1863 papers
This paper proposes an interpretable 3D Concept Bottleneck Model that integrates morphological and hemodynamic clinical features to classify intracranial aneurysms with high accuracy (up to 93.33%) while ensuring clinical transparency and regulatory compliance.
This paper introduces VIVECaption, a systematic two-sided approach that combines a comprehensive evaluation taxonomy with a gold-standard dataset creation methodology and model alignment strategy to significantly improve image-caption alignment quality for training text-to-image and text-to-video generative models using copyright-safe "vegan" data.
This paper addresses the lack of specialized dental datasets by proposing a framework that uses Vision-Language Models with guided prompts to generate high-quality, holistic captions for single-tooth RGB images, thereby enabling more comprehensive dental image analysis.
The paper introduces UnSCAR, a scalable and controllable universal image restoration framework that utilizes a multi-branch mixture-of-experts architecture to overcome the limitations of catastrophic forgetting and performance degradation in existing all-in-one models when handling multiple real-world degradations.
This paper introduces QdaVPR, a novel query-based visual place recognition model that achieves state-of-the-art domain-agnostic performance across diverse environmental conditions by employing a dual-level adversarial learning framework and query-based triplet supervision trained on augmented synthetic domains.
This paper proposes DTPSR, a novel diffusion-based image super-resolution framework that leverages disentangled textual priors across spatial and frequency dimensions, supported by a new large-scale dataset and multi-branch guidance, to achieve high perceptual quality and semantic controllability.
This paper addresses the underexplored challenge of generalization in online reinforcement learning for mobile GUI agents by introducing the AndroidWorld-Generalization benchmark and a scalable GRPO-based training system, demonstrating that while RL significantly improves zero-shot performance on unseen task instances, generalization to new templates and applications remains difficult and benefits from test-time few-shot adaptation.
The paper proposes Data Agent, an end-to-end dynamic data selection framework that formulates sample selection as a training-aware sequential decision-making problem to accelerate model training while preserving performance across diverse datasets and tasks.
RPG-SAM is a training-free one-shot polyp segmentation framework that improves performance by addressing regional and response heterogeneity through reliability-weighted prototype mining, geometric adaptive threshold selection, and iterative boundary refinement, achieving a 5.56% mIoU gain on the Kvasir dataset.
DogWeave is a novel framework that reconstructs high-fidelity 3D canine models from a single RGB image by refining parametric meshes into detailed SDF representations via diffusion-enhanced normal optimization and generating view-consistent textures through conditional inpainting, thereby overcoming challenges like self-occlusion and fur detail to outperform existing state-of-the-art methods.
Med-Evo is a novel self-evolution framework for medical multimodal large language models that leverages label-free reinforcement learning, featuring Feature-driven Pseudo Labeling and Hard-Soft Reward mechanisms, to significantly enhance model performance on unlabeled test data without requiring additional annotated medical datasets.
The paper introduces SLNet, a super-lightweight 3D point cloud recognition network utilizing Nonparametric Adaptive Point Embedding (NAPE) and Geometric Modulation Units (GMU) to achieve state-of-the-art accuracy on benchmarks like ModelNet40 and ScanObjectNN with significantly fewer parameters and computational costs compared to existing models.
This paper provides a comprehensive technical survey of the history and evolution of image generation models, detailing the objectives, architectures, and limitations of various approaches from VAEs to diffusion methods, while also addressing recent advancements in video generation and the critical challenges of robustness and responsible deployment.
SIGMAE is a novel foundation model for multispectral remote sensing that enhances Masked Autoencoder pretraining by incorporating domain-specific spectral indices to guide dynamic token masking toward semantically salient regions, thereby achieving superior performance across various downstream tasks compared to existing geospatial models.
This paper introduces MonoSTL, a selective transfer learning framework that addresses the negative transfer caused by modality gaps in cross-modality distillation for monocular 3D object detection by employing similar architectures and novel depth-aware selective distillation modules to effectively transfer LiDAR depth information to image-based networks, achieving state-of-the-art performance on KITTI and NuScenes benchmarks.
This paper introduces the ThingiPrint dataset and a contrastive fine-tuning approach that enables the classification of novel 3D-printed objects using their CAD models without requiring model retraining, thereby addressing a critical bottleneck in automating industrial post-production workflows.
FedEU is a novel federated learning framework that enhances remote sensing image segmentation by integrating evidential uncertainty quantification and client-specific feature embeddings to guide adaptive global aggregation, thereby improving model robustness and reliability across heterogeneous distributed datasets.
This paper introduces Early Vision-Language Fusion (EVLF), a plug-and-play method that aligns textual and visual embeddings early in the diffusion process to overcome the visual dominance issues of late-stage guidance, thereby generating semantically faithful and visually coherent synthetic datasets that improve downstream classification accuracy.
This paper proposes a Multi-Modal Decouple and Recouple Network that enhances robust 3D object detection under data corruption by explicitly separating BEV features into invariant and specific components to enable cross-modal compensation, followed by an adaptive fusion of three specialized experts tailored to different corruption scenarios.
This paper introduces RobustSCI, a pioneering framework that shifts snapshot compressive imaging from simple reconstruction to robust restoration by proposing a novel network architecture and a large-scale benchmark to effectively recover pristine scenes from real-world degraded measurements caused by motion blur and low light.