SHED improves domain generalization in CLIP by aligning style-homogenized embeddings instead of raw ones, achieving state-of-the-art results on five benchmarks including a 4% gain on DomainNet.
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2026 14representative citing papers
A rule-based strikingness measure is added to TKGR metrics to weight rare events higher, revealing that models weaken on striking events and ensemble gains come mostly from trivial fits.
RevealLayer decomposes natural images into multiple RGBA layers using diffusion models with region-aware attention, occlusion-guided adaptation, and a composite loss, outperforming prior methods on a new benchmark dataset.
FO2 groundings can require 2^Ω(n) DNNF size, but a type-based compiler with residual caching often yields smaller circuits and faster runtimes than naive grounding.
EyeCue detects driver cognitive distraction by modeling gaze-visual context interactions in egocentric videos and achieves 74.38% accuracy on the new CogDrive dataset, outperforming 11 baselines.
Tenability defines when an argument can be maintained in debate against any conflict-free opponent attack using monotone commitment games, with three variants that differ from prior weak semantics on benchmarks like self-defeating attacks and floating assignments.
HDFM adds a continuous heat-dissipation (blur) process to flow matching, aligns an interpolated path to fix ill-posed inverse heat dissipation, and uses x-prediction to ease high-dimensional regression, yielding better performance than most baselines on image datasets.
DR-Smoothing introduces a disrupt-then-rectify prompt processing scheme into smoothing defenses, delivering tight theoretical bounds on success probability against both token- and prompt-level jailbreaks.
An automated sub-exponential algorithm computes winning strategies and ranking certificate witnesses for polynomial reachability games on infinite-state real-variable graphs.
DR-MMSearchAgent derives batch-wide trajectory advantages and uses differentiated Gaussian rewards to prevent premature collapse in multimodal agents, outperforming MMSearch-R1 by 8.4% on FVQA-test.
Neurosymbolic framework grounds skeleton motion in learnable pose and dynamics concepts then reasons over them with differentiable logic to recognize actions interpretably on NTU and NW-UCLA benchmarks.
A deep learning model generates image-aware poster layouts that satisfy user-specified attribute constraints via Gaussian noise sampling and partial layout constraints via a dedicated loss and random mask, reaching state-of-the-art performance.
Derives novel generalization error bounds for multimodal pairwise metric learning showing that fine-grained modality features reduce hypothesis space complexity via enhanced complementarity.
citing papers explorer
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SHED: Style-Homogenized Embedding Alignment for Domain Generalization
SHED improves domain generalization in CLIP by aligning style-homogenized embeddings instead of raw ones, achieving state-of-the-art results on five benchmarks including a 4% gain on DomainNet.
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Strikingness-Aware Evaluation for Temporal Knowledge Graph Reasoning
A rule-based strikingness measure is added to TKGR metrics to weight rare events higher, revealing that models weaken on striking events and ensemble gains come mostly from trivial fits.
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RevealLayer: Disentangling Hidden and Visible Layers via Occlusion-Aware Image Decomposition
RevealLayer decomposes natural images into multiple RGBA layers using diffusion models with region-aware attention, occlusion-guided adaptation, and a composite loss, outperforming prior methods on a new benchmark dataset.
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On Knowledge Compilation For Two-Variable First-Order Logic
FO2 groundings can require 2^Ω(n) DNNF size, but a type-based compiler with residual caching often yields smaller circuits and faster runtimes than naive grounding.
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EyeCue: Driver Cognitive Distraction Detection via Gaze-Empowered Egocentric Video Understanding
EyeCue detects driver cognitive distraction by modeling gaze-visual context interactions in egocentric videos and achieves 74.38% accuracy on the new CogDrive dataset, outperforming 11 baselines.
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Tenability and Weak Semantics: Modeling Non-uniform Defense -- Extended Version
Tenability defines when an argument can be maintained in debate against any conflict-free opponent attack using monotone commitment games, with three variants that differ from prior weak semantics on benchmarks like self-defeating attacks and floating assignments.
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Multi-Scale Generative Modeling with Heat Dissipation Flow Matching
HDFM adds a continuous heat-dissipation (blur) process to flow matching, aligns an interpolated path to fix ill-posed inverse heat dissipation, and uses x-prediction to ease high-dimensional regression, yielding better performance than most baselines on image datasets.
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Guaranteed Jailbreaking Defense via Disrupt-and-Rectify Smoothing
DR-Smoothing introduces a disrupt-then-rectify prompt processing scheme into smoothing defenses, delivering tight theoretical bounds on success probability against both token- and prompt-level jailbreaks.
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Automated Approach for Solving Infinite-state Polynomial Reachability Games
An automated sub-exponential algorithm computes winning strategies and ranking certificate witnesses for polynomial reachability games on infinite-state real-variable graphs.
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DR-MMSearchAgent: Deepening Reasoning in Multimodal Search Agents
DR-MMSearchAgent derives batch-wide trajectory advantages and uses differentiated Gaussian rewards to prevent premature collapse in multimodal agents, outperforming MMSearch-R1 by 8.4% on FVQA-test.
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Neurosymbolic Framework for Concept-Driven Logical Reasoning in Skeleton-Based Human Action Recognition
Neurosymbolic framework grounds skeleton motion in learnable pose and dynamics concepts then reasons over them with differentiable logic to recognize actions interpretably on NTU and NW-UCLA benchmarks.
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Image-aware Layout Generation with User Constraints for Poster Design
A deep learning model generates image-aware poster layouts that satisfy user-specified attribute constraints via Gaussian noise sampling and partial layout constraints via a dedicated loss and random mask, reaching state-of-the-art performance.
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Quantifying Multimodal Capabilities: Formal Generalization Guarantees in Pairwise Metric Learning
Derives novel generalization error bounds for multimodal pairwise metric learning showing that fine-grained modality features reduce hypothesis space complexity via enhanced complementarity.
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