1863 papers
The paper introduces RADAR, a multimodal benchmark comprising expert-annotated 3D abdominal CT scans and radiology report edits that enables the systematic evaluation of AI models on fine-grained clinical reasoning tasks, specifically image-text alignment and discrepancy assessment during the radiology report review process.
The paper proposes ECHO, a multi-agent framework that utilizes iterative hypergraph operations and a "Link-then-Bind" strategy to mitigate cascading errors in Multimedia Event Extraction, achieving significant performance improvements over state-of-the-art methods on the M2E2 benchmark.
This paper presents an innovative, mobile-device-based 3D imaging approach using Structure-from-Motion and segmentation algorithms to reconstruct and analyze aggregate size and shape from stockpiles, offering a convenient and affordable solution for onsite quality assurance in road construction and geotechnics.
This paper introduces TimeSpot, a comprehensive benchmark comprising 1,455 real-world images from 80 countries designed to evaluate the limited geo-temporal reasoning capabilities of current vision-language models in predicting location, time, and environmental context from visual evidence alone.
The paper introduces "Narrative Weaver," a novel framework that achieves controllable, long-range visual consistency in generative AI by integrating multimodal narrative planning with a dynamic memory bank, validated through extensive experiments and a newly released e-commerce advertising dataset.
This paper presents an unsupervised learning framework utilizing convolutional filtering and neural networks with optimized early-stopping to achieve robust, high-fidelity reconstruction of ion-beam emittance images from noisy data, enabling unprecedented halo resolution beyond seven standard deviations for particle accelerator diagnostics.
This study evaluates the adaptability of the TerraMind geospatial foundation model to hyperspectral imaging tasks without native pretraining, finding that while band selection strategies allow for moderate performance, deep learning models with native spectral support remain superior, thereby highlighting the critical need for future architectures to incorporate native spectral tokenization.
This paper introduces a robust one-shot badminton shuttlecock detection framework for mobile robots, featuring a novel semi-automatically annotated dataset and a fine-tuned YOLOv8 model that achieves high performance in dynamic, egocentric viewpoints across diverse environments.
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.