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ARXIV:2603.21746 · LARGE VISION-LANGUAGE MODELS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.21746LARGE VISION-LANGUAGE MODELSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALESimone Alghisi · Massimo Rizzoli · Seyed Mahed Mousavi · Giuseppe Riccardi · arXiv
This research explores how explicit object pointing in Large Vision-Language Models improves zero-shot counting accuracy and generalization by encoding spatial information.
Opportunity summary
Pain This research explores how explicit object pointing in Large Vision-Language Models improves zero-shot counting accuracy and generalization by encoding spatial information.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
This research explores how explicit object pointing in Large Vision-Language Models improves zero-shot counting accuracy and generalization by encoding spatial information. The model grounds the objects mentioned in the natural-language query by predicting their…
Pointing increases the accuracy and explainability of Large Vision-Language Models (LVLMs) by modeling grounding and reasoning as explicit sequential steps. The model grounds the objects mentioned in the natural-language query by predicting their coordinates,…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. While pointing has been shown to increase LVLMs' accuracy, it is unclear which mechanism supports these gains and its relevance in cognitive tasks.
Large Vision-Language Models moved forward this cycle; last verified April 2026. Public score 4.0/10.
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This research explores how explicit object pointing in Large Vision-Language Models improves zero-shot counting accuracy and generalization by encoding spatial information.
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10.48550/arXiv.2603.21746This research explores how explicit object pointing in Large Vision-Language Models improves zero-shot counting accuracy and generalization by encoding spatial information.
Abstract
Pointing increases the accuracy and explainability of Large Vision-Language Models (LVLMs) by modeling grounding and reasoning as explicit sequential steps. The model grounds the objects mentioned in the natural-language query by predicting their coordinates, and then generates an answer conditioned on these points. While pointing has been shown to increase LVLMs' accuracy, it is unclear which mechanism supports these gains and its relevance in cognitive tasks. In addition, the reliability of the intermediate points remains understudied, limiting their use as visual explanations. In this work, we study the role of pointing in a cognitive task: zero-shot counting from a visual scene. We fine-tune state-of-the-art LVLMs following two approaches: Direct Counting, where models only predict the total number of objects, and Point-then-Count, where LVLMs generate the target objects' coordinates followed by their count. The results show that Point-then-Count achieves higher out-of-distribution generalization, suggesting that coordinates help LVLMs learn skills rather than overfitting on narrow tasks. Although predicted points are accurately grounded in the image in over 89\% of cases (as measured by F1), performance varies across image regions, revealing spatial biases. Finally, mechanistic analyses show that gains in counting arise from the spatial information encoded in the coordinates.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
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Dimensions overall score 4.0
PROBLEM
This research explores how explicit object pointing in Large Vision-Language Models improves zero-shot counting accuracy and generalization by encoding spatial information. The model grounds the objects mentioned in the natural-language query by predicting their coordinates, and...
METHOD
Pointing increases the accuracy and explainability of Large Vision-Language Models (LVLMs) by modeling grounding and reasoning as explicit sequential steps. The model grounds the objects mentioned in the natural-language query by predicting their coordinates, and then generates...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. While pointing has been shown to increase LVLMs' accuracy, it is unclear which mechanism supports these gains and its relevance in cognitive tasks.
WHY NOW
Large Vision-Language Models moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
This research explores how explicit object pointing in Large Vision-Language Models improves zero-shot counting accuracy and generalization by encoding spatial information. The model grounds the objects mentioned in the natural-language query by predicting their coordinates, and then generates an answer conditioned on these points.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Pointing increases the accuracy and explainability of Large Vision-Language Models (LVLMs) by modeling grounding and reasoning as explicit sequential steps. The model grounds the objects mentioned in the natural-language query by predicting their coordinates, and then generates an answer conditioned on these points.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. While pointing has been shown to increase LVLMs' accuracy, it is unclear which mechanism supports these gains and its relevance in cognitive tasks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large Vision-Language Models moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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This research explores how explicit object pointing in Large Vision-Language Models improves zero-shot counting accuracy and generalization by encoding spatial information.
Segment
Large Vision-Language Models
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Commercial read
4.0/10 public viability
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proof status
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