COCO val2017 (5,000 images, 80 classes), DART achieves 55.8 AP at 15.8 FPS (4 classes, 1008x1008) on a single RTX 4080
Implication not extracted yet.
partial
surpassing purpose-built open-vocabulary detectors trained on millions of box annotations
Implication not extracted yet.
partial
SAM3 processes a single text prompt per forward pass. Detecting N categories requires N independent executions
Implication not extracted yet.
partial
We present Detect Anything in Real Time (DART), a training-free framework that converts SAM3 into a real-time multi-class detector
Implication not extracted yet.
partial
For extreme latency targets, adapter distillation with a frozen encoder-decoder achieves 38.7 AP with a 13.9 ms backbone
Implication not extracted yet.
partial
by exploiting a structural invariant: the visual backbone is class-agnostic, producing image features independent of the text prompt
Implication not extracted yet.
partial
We present Detect Anything in Real Time (DART), a training-free framework that converts SAM3 into a real-time multi-class detector by exploiting a structural invariant
Directly stated in abstract: 'training-free framework' and 'without modifying any model weight'.
partial
the visual backbone is class-agnostic, producing image features independent of the text prompt. This allows the backbone computation to be shared between all classes, reducing its cost from O(N) to O(1)
Explicitly stated in abstract with complexity notation.
partial
these optimizations yield 5.6x cumulative speedup at 3 classes, scaling to 25x at 80 classes
Directly stated with specific numbers in abstract.
partial
On COCO val2017 (5,000 images, 80 classes), DART achieves 55.8 AP at 15.8 FPS (4 classes, 1008x1008) on a single RTX 4080
Explicit numeric result stated in abstract.
partial
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DART transforms promptable detection into a real-time multi-class system with significant speed improvements.
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Real-Time Detection
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