4391 papers
ViterbiPlanNet introduces a principled framework that injects procedural knowledge into instructional video planning via a Differentiable Viterbi Layer, achieving state-of-the-art performance with significantly fewer parameters and improved sample efficiency compared to existing large-scale models.
This paper proposes SSR, a novel text-aided map compression framework that leverages lightweight text descriptions and complementary image feature vectors to achieve superior memory and bandwidth efficiency while maintaining high-fidelity localization performance across diverse indoor and outdoor environments.
This multi-center study evaluates deep learning methods for real-time video polyp detection and segmentation, demonstrating that integrating sequence data and temporal information significantly enhances diagnostic precision by addressing the challenges of variable polyp appearance and reducing missed detection rates in colonoscopy.
CubeComposer is a novel spatio-temporal autoregressive diffusion model that overcomes the computational limitations of existing methods to natively generate high-quality, seam-free 4K-resolution 360° videos from perspective inputs by decomposing them into cubemap representations and employing efficient context management and continuity-aware techniques.
The paper proposes MMFA, a novel unsupervised method that decouples identity from motion information through self-supervised representation learning and a new keypoint computation strategy, enabling controllable and interpolatable face animation with realistic results.
The paper introduces PromptAvatar, a framework utilizing dual diffusion models trained on a novel large-scale multi-modal dataset to generate high-fidelity, shading-free 3D avatars from text or image prompts in under 10 seconds, overcoming the slow inference and limited generalization of existing methods.
This paper introduces CRESTomics, a novel kernel-based additive model with coherence loss and group-sparse regularization, to accurately and interpretably identify radiomics-based markers in carotid plaques from the CREST-2 trial that link B-mode ultrasound texture features to high clinical stroke risk.
This paper introduces MOO, a large-scale synthetic multi-view cattle ReID dataset with precise angular annotations, which enables the identification of critical elevation thresholds for improved cross-view generalization and demonstrates significant performance gains when applied to real-world agricultural scenarios.
The paper introduces SPRINT, the first semi-supervised prototypical framework for few-shot class-incremental learning on tabular data, which leverages abundant unlabeled data and low storage costs to achieve state-of-the-art performance across diverse real-world domains.
This paper presents a scalable framework using vision-language models and distributional analysis to evaluate synthetic environmental augmentations, demonstrating that generative AI significantly outperforms rule-based methods in creating realistic adverse-condition images for AI system testing.
Pointer-CAD is a novel LLM-based framework that unifies B-rep models and command sequences through pointer-based entity selection, effectively overcoming the limitations of traditional command-only methods by enabling complex geometric editing and significantly reducing topological errors caused by quantization.
ArtHOI is a novel zero-shot framework that synthesizes physically plausible articulated human-object interactions by formulating the task as a 4D reconstruction problem from monocular video priors, utilizing flow-based segmentation and a decoupled optimization pipeline to overcome the limitations of existing rigid-object methods.
This study benchmarks Denoising Diffusion Probabilistic Models, Latent Diffusion Models, and Flow Matching for synthetic cardiac MRI generation, revealing that DDPM offers the optimal balance of image fidelity, segmentation utility, and privacy preservation under data-scarce conditions.
The paper introduces Hold-One-Shot-Out (HOSO), a validation-free method that learns the blending ratio for CLIP-Adapter using a single hold-out sample, significantly outperforming existing baselines across multiple few-shot datasets without requiring test-set ablation or additional validation sets.
This study demonstrates that augmenting limited real artwork datasets with synthetic images generated via DreamBooth fine-tuning of Stable Diffusion significantly improves the accuracy and generalization of computational authorship attribution models.
This paper proposes a highly data-efficient, one-shot method that leverages a Large Language Model to generate counterfactual descriptions and engineer similarity-based features, enabling the accurate prediction of Vision-Language Foundation Models' zero-shot performance on underrepresented domains without requiring large labeled test sets.
The paper proposes RANGER, a novel framework for pathology report generation that combines a sparsely-gated Mixture-of-Experts decoder with an adaptive retrieval re-ranking module to overcome limitations in existing transformer-based approaches by enabling dynamic expert specialization and reducing noise from external knowledge retrieval.
FocusGraph is a novel framework for embodied long video question answering that combines a lightweight Scene-Caption LLM Selector for identifying query-relevant clips and a training-free Patch-wise Sparse-Flow Retention method for keyframe selection, achieving state-of-the-art performance on egocentric benchmarks while significantly reducing inference time.
Helios is a pioneering 14B autoregressive diffusion model that achieves real-time, minute-scale video generation with high robustness and quality on a single GPU, eliminating the need for common anti-drifting heuristics, standard acceleration techniques, or parallelism frameworks through novel training strategies and infrastructure optimizations.
TaxonRL is a reinforcement learning framework that employs hierarchical intermediate rewards to decompose fine-grained visual reasoning into structured taxonomic steps, achieving state-of-the-art accuracy and interpretable decision-making that surpasses human performance on challenging species classification tasks.