5403 papers
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.
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 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 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 introduces the Fifth Graph Normal Form (5GNF), a trait-based framework that normalizes property graph metadata by extracting recurring attributes into shared Trait Nodes, thereby reducing redundancy and improving semantic clarity while maintaining competitive query performance.
This paper proposes a novel approach to mining data-aware temporal specifications from execution traces by combining Syntax Guided Synthesis with a finite-prefix interpretation of Temporal Stream Logic (TSL), enabling the robust and sample-efficient synthesis of reactive programs that capture both data transformations and temporal behaviors.
This paper presents a pivot-based, 3D-printed "kiri-spoon" that functions as both a hand-held and robot-assisted utensil, utilizing a plier-like mechanism to securely grasp food and significantly reduce spills for users with tremors and mobility limitations.
This paper proposes a hierarchical planning approach that integrates supplemental geostationary satellite data to extend lookahead horizons for Dynamic Targeting missions, demonstrating up to a 41% performance improvement over traditional onboard-only planners, particularly in scenarios with sparsely distributed targets.
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 presents "Agent Hunt," a decentralized autoformalization system for algebraic topology that utilizes a simulated bounty-based marketplace to coordinate multiple LLM agents in dynamically proposing, competing to prove, and iteratively refining formal statements within an Interactive Theorem Proving environment.
The paper introduces ResearchEnvBench, a new benchmark designed to evaluate autonomous agents' ability to synthesize complex execution environments for research code, revealing significant current limitations in dependency resolution and version management.
The paper introduces ViroGym, a comprehensive benchmark comprising extensive deep mutational scanning data and real-world tasks to evaluate protein language models for predicting viral variant effects and guiding rational antigen selection for vaccine development.