3489 papers
This paper introduces BATDiff, an unsupervised single-image super-resolution model that leverages bivariate A-trous wavelet transforms and cross-scale parent-child dependencies to generate sharper, more structurally consistent high-frequency details while minimizing artifacts and dataset-driven hallucinations.
The paper proposes HY-WU, a memory-first adaptation framework that replaces static weight overwriting with a functional neural memory module to synthesize instance-specific weight updates on-the-fly, thereby enabling robust continual learning and personalization without degrading previously learned behaviors.
FabricGen is an end-to-end framework that generates realistic woven fabric materials from text by decoupling macro-scale texture synthesis via fine-tuned diffusion models from micro-scale weaving pattern generation using a specialized LLM-driven procedural geometric model.
This paper introduces PresentBench, a fine-grained, rubric-based benchmark comprising 238 instances and detailed binary checklists that enables more reliable, human-aligned evaluation of automated slide generation, revealing that NotebookLM currently outperforms other methods in this domain.
This paper introduces LEPA, a learned architecture that conditions on geometric augmentations to accurately predict transformed satellite image embeddings, effectively overcoming the limitations of standard interpolation in non-convex geospatial foundation model manifolds and significantly improving geometric adjustment performance.
This paper introduces Variational Flow Maps (VFMs), a framework that enables high-quality, single-step conditional generation and inverse problem solving by learning a noise adapter to align the initial noise distribution with observations, thereby bypassing the need for iterative sampling trajectories required by traditional diffusion models.
This paper introduces BD-VITON, a new benchmark dataset featuring culturally diverse Bangladeshi garments with complex draping and layering challenges, and evaluates the performance of state-of-the-art virtual try-on models on this dataset to demonstrate significant improvements over zero-shot inference.
This paper introduces MAviS, a domain-adaptive multimodal conversational assistant for avian species that leverages the newly created MAviS-Dataset and is evaluated on the MAviS-Bench to achieve state-of-the-art performance in fine-grained bird species understanding and multimodal question answering.
This paper proposes a training-centered approach that combines adversarial training with a lightweight feature-map smoothing block to generate saliency maps that are simultaneously sparse, input-stable, and output-stable, thereby enhancing their perceived trustworthiness and sufficiency.
This paper introduces StructSAM, a novel token merging framework that preserves structural boundaries and spectral properties in Segment Anything Models (SAM) by using gradient-based energy scores and grid-based screening to achieve significant computational savings with minimal accuracy loss across natural and medical imaging benchmarks.
Faster-HEAL is a lightweight, privacy-preserving collaborative perception framework that addresses the challenges of heterogeneous autonomous vehicles by using low-rank visual prompt fine-tuning and pyramid fusion to align diverse features into a unified space, achieving superior detection performance with significantly reduced computational overhead compared to state-of-the-art methods.
This paper proposes a lightweight, digital-twin-based framework for edge-assisted vehicle tracking and collision prediction that achieves approximately 88% accuracy by relying on object detection and K-D tree-indexed path maps instead of computationally intensive trajectory prediction models.
The AgrI Challenge introduces a data-centric competition framework featuring Cross-Team Validation to demonstrate that while single-source training suffers from significant generalization gaps in agricultural vision, collaborative multi-source training on independently collected, heterogeneous datasets dramatically improves model robustness and real-world performance.
The paper introduces N-Tree Diffusion (NT-Diffusion), a hierarchical diffusion model that improves long-horizon wildfire risk forecasting by sharing early denoising stages across prediction horizons to reduce computational redundancy while maintaining probabilistic accuracy.
By tracking macaque V4 neural responses during visual discrimination learning, the study demonstrates that task learning increases information redundancy rather than reducing it, supporting a Bayesian generative inference model where redundancy enhances the information carried by individual neurons.
This paper introduces AQuA, a fine-grained dataset that categorizes ambiguous visual questions into four levels with corresponding optimal response strategies, demonstrating that fine-tuning Vision-Language Models on this dataset enables them to effectively recognize ambiguity and adaptively generate context-appropriate responses such as seeking clarification or listing alternatives, thereby outperforming existing baselines.
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.