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
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
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
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
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A personalized Korean visual speech recognition (VSR) system is presented and the gap between the population-level benchmark score and an individual user's error is quantified, on the nine-camera OLKAVS corpus.
Se Un Park, Hak-seon Kim, Tae-Hoon Roh et al.· 0 citations
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
SemMSA, a latent semantic-aided framework that constructs rich sentiment-relevant semantics with LLMs, fully integrating with all modalities via anchor-free spectral alignment, is proposed.
Wen-Hao Li, Zhi-Bin Wu, Chong-Yao Xiao et al.· 0 citations
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
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
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· arXiv.org· 2 citations
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.
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.· arXiv.org· 12 citations· ⚡1
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
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.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.