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computer vision

3,080 papers

#computer vision Preprint May 2026

Persona Prompting in Multimodal Urban Perception: Descriptive Convergence and Interpretive Variation

Overall, persona prompting affects interpretive framing more strongly than descriptive grounding, and profile-pair similarity patterns are strongly correlated for all three output types, although agreement is lowest for justifications.

Neemias B. da Silva, Matt Ratto, Myriam Delgado et al. · 0 citations
#computer vision Preprint Sep 2026

Multimodal Thinking with Renderable Programs

This work introduces SVGLM, a framework that uses scalable vector graphics (SVG) primitives to connect text and image in reasoning tasks, and exploits the duality of SVG as both image description and text instructions, yielding a more compact, interpretable solution to equip general VLMs with the capability of generati...

Sun-Li Chen, Ding Zhong, Ziqiao Ma et al. · 0 citations
#computer vision Preprint Aug 2026

STRAND: Benchmarking and Improving Object-Centric Spatio-Temporal Monitoring in Video Large Language Models

STRAND is introduced, a benchmark of human-verified object-centric facts that evaluates intermediate reasoning by decomposing queries into sub-questions, distinguishing genuine temporal understanding from coincidental correctness and an object-centric framework that explicitly constructs and reasons over structured obj...

T. Nguyen, Tri Cao, Khoi M. Le et al. · 0 citations
#computer vision Preprint Aug 2026

Free the Language Model From the Vision Encoder: Semantic Serialization as a Perception Interface for Small Language Models

An embodied scene question-answering (QA) interface in which vision never enters the language model is studied, showing the gain survives paraphrase, attributing it to decision-aligned computation rather than answer-string leakage, while novel judgment vocabularies bound its scope.

Cong Xu, Ravi Sankar · 1 citation
#computer vision Preprint Sep 2026

Small yet Assistive: Spatially-Aware Post-Training for Low Vision

Results show that BLV-focused post-training improves both accessibility-specific spatial grounding and general visual-text reasoning, and is substantially outperforms the baseline across various benchmarks.

Rishabh C. Choudhary, S. Raj, Umesh Goyal et al. · 0 citations
#computer vision Preprint Sep 2026

What, When, and How: Audio Description as Constrained Global Optimization

This hybrid system uses large language models to propose and ground visual elements, estimate their salience to the narrative, and generate compressed realizations, which makes better decisions than prompted LLMs about what to describe and when to describe it.

Igor Sterner, Mirella Lapata, A. Lascarides et al. · 0 citations
#machine learning Preprint Open access Sep 2026

Open-access model for detecting openly dumped dispersed municipal solid waste from crowdsourced UAV imagery in Sub-Saharan Africa

Managing municipal solid waste in rapidly urbanizing Sub-Saharan Africa remains challenging due to dispersed informal dumping and limited high-resolution datasets for spatial monitoring. We present an open-access deep learning model for automated detection of openly dumped dispersed solid waste via crowdsourced UAV ima...

Steffen Knoblauch, Ram Kumar Muthusamy, Luis M. A. Bettencourt et al. · 0 citations
#machine learning Review Apr 2020

CatSIM: A Categorical Image Similarity Metric

CatSIM uses a structural similarity image quality paradigm and is robust to small perturbations in location so that structures in similar, but not entirely overlapping, regions of two images are rated higher than using simple matching.

Geoffrey Z. Thompson, Ranjan Maitra · 2 citations

GOMA: Toward Structure-Driven Multimodal Alignment from a Graph Signal Smoothing Perspective

Graph-Optimized Multimodal Alignment (GOMA), which lets a jointly trained model support single-modality and dual-attribute retrieval through a task-specific readout, achieves state-of-the-art performance on all 14 primary measures against 14 external methods.

Xu Wang, Xun-Kai Li, Yin-Lin Zhu et al. · 0 citations

AIR: Analytic Imbalance Rectifier for Continual Learning

An analytic imbalance rectifier (AIR) algorithm for real-world CL is proposed that addresses class imbalance with an analytic reweighting module (ARM) that calculates a reweighting factor for each class in the loss function to equalize total sample weights across classes.

Di Fang, Yinan Zhu, Run-Ze Fang et al. · 12 citations · ⚡1

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Microsoft Research Blog Oct 6, 2026

What AI gets wrong and what failure teaches us

Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity.  The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.

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