5329 papers
This paper proposes a LISTA-Transformer model that integrates Learnable Iterative Shrinkage Threshold Algorithm-based sparse coding with the Transformer architecture to overcome the limitations of CNNs and standard Transformers in local and global feature modeling, achieving a 98.5% fault recognition rate on the CWRU dataset through time-frequency signal analysis.
This paper introduces a degradation-based augmented training framework that artificially diversifies image degradations during training to significantly improve the robustness and accuracy of deep learning models for individual animal re-identification across various species and real-world conditions.
The paper introduces PlaneCycle, a training-free and adapter-free method that lifts pretrained 2D foundation models to 3D by cyclically distributing spatial aggregation across orthogonal planes, enabling strong 3D performance without architectural modifications or additional parameters.
This paper establishes a principled theoretical framework for density aggregation by demonstrating that normalized generalized means with order are the only rules guaranteeing systematic improvements in log-likelihood over individual distributions, thereby providing a unified justification for the widespread use of linear and geometric pooling in Deep Ensembles.
The paper introduces Real5-OmniDocBench, the first benchmark that physically reconstructs the entire OmniDocBench v1.5 dataset across five real-world scenarios to rigorously evaluate and diagnose the performance gap of Vision-Language Models in physical document parsing.
This paper proposes a novel encoder-based approach that combines a specialized variational autoencoder with non-parametric nearest-neighbor density estimation to explicitly optimize for independence from sensitive variables, effectively removing unwanted dependencies while preserving essential data utility.
This paper introduces DiverseDiT, a novel framework that enhances Diffusion Transformers by systematically analyzing and explicitly promoting representation diversity across blocks through long residual connections and a diversity loss, resulting in consistent performance gains and faster convergence across various model sizes and generation settings.
The paper proposes DeNuC, a method that decouples nuclei detection and classification by using a lightweight model for localization and a Pathology Foundation Model for feature-based classification, thereby overcoming representation degradation and computational inefficiency to achieve state-of-the-art performance with significantly fewer trainable parameters.
EmbodiedSplat is an online, feed-forward 3D Gaussian Splatting framework that enables simultaneous, near real-time 3D reconstruction and open-vocabulary semantic understanding of streaming scenes by integrating a memory-efficient CLIP-based coefficient field with 3D geometric-aware feature aggregation.
This paper introduces a novel color equivariant architecture that eliminates approximation artifacts in handling saturation and luminance by lifting interval-valued quantities to a circular double-cover, thereby achieving superior robustness, interpretability, and performance in tasks like fine-grained classification and medical imaging.
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.