Claims Browser

A new large-scale dataset of 115K scenes is the first hybrid dataset for image-to-scene generation

"we construct a new large-scale dataset of 115K scenes. To the best of our knowledge, it is the first hybrid dataset for image-to-scene generation."

Detecting the fetal abdominal circumference standard plane in low-cost obstetric blind sweeps is a highly imbalanced frame-classification problem where positive frames account for under 3% of a sequence

"Detecting the fetal abdominal circumference standard plane in low-cost obstetric blind sweeps is a highly imbalanced frame-classification problem: positive frames account for under 3% of a sequence"

AnatoProto outperforms the strongest foundation-model baseline by 13.20 F1 points and the strongest video temporal-action-detection baseline by 15.76 F1 points on the ACOUSLIC-AI benchmark

"On the ACOUSLIC-AI benchmark, AnatoProto reaches a test F1 of 67.72, outperforming the strongest foundation-model baseline (FetalCLIP + PRS, F1 = 54.52) by +13.20 F1 and the strongest video temporal-action-detection baseline (TriDet + PRS) by +15.76 F1"

Prototype loss and anatomy-weighted pooling exhibit non-additive synergy: prototype loss alone reduces recall by 12 points, but combined with anatomy-weighted pooling it increases recall by 6.5 points

"A synergy study, backed by embedding geometry and paired-bootstrap confidence intervals, shows that the prototype loss and anatomy-weighted pooling are not additive: applied alone the prototype loss reduces recall by 12 points, but combined with anatomy-weighted pooling it increases recall by 6.5 points"

Anatomy-weighted spatial pooling using nnU-Net abdominal-region probabilities as a spatial prior enables frozen semantic features to be aggregated onto anatomically meaningful regions

"anatomy-weighted spatial pooling that uses nnU-Net abdominal-region probabilities as a spatial prior to reweight BiomedCLIP patch tokens, so frozen semantic features are aggregated onto anatomically meaningful regions"

On-policy self-distillation (OPSD) has recently emerged as an effective post-training paradigm that improves policy optimization through dense token-level supervision from a privileged self-teacher

"On-policy self-distillation (OPSD) has recently emerged as an effective post-training paradigm that improves policy optimization through dense token-level supervision from a privileged self-teacher."

Long videos contain substantial temporal redundancy, with only a small subset of frames providing the evidence necessary to answer a question

"long videos contain substantial temporal redundancy, and only a small subset of frames provides the evidence necessary to answer a question."

Video-OPSD achieves performance comparable to GRPO while requiring substantially less training time

"achieves performance comparable to GRPO while requiring substantially less training time"

Evidence-Grounded Self-Teacher enables the teacher to provide more informative supervision by conditioning exclusively on annotated evidence frames

"This focused visual input enables the teacher to provide more informative supervision."

Privacy-Enhancing Technologies in computer vision that rely on noise or image perturbations create a trade-off between task performance and protection

"Privacy-Enhancing Technologies (PETs) in computer vision often rely on noise or image perturbations to protect visual data while securely processing it, creating a trade-off between task performance and protection."

PETs with similar classification accuracy can differ substantially on other vision tasks

"Across irreversible privacy transformations, key-based block primitives, and learnable image encryption schemes, we demonstrate that PETs with similar classification accuracy can differ substantially on other tasks."

The orthogonality among principal subspaces of the Fisher information matrix's K-FAC approximation, estimated from a small number of downstream task samples, can reflect the similarity between different tasks in pre-trained models

"a key observation about the Fisher information matrix (FIM) of pre-trained models: the orthogonality among the principal subspaces of its Kronecker-Factored Approximate Curvature (K-FAC) approximation, estimated from a small number of downstream task samples, can reflect the similarity between different tasks."

FiUni can effectively infer latent batch-level task affiliations and achieve competitive performance against advanced task-aware continual learning methods with fewer trainable parameters

"Experiments show that FiUni can effectively infer latent batch-level task affiliations and achieve competitive performance against advanced task-aware CL methods with fewer trainable parameters."

FiUni's approach of constructing FIM-derived frozen subspaces to guide low-rank adaptation while matching Fisher principal subspaces enables adaptive determination of whether to reuse knowledge, expand subspaces, or create new ones, dynamically balancing knowledge sharing and task isolation

"FiUni constructs FIM-derived frozen subspaces to guide low-rank adaptation (LoRA), while matching the Fisher principal subspace of each incoming batch window with historical subspaces. This enables FiUni to adaptively determine whether to reuse existing knowledge, expand a related subspace, or create a new subspace, dynamically balancing knowledge sharing and task isolation."

Emotions play an important functional role in regulating the relative priorities of competing goals in decision-making agents under changing external environments and evolving internal states

"Under changing external environments and evolving internal states, emotions play an important functional role in regulating the relative priorities of competing goals."

Preference regulation in decision-making agents can be computationally realized through reinforcement learning based on goal-directed theory of emotion

"Inspired by the goal-directed theory of emotion, this paper studies how such preference regulation can be computationally realized through reinforcement learning."

High-level goals can autonomously induce state-dependent preferences over competing lower-level objectives through emergent emotional preference

"We first propose a conception of emergent emotional preference: a high-level goal autonomously induces state-dependent preferences over competing lower-level objectives."

The optimality gap in preference-regulated agent policies vanishes when the optimal policy can be represented by available preference-conditioned policies

"We show that the gap vanishes when the optimal policy can be represented by the available preference-conditioned policies."