1863 papers
This paper introduces "differentiable microscopy" (), a data-driven, top-down design framework that automatically optimizes optical systems for phase retrieval, demonstrating superior performance over existing methods and experimentally validating its effectiveness on biological samples.
This paper proposes a mutual conditional blended-target domain adaptation framework that aligns categorical distributions and rectifies classifier bias through uncertainty-guided discrimination and low-level feature augmentation, achieving state-of-the-art performance even without explicit domain labels and under label distribution shifts.
The paper introduces altiro3D, a free library that synthesizes realistic 3D experiences and novel views from a single RGB image or video by combining monocular depth estimation, inpainting, and optimized projection algorithms to generate multi-viewpoint light fields for free-view displays.
This paper introduces PD-REAL, a novel large-scale RGB-D dataset for unsupervised anomaly detection based on Play-Doh models, and proposes a multi-scale teacher-student framework with hierarchical distillation that leverages 3D information to achieve superior detection accuracy compared to existing methods.
DivCon introduces a divide-and-conquer framework that decouples text-to-image generation into simplified numerical/spatial reasoning and progressive object synthesis steps, enabling lightweight models to achieve superior layout accuracy and perceptual quality for complex multi-object prompts without relying on closed-source large language models.
This paper presents a comprehensive survey and benchmark of deepfake generation and detection, unifying task definitions, reviewing state-of-the-art methods across four key generation fields and forgery detection, and analyzing current challenges and future research directions.
This paper proposes three high-quality, controlled test sets (Hadrian, Eclipse, and ND-Twins) designed to challenge face verification models on natural variations in facial attributes and similar-looking identities, while introducing "Goldilocks" rules to ensure balanced difficulty and demographic fairness without artificially degrading image quality.
This paper identifies a "corruption stage" in few-shot fine-tuned diffusion models caused by a narrowed learning distribution and proposes a Bayesian Neural Network approach with variational inference to broaden this distribution, thereby mitigating corruption and improving image fidelity, quality, and diversity without additional inference costs.
This paper proposes RDM, a recurrent diffusion model that leverages Normalizing Flows to condition generation on preceding noisy frames, enabling efficient, long-duration human motion synthesis with reduced computational costs while maintaining high alignment with text prompts.
The paper proposes PiVOT, a visual prompting mechanism that leverages a pretrained CLIP foundation model to automatically generate and refine online visual prompts, thereby enhancing generic object tracking by effectively suppressing distractors through contrastive guidance.
ExpGest is a novel diffusion-based framework that generates expressive, controllable full-body gestures by leveraging synchronized audio and text guidance, along with a specialized noise emotion classifier, to overcome the limitations of existing methods that often produce stiff, upper-body-only movements.
This paper introduces a neurosymbolic system that reconstructs medical images using visual primitives to generate high-level structural explanations, achieving superior classification accuracy and transparency compared to conventional deep learning models in diagnosing histological abnormalities.
This paper proposes an input-adaptive framework for diffusion models that dynamically adjusts the generative trajectory and sampling steps for each sample based on its complexity, thereby maintaining generation quality while reducing the average number of required steps.
This paper introduces HarmonicEval, a reference-free, multi-criteria evaluation metric for vision-language models that aggregates criterion-wise scores to better align with human judgments across diverse multi-modal tasks, supported by the newly constructed MMHE benchmark containing 18,000 expert human evaluations.
This paper proposes a method that leverages pretrained vision-language models to learn compact, abstract symbolic world models from limited visual demonstrations, enabling zero-shot generalization and long-horizon planning for complex robotic tasks across novel objects, environments, and goals.
This paper proposes efficient data reduction strategies for semi-supervised adversarial training that utilize latent clustering techniques to select or generate critical boundary-adjacent samples, significantly reducing data requirements and computational costs while maintaining state-of-the-art robustness.
This paper proposes a novel, problem-agnostic diffusion model that enhances inverse problems by decomposing the conditional score function into an unconditional pretrained component and a Gaussian-prior-based MAP-guided term, resulting in superior content preservation and structural coherence compared to state-of-the-art methods.
This paper introduces Generative Predictive Control (GPC), a framework that enhances robotic manipulation by combining a diffusion-based policy with an action-conditioned world model to enable online planning and optimization, consistently outperforming standard behavior cloning across diverse simulation and real-world tasks.
Prompt-SID is a self-supervised single-image denoising framework that leverages a latent diffusion-based structural representation generator and a scale replay training mechanism to preserve detailed structural information without relying on expensive paired datasets.
The paper proposes LaVCa, a novel data-driven approach that leverages large language models to generate natural-language captions for images, thereby providing more accurate and detailed interpretations of human visual cortex voxel selectivity and revealing fine-grained functional differentiation within the visual cortex compared to existing deep neural network-based methods.