8891 papers
This paper presents a controllable video diffusion framework that transforms operating room scenes into abstract geometric representations to synthesize realistic routine and rare safety-critical events, thereby overcoming data scarcity challenges and enabling the development of ambient intelligence models for detecting sterile-field violations.
The paper proposes MMLoP, a highly parameter-efficient multi-modal prompting framework that utilizes low-rank factorization and novel regularization techniques to achieve state-of-the-art vision-language adaptation performance with only 11.5K trainable parameters.
This paper introduces a training-free, open-vocabulary zero-shot temporal action segmentation framework that leverages the capabilities of diverse Vision-Language Models to segment video actions without task-specific supervision, demonstrating their strong potential for structured temporal understanding.
This paper introduces the WildSVG Benchmark, comprising real-world and synthetic datasets, to address the lack of evaluation resources for SVG extraction and reveals that while current multimodal models struggle with noisy natural images, iterative refinement methods offer a promising path toward reliable performance.
This paper introduces \textit{tugturn.py}, a reproducible Python-based pipeline that automates 3D markerless Timed Up and Go analysis by segmenting trial phases, detecting gait events, and calculating comprehensive spatiotemporal and stability metrics to support clinical and research decision-making.
This paper introduces PSF-Med, a benchmark revealing significant paraphrase sensitivity in medical Vision Language Models, and demonstrates that causal intervention on a specific sparse feature identified via Sparse Autoencoders can substantially reduce these instability risks while maintaining accuracy.
This paper proposes a causal decoding framework that intervenes during generation to attenuate spurious dependencies, effectively reducing object hallucinations in Multimodal Large Language Models while maintaining high descriptive quality and achieving state-of-the-art faithfulness.
This study evaluates the vulnerability of deep learning-based thyroid nodule segmentation in ultrasound to two distinct adversarial attacks, revealing that while spatial-domain perturbations can be partially mitigated by inference-time defenses, frequency-domain attacks remain largely unaddressed, underscoring modality-specific challenges in robustness.
This paper proposes the Efficient Perceptual Bi-directional Attention Network (Efficient-PBAN), an image-level framework trained on a new human-annotated dataset to optimize single-image super-resolution by integrating a differentiable perceptual loss that aligns reconstruction with human visual preferences, thereby resolving the trade-off between fidelity and visual quality.
This paper introduces SPL, a unified training framework that combines semantic pseudo-labeling and a novel multi-stage prototype learning strategy to achieve state-of-the-art performance in both unsupervised and sparsely-supervised 3D object detection by effectively generating high-quality pseudo-labels and stabilizing feature representation learning.
The paper proposes the Asymmetric Hierarchical Attention Network (AHAN), a novel architecture featuring hierarchical cross-attention, facial asymmetry analysis, and twin-aware regularization to significantly improve identical twin face verification accuracy by capturing subtle non-genetic variations.
WaterVIB is a theoretically grounded framework that enhances robustness against AIGC-based regeneration attacks by reformulating the watermark encoder as a Variational Information Bottleneck to extract minimal sufficient statistics invariant to generative shifts, thereby outperforming existing state-of-the-art methods.
This paper introduces TEA-CXA, a tool-expertise-aware chest X-ray agent that utilizes multimodal agentic learning to empirically resolve conflicts between error-prone medical tools by learning their reliability across different query types, while also extending reinforcement learning frameworks to support complex multimodal interactions.
LiLo-VLA is a modular framework that achieves robust zero-shot generalization for long-horizon manipulation tasks by decoupling global transport from object-centric interaction, thereby mitigating cascading failures and significantly outperforming state-of-the-art VLA models in both simulation and real-world benchmarks.
This paper proposes a novel framework for unposed sparse-view outdoor 3D reconstruction that combines bidirectional diffusion-guided pseudo-view restoration with confidence-based deblurring and a scene perception Gaussian management strategy to overcome geometric inconsistencies and improve fidelity under extreme view sparsity.
IHF-Harmony is a novel unified framework that leverages an invertible hierarchy flow and artifact-aware normalization to achieve scalable, high-fidelity, and anatomy-preserving harmonization of multi-modality MRI data using unpaired datasets.
VasGuideNet is a novel Couinaud liver segmentation framework that explicitly leverages vascular topology features encoded via Graph Convolutional Networks and integrated through cross-attention, alongside a Structural Contrastive Loss, to achieve superior anatomical consistency and segmentation accuracy compared to existing state-of-the-art methods.
GPOcc is a novel framework that enhances monocular and streaming 3D occupancy prediction by leveraging generalizable visual geometry priors to generate volumetric Gaussian primitives, achieving significant improvements in accuracy and inference speed over existing state-of-the-art methods.
This paper presents MultiAnimate, an extensible framework leveraging Diffusion Transformers and novel Identifier components to achieve state-of-the-art, identity-preserving pose-guided video animation for multiple characters, including scenarios with more participants than seen during training.
The paper proposes SEF-MAP, a robust multimodal HD map prediction framework that disentangles BEV features into four semantic subspaces with dedicated experts and an uncertainty-aware gating mechanism to effectively handle modality inconsistencies and degraded conditions, achieving state-of-the-art performance on nuScenes and Argoverse2 benchmarks.