Computer Vision

E2ENet: A novel 3D medical image segmentation model boasts high accuracy and efficiency by dynamically fusing multi-scale features and using restricted depth-shift 3D convolutions, significantly outp…

E-Motion: Predicting future motion with unprecedented accuracy using event cameras and video diffusion models.

Dynamic Tuning (DyT) significantly boosts Vision Transformer (ViT) adaptation by dynamically skipping less important tokens during inference, achieving superior performance with 71% fewer FLOPs than e…

Self-supervised dual-frame fluid motion estimation achieves superior accuracy with 99% less training data, using a novel zero-divergence loss and dynamic velocimetry enhancement.

Dual-Diffusion boosts binocular 3D human pose estimation accuracy by simultaneously denoising 2D and 3D pose uncertainties using a diffusion model.

Dual encoder GAN inversion achieves high-fidelity 3D head reconstruction from single images by cleverly combining outputs from encoders specialized for visible and invisible regions, surpassing existi…

DRIP: A novel image matting method using pre-trained latent diffusion models achieves state-of-the-art performance by jointly predicting foreground and alpha values, significantly improving accuracy a…

DreamSteerer enhances source image-conditioned editability in personalized diffusion models via a novel Editability Driven Score Distillation objective and mode shifting regularization, achieving sign…

DreamScene4D generates realistic 3D dynamic multi-object scenes from monocular videos via novel view synthesis, addressing limitations of existing methods with a novel decompose-recompose approach.

DreamMesh4D: Generating high-fidelity dynamic 3D meshes from monocular video using a novel Gaussian-mesh hybrid representation and adaptive hybrid skinning.

DRACO, a denoising-reconstruction autoencoder, revolutionizes cryo-EM by leveraging a large-scale dataset and hybrid training for superior image denoising and downstream task performance.

Doubly hierarchical geometric representations enable realistic human hairstyle generation by separating low and high-frequency details in hair strands, resulting in high-quality, detailed virtual hair…

DomainGallery: Few-shot domain-driven image generation via attribute-centric finetuning, solving key issues of previous works by introducing attribute erasure, disentanglement, regularization, and enh…

KGD: a novel knowledge graph distillation technique empowers large-vocabulary object detectors with superior cross-domain object classification, achieving state-of-the-art performance.

DOGS: Distributed-Oriented Gaussian Splatting accelerates large-scale 3D reconstruction by distributing the training of 3D Gaussian Splatting models across multiple machines, achieving 6x faster train…

Zero-shot online action detection gets a boost! OV-OAD leverages vision-language models and text supervision to achieve impressive performance on various benchmarks without relying on manual annotati…

DN-4DGS: Real-time dynamic scene rendering is revolutionized by a denoised deformable network with temporal-spatial aggregation, achieving state-of-the-art quality.

DMesh: A novel differentiable mesh representation enabling efficient gradient-based optimization for diverse 3D shape applications.

DiTFastAttn: A post-training compression method drastically speeds up diffusion transformer models by cleverly reducing redundancy in attention calculations, leading to up to a 1.8x speedup at high re…

DistDiff, a training-free data expansion framework, leverages distribution-aware diffusion models to generate high-fidelity, diverse samples that enhance downstream model performance.