Computer Vision

SelfFormer: A novel self-supervised transformer-based method outperforms existing techniques by leveraging directional self-attention for efficient and accurate real-world image denoising.

Prune and Repaint: A new content-aware method for superior image retargeting across any aspect ratio, preserving key features and avoiding artifacts.

ProxyFusion, a novel face feature fusion method, achieves real-time performance by using sparse experts to weight features without relying on intermediate representations or metadata, substantially im…

ProvNeRF enhances NeRF reconstruction by modeling per-point provenance as a stochastic field, improving novel view synthesis and uncertainty estimation, particularly in sparse, unconstrained view sett…

Prototypical Hash Encoding (PHE) significantly boosts on-the-fly fine-grained category discovery by using multiple prototypes per category to generate highly discriminative hash codes, thus resolving …

PromptFix: a novel framework enables diffusion models to precisely follow instructions for diverse image processing tasks, using a new high-frequency guidance sampling method and an auxiliary prompt a…

Prompt-Agnostic Adversarial Perturbation (PAP) defends customized diffusion models against image tampering, achieving superior generalization over prompt-specific methods.

Progressive Exploration-Conformal Learning (PECL) revolutionizes sparsely annotated object detection in aerial images by adaptively selecting high-quality pseudo-labels, overcoming limitations of exis…

ProEdit: High-quality 3D scene editing via progressive subtask decomposition.

Principled Probabilistic Imaging uses diffusion models as plug-and-play priors for accurate posterior sampling in inverse problems, surpassing existing methods.

PrefPaint: Aligning image inpainting diffusion models with human preferences using reinforcement learning, resulting in significantly improved visual appeal.

Parametric Piecewise Linear Networks (PPLNs) achieve state-of-the-art results in event-based and frame-based computer vision tasks by mimicking biological neural principles.

This paper presents a novel method for analytically extracting meshes from neural implicit surface networks using trilinear interpolation, offering theoretical insights and practical efficiency.

PointMamba: A linear-complexity state space model achieving superior performance in point cloud analysis, reducing computational cost significantly.

PointAD: a novel zero-shot 3D anomaly detection method using CLIP’s strong generalization abilities to identify anomalies in unseen objects by transferring knowledge from both points and pixels.

Point-PRC improves generalizable 3D point cloud analysis by regulating prompt learning to harmonize task-specific and general knowledge within large 3D models.

PLIP: Novel language-image pre-training framework excels at person representation learning, surpassing existing methods on various downstream tasks thanks to its three pretext tasks and large-scale SY…

Physics-constrained learning significantly boosts optical neural network accuracy by addressing systematic physical errors, achieving state-of-the-art results on image classification tasks.

PHYRECON: A novel neural scene reconstruction method uses differentiable rendering and physics simulation for physically plausible 3D models.

Phased Consistency Models (PCMs) revolutionize diffusion model generation by overcoming LCM limitations, achieving superior speed and quality in image and video generation.