1864 papers
This paper proposes Soft Equivariance Regularization (SER), a lightweight, plug-in method that decouples invariance and equivariance objectives by enforcing equivariance on intermediate spatial features while preserving invariance on the final embedding, thereby improving both linear evaluation accuracy and robustness to geometric perturbations without requiring auxiliary heads or transformation labels.
This paper introduces HARP, a deep learning framework that harmonizes multi-site in-vivo diffusion MRI data by training exclusively on easily transportable phantom scans, thereby eliminating the need for impractical multi-site human cohorts while significantly reducing inter-scanner variability.
This paper introduces a method that enhances medical Vision-Language Models by using sequential eye-tracking data as supervision to train dedicated gaze tokens, enabling the models to mimic radiologists' visual search patterns and achieve state-of-the-art performance in both in-domain and out-of-domain medical reasoning tasks.
This paper investigates the severe dimensional collapse and resulting robustness fragility that occur when distilling a large Vision Transformer into capacity-constrained CNNs, revealing that while larger student models pack information densely but lose noise immunity, extremely small models act as robust low-pass filters due to fundamental geometric limitations in asymmetric cross-modal transfer.
This paper introduces gRef-CW, the first benchmark dataset for generalised visual grounding in agriculture that includes negative expressions, and proposes Weed-VG, a modular framework designed to overcome the domain gap and effectively localise crop and weed instances under challenging field conditions.
This paper introduces the SIQA framework, which redefines scientific image quality assessment by distinguishing between perceptual alignment and scientific correctness, and demonstrates through a new benchmark that current multimodal models often achieve high scoring consistency with experts while lacking genuine scientific understanding.
This paper argues that RGB-only Multimodal Large Language Models fail to generalize across different cameras due to entangled perspective and object properties, and proposes a Camera-Aware MLLM framework that integrates camera intrinsics, augmented data, and 3D geometric priors to achieve robust, generalizable spatial intelligence.
This paper enhances the reliability of solar flare regression by applying conformal prediction to deep learning models, demonstrating that conformalized quantile regression outperforms alternative methods in achieving valid coverage rates and favorable interval lengths for space weather forecasting.
This paper introduces Universal Watermark Presence Detection (UWPD), a novel task for identifying invisible watermarks without prior algorithm knowledge, supported by the UniFreq-100K dataset and the Frequency Shield Network (FSNet) model that achieves superior zero-shot detection by dynamically amplifying high-frequency watermark signals while suppressing semantic content.
This paper introduces the Open-Vocabulary Temporal Sentence Grounding (OV-TSGV) task with new benchmarks (Charades-OV and ActivityNet-OV) and proposes HERO, a hierarchical embedding-refinement framework that achieves state-of-the-art performance by effectively generalizing to novel linguistic expressions through multi-level semantic modeling and cross-modal refinement.
This paper proposes a vessel-aware deep learning framework for detecting age-related macular degeneration (AMD) in OCTA images by integrating external multiplicative attention with clinically meaningful vascular biomarkers, specifically tortuosity and dropout maps, to guide the model toward physiologically relevant regions and improve interpretability.
This paper introduces an efficient framework for heterogeneous decentralized diffusion models that enables experts to train with mixed objectives (DDPM and Flow Matching) and reduced resource requirements, achieving a 16x decrease in compute and 14x reduction in data compared to prior approaches while improving image quality and diversity.
ButterflyViT introduces a geometric parameterization method that treats Mixture of Experts as rotations of a shared quantized substrate, achieving a 354 memory reduction for Vision Transformers on edge devices while maintaining accuracy through spatial smoothness regularization.
This paper introduces XMACNet, an explainable, lightweight CNN that combines self-attention mechanisms with multi-modal fusion of RGB images and vegetation indices to achieve high-accuracy chili disease classification suitable for edge deployment.
This paper introduces EarthBridge, a high-fidelity cross-modal translation framework combining Diffusion Bridge Implicit Models and Contrastive Unpaired Translation to achieve second place in the 4th Multi-modal Aerial View Image Challenge by effectively translating between SAR, EO, and IR aerial imagery.
The paper proposes HiDE, a hierarchical dictionary-based entropy modeling framework for learned image compression that enhances coding efficiency by decomposing external priors into global and local dictionaries with cascaded retrieval and employing a context-aware parameter estimator to achieve significant BD-rate savings over state-of-the-art methods.
This paper presents a hybrid machine learning model that combines VGG19, EfficientNet, and ResNet50 for feature extraction with a Bi-LSTM classifier to achieve a 98.83% accuracy in the early detection of Cerebral Palsy from MRI images, outperforming several individual pre-trained models.
This paper establishes that the quality of a model's step-level visual grounding, quantified by the Step Grounding Rate (SGR), serves as a robust and independent predictor of out-of-distribution generalization in long-horizon vision-language models, outperforming traditional final-answer accuracy metrics.
This paper introduces MotionBits, a novel concept and learning-free segmentation method that identifies the smallest manipulable rigid bodies through kinematic spatial twist equivalence, outperforming state-of-the-art embodied perception models on the new MoRiBo benchmark and enabling more effective downstream robotic manipulation and reasoning tasks.
This paper introduces Perturbed Gaussian Ensemble, an active view selection framework for sparse-view CT that leverages stochastic density scaling of uncertain Gaussian primitives to identify high-variance projections, thereby significantly improving reconstruction fidelity and reducing geometric artifacts compared to existing methods.