8650 papers
This paper proposes a novel multimodal learning framework that decomposes representations into invariant and specific components to derive tractable Probability of Necessity and Sufficiency (PNS) objectives, thereby enhancing predictive performance and robustness to missing modalities in medical data analysis.
Proof-of-Perception (PoP) is a tool-using multimodal reasoning framework that generates calibrated, stepwise uncertainty via conformal sets to dynamically allocate computational resources, thereby reducing hallucinations and improving accuracy-efficiency trade-offs compared to existing baselines.
This paper proposes a novel conditional diffusion framework for low-light image enhancement that utilizes a Structured Control Embedding Module (SCEM) to decompose input images into physical priors, achieving state-of-the-art performance and strong generalization across multiple benchmarks without fine-tuning.
This paper presents a percept-aware surgical planning framework that optimizes electrode placement for cortical visual prostheses by formulating it as a differentiable constrained optimization problem, which simultaneously maximizes perceptual reconstruction fidelity and adheres to critical vascular safety and anatomical feasibility constraints.
This study presents a deep learning framework for meat freshness detection that combines U-Net-based segmentation with OOD-aware classification, demonstrating that EfficientNet-B0 achieves the highest accuracy (98.10%) on RGB images while supporting practical on-device deployment via TensorFlow Lite.
This paper presents a novel unsupervised framework for segmenting large-scale synchrotron computed tomography datasets that generates initial pseudo labels via voxel clustering and refines them using an Unbiased Teacher approach, thereby eliminating the need for manual annotation while significantly improving segmentation accuracy.
DiffSOS is a novel acoustic conditional diffusion model that achieves high-fidelity, near real-time Speed-of-Sound reconstruction in Ultrasound Computed Tomography by leveraging a physics-grounded ControlNet and stochastic sampling to overcome the oversmoothing and computational limitations of existing methods while providing pixel-wise uncertainty estimates.
The paper introduces SSR, a 7B-parameter framework that achieves state-of-the-art spatial intelligence by integrating 2D and 3D representations through lightweight alignment and a novel scene graph generation pipeline, enabling precise geometric reasoning without costly large-scale pre-training.
To overcome the scarcity of 3D-text data and the resulting loss of geometric information in existing 3D Vision-Language Models, PointAlign introduces a lightweight feature-level alignment regularization that explicitly supervises intermediate point cloud tokens to preserve fine-grained 3D geometric-semantic details, significantly improving performance on classification and captioning tasks.
This paper presents DiffTrans, a differentiable rendering framework that utilizes FlexiCubes for initial geometry and a recursive CUDA-based ray tracer to jointly optimize geometry, refractive index, and absorption, enabling high-quality reconstruction of transparent objects with diverse topologies and complex textures in intricate scenes.
The paper proposes Query-Conditioned Gaussian Splatting (QCGS), a novel framework that fuses sparse weather station data with satellite imagery to efficiently generate high-resolution precipitation fields by selectively rendering only rainfall regions, achieving over 50% improvement in RMSE compared to conventional products.
This paper introduces InstructX2X, an interpretable local editing model that leverages region-specific editing and a new expert-verified dataset (MIMIC-EDIT-INSTRUCTION) to generate high-quality counterfactual medical images while preventing unintended demographic changes and providing visual explanations for the editing process.
The paper introduces Fact-Flow, a novel framework that enhances the factual accuracy of MLLM-based medical report generation by decoupling visual fact identification from text generation and utilizing an LLM-bootstrapped pipeline to create labeled training data without manual annotation.
This paper proposes Taxonomy-Aware Representation Alignment (TARA), a method that enhances Large Multimodal Models' hierarchical visual recognition capabilities for both known and novel categories by aligning their visual representations with biology foundation models and ground-truth labels to enforce taxonomic consistency.
This paper proposes TAP-SLF, a parameter-efficient framework that combines task-aware soft prompts and selective fine-tuning of top encoder layers to effectively adapt Vision Foundation Models for multi-task ultrasound image analysis while minimizing overfitting and computational costs.
This paper introduces ICLA, an internal self-correction mechanism that leverages a diagonal cross-layer attention mechanism to enable Large Vision-Language Models to refine their own hidden states and mitigate hallucinations without external signals, demonstrating consistent improvements across benchmarks with minimal additional parameters.
Mamba-CAD is a self-supervised generative modeling framework that leverages a Mamba-based encoder-decoder architecture and a new large-scale dataset to effectively generate complex, long-sequence parametric CAD models for industrial applications.
SesaHand is a novel framework that enhances 3D hand reconstruction by generating diverse, high-quality synthetic hand images through a controllable generation pipeline that ensures semantic alignment via Chain-of-Thought reasoning and structural alignment via hierarchical fusion and attention mechanisms.
This paper proposes an improved adversarial diffusion compression method that distills a heavy 3D diffusion Transformer into a lightweight 2D-based model with 1D temporal convolutions and a dual-head adversarial scheme, achieving a 95% reduction in parameters and 8 speedup while effectively balancing spatial detail and temporal consistency for real-world video super-resolution.
The paper introduces LSS-LTCNet, an efficient and explainable framework that combines Local Self-Similarity texture priors with continuous-time neural dynamics to achieve state-of-the-art foot ulcer segmentation and boundary precision on the MICCAI FUSeg dataset.