Computer Vision

DGNet enhances weakly supervised point cloud segmentation by aligning feature embeddings to a mixture of von Mises-Fisher distributions, achieving state-of-the-art performance.

DistillNeRF: a self-supervised learning framework enabling accurate 3D scene reconstruction from sparse, single-frame images by cleverly distilling features from offline NeRFs and 2D foundation models…

This paper introduces a novel implicit Zero-Watermarking scheme using disentangled style domains to detect unauthorized dataset usage in text-to-image models, offering robust copyright protection via …

DisC-GS enhances Gaussian Splatting for real-time novel view synthesis by accurately rendering image discontinuities and boundaries, improving visual quality.

Director3D generates realistic 3D scenes and camera trajectories from text descriptions using a three-stage pipeline: Cinematographer, Decorator, and Detailer.

Direct3D: Revolutionizing image-to-3D generation with a scalable, native 3D diffusion model achieving state-of-the-art quality.

Direct Unlearning Optimization (DUO) robustly removes unsafe content from text-to-image models by using paired image data and output-preserving regularization, effectively defending against adversaria…

Boosting personalized image generation! Direct Consistency Optimization (DCO) fine-tunes text-to-image models, ensuring subject consistency and prompt fidelity, even when merging separately customized…

DiPEx: a novel self-supervised prompt expansion method dramatically boosts class-agnostic object detection by progressively learning non-overlapping hyperspherical prompts, surpassing existing methods…

DiP-GO: A novel pruning method accelerates diffusion models via few-step gradient optimization, achieving a 4.4x speedup on Stable Diffusion 1.5 without accuracy loss.

DINTR: A novel diffusion-based object tracker surpasses existing methods by using efficient interpolation, achieving superior performance across diverse benchmarks.

DiMSUM: A novel diffusion model boosts image generation by unifying spatial and frequency information, achieving superior results and faster training.

DiffusionFake enhances deepfake detection by cleverly reversing the image generation process, enabling detectors to learn more robust features and significantly improve cross-domain generalization.

DiffusionBlend++ learns a 3D image prior via position-aware diffusion score blending, achieving state-of-the-art 3D CT reconstruction with superior efficiency.

Diffusion4D: Fast, consistent 4D content generation via a novel 4D-aware video diffusion model, surpassing existing methods in efficiency and 4D geometry consistency.

Unsupervised OOD detection gets a boost with a diffusion-based approach that leverages multi-layer semantic feature reconstruction for improved accuracy and speed.

DiffuLT uses a novel diffusion model to generate balanced training data from imbalanced datasets, achieving state-of-the-art results in long-tailed image recognition without external knowledge.

DiffuBox refines 3D object detection using a novel diffusion-based approach, significantly improving accuracy across various domains by refining bounding boxes based on surrounding LiDAR point clouds.

DiffCut, a novel unsupervised zero-shot semantic segmentation method, leverages diffusion UNet features and recursive normalized cuts to achieve state-of-the-art performance.

DI-MaskDINO: Novel model significantly boosts object detection & instance segmentation accuracy by addressing performance imbalance using a De-Imbalance module and Balance-Aware Tokens Optimization.