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ARXIV:2603.24257 · VISION-LANGUAGE SYSTEMS · SUBMITTED 26 MAR · 20:30 UTC · FRESHNESS STALE
ARXIV:2603.24257VISION-LANGUAGE SYSTEMSSUBMITTED 26 MAR · 20:30 UTCFRESHNESS STALETommaso Galliena · Stefano Rosa · Tommaso Apicella · Pietro Morerio · Alessio Del Bue · Lorenzo Natale · arXiv
A memory-augmented vision-language model ensuring consistent multi-view object captioning for better embodied agent navigation.
Opportunity summary
Pain A memory-augmented vision-language model ensuring consistent multi-view object captioning for better embodied agent navigation.
Evidence 0 refs | 0 sources | 50% coverage
Blocker Evidence unverified
A memory-augmented vision-language model ensuring consistent multi-view object captioning for better embodied agent navigation. Previous methods resolved inconsistencies using offline multi-view aggregation or multi-stage pipelines that decouple exploration, data association, and caption learning, with…
Vision-Language Models (VLMs) often yield inconsistent descriptions of the same object across viewpoints, hindering the ability of embodied agents to construct consistent semantic representations over time. Previous methods resolved inconsistencies using offline multi-view aggregation…
ScienceToStartup currently rates this 9.0/10 on the public viability pass. Extensive evaluation on a manually annotated object-level test set, demonstrate improvements of up to +11.86% in standard captioning scores and +7.39% in caption self-similarity…
Vision-Language Systems moved forward this cycle; last verified April 2026. Public score 9.0/10. Implementation evidence is present through a linked repository.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
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A memory-augmented vision-language model ensuring consistent multi-view object captioning for better embodied agent navigation.
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10.48550/arXiv.2603.24257A memory-augmented vision-language model ensuring consistent multi-view object captioning for better embodied agent navigation.
Abstract
Vision-Language Models (VLMs) often yield inconsistent descriptions of the same object across viewpoints, hindering the ability of embodied agents to construct consistent semantic representations over time. Previous methods resolved inconsistencies using offline multi-view aggregation or multi-stage pipelines that decouple exploration, data association, and caption learning, with limited capacity to reason over previously observed objects. In this paper, we introduce a unified, memory-augmented Vision-Language agent that simultaneously handles data association, object captioning, and exploration policy within a single autoregressive framework. The model processes the current RGB observation, a top-down explored map, and an object-level episodic memory serialized into object-level tokens, ensuring persistent object identity and semantic consistency across extended sequences. To train the model in a self-supervised manner, we collect a dataset in photorealistic 3D environments using a disagreement-based policy and a pseudo-captioning model that enforces consistency across multi-view caption histories. Extensive evaluation on a manually annotated object-level test set, demonstrate improvements of up to +11.86% in standard captioning scores and +7.39% in caption self-similarity over baseline models, while enabling scalable performance through a compact scene representation. Code, model weights, and data are available at https://github.com/hsp-iit/epos-vlm
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PROBLEM
A memory-augmented vision-language model ensuring consistent multi-view object captioning for better embodied agent navigation. Previous methods resolved inconsistencies using offline multi-view aggregation or multi-stage pipelines that decouple exploration, data association, an...
METHOD
Vision-Language Models (VLMs) often yield inconsistent descriptions of the same object across viewpoints, hindering the ability of embodied agents to construct consistent semantic representations over time. Previous methods resolved inconsistencies using offline multi-view aggre...
RESULT
ScienceToStartup currently rates this 9.0/10 on the public viability pass. Extensive evaluation on a manually annotated object-level test set, demonstrate improvements of up to +11.86% in standard captioning scores and +7.39% in caption self-similarity over baseline models, whil...
WHY NOW
Vision-Language Systems moved forward this cycle; last verified April 2026. Public score 9.0/10. Implementation evidence is present through a linked repository.
The model processes the current RGB observation, a top-down explored map, and an object-level episodic memory serialized into object-level tokens
Directly stated in abstract as input components
partial
demonstrate improvements of up to +11.86% in standard captioning scores
Explicitly stated in the abstract with specific numeric improvement
partial
+7.39% in caption self-similarity over baseline models
Explicitly stated in the abstract with specific numeric improvement
partial
introduce a unified, memory-augmented Vision-Language agent that simultaneously handles data association, object captioning, and exploration policy within a single autoregressive framework
Directly stated in abstract as core methodological contribution
partial
ensuring persistent object identity and semantic consistency across extended sequences
Directly stated in abstract as key technical feature
partial
To train the model in a self-supervised manner, we collect a dataset in photorealistic 3D environments using a disagreement-based policy
Strongly supported by abstract and analysis, though specific training details may be in full paper
partial
while enabling scalable performance through a compact scene representation
Directly stated in abstract but without specific scalability metrics
partial
Possible limitations include the model's reliance on specific datasets for training and the complexity involved in transferring the solution to different hardware platforms or operating environments
Stated as a limitation in the analysis section, though not quantified
partial
demonstrate improvements of up to +11.86% in standard captioning scores
Explicitly stated in the abstract with specific numeric improvement
partial
+7.39% in caption self-similarity over baseline models
Explicitly stated in the abstract with specific numeric improvement
partial
introduce a unified, memory-augmented Vision-Language agent that simultaneously handles data association, object captioning, and exploration policy within a single autoregressive framework
Directly stated in abstract as core methodological contribution
partial
ensuring persistent object identity and semantic consistency across extended sequences
Strongly supported in both abstract and analysis sections
partial
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A memory-augmented vision-language model ensuring consistent multi-view object captioning for better embodied agent navigation.
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