8938 papers
This paper introduces NoLan, a training-free framework that mitigates object hallucinations in Large Vision-Language Models by identifying language decoder priors as the primary cause and dynamically suppressing them during decoding.
CASR is a robust, single-model cyclic framework for arbitrary-scale super-resolution that mitigates cross-scale distribution shifts and texture inconsistencies by reformulating ultra-magnification as a sequence of in-distribution transitions guided by structural alignment and self-similarity priors.
This paper proposes a region-level mixing encoder that fuses multi-magnification tile representations through masked embedding modeling pretraining to enhance generalizable region-level features and improve biomarker prediction performance in computational pathology.
This paper demonstrates that off-the-shelf image-to-image generative AI models can be simply repurposed as generic denoisers to effectively defeat a wide range of image protection schemes, outperforming specialized attacks and revealing a critical vulnerability in current defense mechanisms.
WHOLE is a novel method that holistically reconstructs hand and object motion in world space from challenging egocentric videos by leveraging a learned generative prior to jointly reason about their interactions, thereby achieving state-of-the-art performance in handling occlusions and ensuring consistent hand-object relations.
This paper addresses the challenge of partitioning input variables in Shapley value-based Explainable AI by analyzing attribution conflicts caused by AND-OR interactions, proposing a new attribution metric for variable coalitions and three faithfulness evaluation metrics that are validated across diverse domains.
This paper proposes Proto-Caps, an interpretable medical image classification model that integrates capsule networks, prototype learning, and privileged information to achieve state-of-the-art accuracy in lung nodule malignancy prediction while providing visual, case-based reasoning for radiologist validation.
This paper introduces PILOT, a tuning-free image inpainting method that optimizes latent spaces using semantic centralization and background preservation losses to generate coherent, multi-modal content that seamlessly integrates with pre-trained diffusion models.
ScaleDepth is a novel monocular depth estimation method that decomposes metric depth into scene scale and relative depth via semantic-aware and adaptive modules, enabling robust, unified metric depth prediction across diverse indoor and outdoor scenes without requiring scene-specific fine-tuning or depth range settings.
DreamBarbie is a novel text-driven framework that generates high-quality, animatable 3D avatars with the iconic "Barbie doll" aesthetic by utilizing an SDF-based G-Shell representation and specialized expert diffusion models to produce fine-grained, disentangled components like shoes and simulation-ready garments.
This study presents UWF-RI2FA, a generative AI model that synthesizes realistic, multi-frame, dye-free ultrawide-field fluorescein angiography images from noninvasive retinal imaging, significantly improving diabetic retinopathy stratification accuracy compared to using retinal imaging alone.
RegTrack challenges the notion that complexity is essential for robustness by introducing an efficient, generalizable multi-modal 3D tracking method that utilizes a unified tri-cue encoder inspired by Yang-Mills gauge theory to achieve state-of-the-art performance on KITTI and nuScenes using only 2.6M parameters and point cloud inputs during inference.
This paper presents and validates two neural network operator-based algorithms for modeling and enhancing remote sensing images, demonstrating their superior performance over classical interpolation methods like bilinear and bicubic techniques, particularly in terms of Structural Similarity Index (SSIM).
This paper proposes a variational approach for surface segmentation on triangular meshes that compares two total variation regularizers, demonstrating that a label-space regularizer yields superior noise removal at the cost of higher computation, which is mitigated by introducing a manifold Newton scheme to efficiently solve the associated Riemannian center of mass problem.
This paper investigates the generalization failure of low-level vision models in image deraining, revealing that networks overfit simple degradation patterns when background content is complex, and proposes balancing data complexity and leveraging generative priors to redirect learning toward high-quality image reconstruction.
The paper introduces EAGLE, a deep learning framework that leverages foundation models to selectively analyze informative regions in pathology images, achieving state-of-the-art performance across 43 tasks while reducing computational time by over 99% to enable real-time, accessible, and auditable clinical workflows.
The paper introduces SEED, a novel semantic evaluation metric for visual brain decoding that integrates three neuroscientifically inspired components to achieve superior alignment with human judgments, revealing critical limitations in current state-of-the-art models and providing open-source data to guide future advancements.
VisionLogic is a novel neural-symbolic framework that generates faithful, hierarchical explanations for vision models by learning activation thresholds to form predicates, inducing class-level logical rules, and grounding these rules in causally validated visual concepts through ablation-based testing.
This paper introduces ELogitNorm, a novel hyperparameter-free training method that addresses feature collapse in Logit Normalization by adding a feature distance-awareness loss term, thereby significantly improving out-of-distribution detection and in-distribution confidence calibration without compromising classification accuracy.
This paper proposes an efficient LiDAR-camera fusion framework featuring a UniMT detector and an RTMCT predictor that achieves state-of-the-art 3D object detection and trajectory prediction performance while enabling real-time inference on resource-constrained service robots.