Image Segmentation

BlackFed: Privacy-preserving federated semantic segmentation using zero/first-order optimization, avoiding gradient/weight sharing!

DUSA:Unlocking Diffusion Models’ Discriminative Power for Efficient Test-Time Adaptation

EAGLE: A novel unsupervised cross-view adaptation method for semantic segmentation achieves state-of-the-art performance by efficiently modeling geometric structural changes across different camera vi…

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…

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…

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

DarkSAM, a novel prompt-free attack, renders the Segment Anything Model incapable of segmenting objects across diverse images, highlighting its vulnerability to universal adversarial perturbations.

CoSW: a novel conditional sample weighting method for robust smoke segmentation, achieves state-of-the-art results by handling inconsistent noisy labels through a multi-prototype framework.

Spiking neural network effectively segments mixed-motion event streams via spike-based Bayesian computation, achieving efficient real-time motion decoupling.

SeCo: Semantic Connectivity-driven Pseudo-Labeling enhances cross-domain semantic segmentation by correcting noisy pseudo-labels at the connectivity level, improving model accuracy and robustness.

CAT: A novel dual-prompt model coordinates anatomical and textual prompts for superior multi-organ & tumor segmentation in medical imaging, overcoming limitations of single-prompt methods.

GF-SAM: A novel graph-based few-shot semantic segmentation method leverages SAM’s power efficiently via positive-negative prompt alignment and mask clustering for superior accuracy and speed.

GPTrack: A novel unsupervised framework enhances cardiac motion tracking by using sequential Gaussian processes and bidirectional recurrence, improving accuracy and efficiency.

GNNs automate multi-dataset semantic segmentation label unification, improving model training efficiency and performance by resolving conflicts across label spaces.

AUCSeg tackles pixel-level long-tail semantic segmentation by introducing an AUC-oriented loss function and a Tail-Classes Memory Bank to efficiently manage memory and improve performance on imbalance…

AdaPKC upgrades PeakConv for superior radar semantic segmentation by dynamically adjusting its receptive field, outperforming current state-of-the-art methods.

Simple rule-based base-class mining (BCM) significantly boosts generalized few-shot semantic segmentation (GFSS) performance, surpassing complex existing methods.