A mixture model with adaptive KDE and per-image cross-validation raises estimated human fixation consistency by 5-15% median log-likelihood and up to 2 AUC points over fixed-bandwidth Gaussian baselines.
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7 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 7representative citing papers
PAMELA provides a multi-user rating dataset and personalized reward model that predicts individual image preferences more accurately than prior population-level aesthetic models.
MG-IQA trains vision-language models with attribute-aware RL2R and a multi-dimensional Thurstone reward model to jointly predict overall quality and fine-grained attributes, reporting 2.1% average SRCC gains on eight IQA benchmarks.
DSCC groups spectrally similar and spatially close pixels into supertokens using multi-criteria distance and soft labels, then classifies at the token level to achieve 0.728 CF1 at 197.75 FPS on WHU-OHS.
RoomRecon delivers a real-time mobile system for high-quality textured 3D room reconstructions that combines AR-guided imaging with generative AI texturing focused on permanent structures and claims to outperform prior methods in quality and speed.
DAT combines a small-large model cascade with fine-tuning and bandwidth-aware multi-stream transmission to deliver high-accuracy event recognition and low-latency alerts for video streams in edge-cloud systems.
A DenseNet201 base model trained on a constructed plant leaf disease dataset outperforms baselines and enables faster, more robust transfer learning with less data than general models.
citing papers explorer
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Raising the Ceiling: Better Empirical Fixation Densities for Saliency Benchmarking
A mixture model with adaptive KDE and per-image cross-validation raises estimated human fixation consistency by 5-15% median log-likelihood and up to 2 AUC points over fixed-bandwidth Gaussian baselines.
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Personalizing Text-to-Image Generation to Individual Taste
PAMELA provides a multi-user rating dataset and personalized reward model that predicts individual image preferences more accurately than prior population-level aesthetic models.
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Multi-Granularity Reasoning for Image Quality Assessment via Attribute-Aware Reinforcement Learning to Rank
MG-IQA trains vision-language models with attribute-aware RL2R and a multi-dimensional Thurstone reward model to jointly predict overall quality and fine-grained attributes, reporting 2.1% average SRCC gains on eight IQA benchmarks.
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Hyperspectral Image Classification via Efficient Global Spectral Supertoken Clustering
DSCC groups spectrally similar and spatially close pixels into supertokens using multi-criteria distance and soft labels, then classifies at the token level to achieve 0.728 CF1 at 197.75 FPS on WHU-OHS.
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RoomRecon: High-Quality Textured Room Layout Reconstruction on Mobile Devices
RoomRecon delivers a real-time mobile system for high-quality textured 3D room reconstructions that combines AR-guided imaging with generative AI texturing focused on permanent structures and claims to outperform prior methods in quality and speed.
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DAT: Dual-Aware Adaptive Transmission for Efficient Multimodal LLM Inference in Edge-Cloud Systems
DAT combines a small-large model cascade with fine-tuning and bandwidth-aware multi-stream transmission to deliver high-accuracy event recognition and low-latency alerts for video streams in edge-cloud systems.
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Developing a Strong Pre-Trained Base Model for Plant Leaf Disease Classification
A DenseNet201 base model trained on a constructed plant leaf disease dataset outperforms baselines and enables faster, more robust transfer learning with less data than general models.