SemGrad is a gradient-based uncertainty quantification technique for free-form LLM generation that operates in semantic space using a Semantic Preservation Score to select stable embeddings.
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Pith papers citing it
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2026 9representative citing papers
AniMatrix generates anime videos by structuring artistic production rules into a controllable taxonomy and training the model to prioritize those rules over physical realism, achieving top scores from professional animators on prompt understanding and artistic motion.
Text-guided class-agnostic counting models exhibit significant weaknesses in grounding textual prompts to visual objects, as demonstrated by new negative-label and distractor tests on a multi-category dataset.
HARMES is the first large-scale dataset to combine wrist IMU, environmental, and audio sensors for recognizing 15 household activities across over 80 hours of data from 20 participants.
Mixing real UAV imagery with 2101 AI-generated image-mask pairs improves semantic segmentation F1 scores for fine-grained forest species by over 15 percentage points overall and up to 30 points for rare classes.
MooD introduces continuous valence-arousal modeling with VA-aware retrieval and perception-enhanced guidance for efficient, controllable affective image editing, plus a new AffectSet dataset.
The autoPET3 challenge finds that leading AI models reach a mean Dice score of 0.66 for multitracer PET/CT lesion segmentation, with compositional generalization to unseen tracer-center pairs remaining an open problem driven by volume overestimation and case heterogeneity.
Gaussian and linear cropping strategies for large point clouds improve 3D neural network performance over spherical crops, especially in outdoor scenes, and achieve new state-of-the-art results.
The paper delivers a chronological history of Fréchet distances connecting early abstract set theory to curve metrics, optimal transport, and the FID metric in generative models.
citing papers explorer
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Gradients with Respect to Semantics Preserving Embeddings Tell the Uncertainty of Large Language Models
SemGrad is a gradient-based uncertainty quantification technique for free-form LLM generation that operates in semantic space using a Semantic Preservation Score to select stable embeddings.
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AniMatrix: An Anime Video Generation Model that Thinks in Art, Not Physics
AniMatrix generates anime videos by structuring artistic production rules into a controllable taxonomy and training the model to prioritize those rules over physical realism, achieving top scores from professional animators on prompt understanding and artistic motion.
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Does it Really Count? Assessing Semantic Grounding in Text-Guided Class-Agnostic Counting
Text-guided class-agnostic counting models exhibit significant weaknesses in grounding textual prompts to visual objects, as demonstrated by new negative-label and distractor tests on a multi-category dataset.
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HARMES: A Multi-Modal Dataset for Wearable Human Activity Recognition with Motion, Environmental Sensing and Sound
HARMES is the first large-scale dataset to combine wrist IMU, environmental, and audio sensors for recognizing 15 household activities across over 80 hours of data from 20 participants.
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Leveraging Image Generators to Address Training Data Scarcity: The Gen4Regen Dataset for Forest Regeneration Mapping
Mixing real UAV imagery with 2101 AI-generated image-mask pairs improves semantic segmentation F1 scores for fine-grained forest species by over 15 percentage points overall and up to 30 points for rare classes.
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MooD: Perception-Enhanced Efficient Affective Image Editing via Continuous Valence-Arousal Modeling
MooD introduces continuous valence-arousal modeling with VA-aware retrieval and perception-enhanced guidance for efficient, controllable affective image editing, plus a new AffectSet dataset.
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The autoPET3 Challenge: Automated Lesion Segmentation in Whole-Body PET/CT $\unicode{x2013}$ Multitracer Multicenter Generalization
The autoPET3 challenge finds that leading AI models reach a mean Dice score of 0.66 for multitracer PET/CT lesion segmentation, with compositional generalization to unseen tracer-center pairs remaining an open problem driven by volume overestimation and case heterogeneity.
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From Spherical to Gaussian: A Comparative Analysis of Point Cloud Cropping Strategies in Large-Scale 3D Environments
Gaussian and linear cropping strategies for large point clouds improve 3D neural network performance over spherical crops, especially in outdoor scenes, and achieve new state-of-the-art results.
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A Brief History of Fr\'echet Distances: From Curves and Probability Laws to FID
The paper delivers a chronological history of Fréchet distances connecting early abstract set theory to curve metrics, optimal transport, and the FID metric in generative models.