Image Segmentation

DEPICT: A new white-box decoder for Transformer-based semantic segmentation, achieving better performance with fewer parameters by leveraging the principle of compression and connecting Transformer de…

Relationship Prompt Network (RPN) achieves state-of-the-art open-vocabulary semantic segmentation using only prompt learning and a Vision-Language Model (VLM), eliminating the need for expensive segme…

SynCx, a novel recurrent autoencoder with complex weights, surpasses state-of-the-art models in unsupervised object discovery by iteratively refining phase relationships to achieve robust object bindi…

PSTUDA, a novel progressive style transfer framework, efficiently segments kidney tumors across multiple MRI sequences using unsupervised domain adaptation, achieving higher accuracy and efficiency th…

Neuroscience-inspired motion energy processing enables human-like zero-shot generalization in figure-ground segmentation, outperforming deep learning models on random dot stimuli.

MetaUAS achieves universal visual anomaly segmentation using only one normal image prompt via a pure vision model, surpassing previous zero-shot, few-shot, and full-shot methods.

MaskFactory generates high-quality synthetic data for dichotomous image segmentation, improving model training efficiency and accuracy.

Lightweight Frequency Masker significantly improves cross-domain few-shot semantic segmentation by cleverly filtering frequency components of images, thereby reducing inter-channel correlation and enh…

ProMaC leverages MLLM hallucinations in an iterative framework to generate precise prompts for accurate object segmentation, minimizing manual prompt dependency.

This paper introduces a novel unsupervised video object segmentation method using long-term point trajectories and optical flow, outperforming prior art by effectively combining sparse, long-term moti…

FADA: a novel frequency-adapted learning scheme boosts domain-generalized semantic segmentation by decoupling style and content using Haar wavelets, achieving state-of-the-art results.

Deep metric learning and Coreset integration enables efficient slice-based active learning for 3D medical segmentation, surpassing existing methods in performance with low annotation budgets.

Active Viewpoint Selection (AVS) significantly improves viewpoint-independent object-centric representations by actively selecting the most informative viewpoints for each scene, leading to better seg…

Hybrid Mamba Network (HMNet) boosts few-shot segmentation accuracy by efficiently fusing support and query features using a novel hybrid Mamba architecture, significantly outperforming current state-o…

HisTPT: Historical Test-Time Prompt Tuning memorizes past learning, enabling robust online prompt adaptation for vision models, overcoming performance degradation in continuously changing data streams…

GraphMorph: revolutionizing tubular structure extraction by morphing predicted graphs for superior topological accuracy.

Boosting indoor panoramic semantic segmentation, a new approach leverages geometric properties to optimize over- and under-sampled image segments for improved accuracy and robustness.

This research presents a novel method for robust semantic segmentation, achieving state-of-the-art results by generating coherent images with both semantic and covariate shifts and recalibrating uncer…

GaussianCut enables intuitive 3D object selection via graph cuts on 3D Gaussian splatting, achieving competitive segmentation without extra training.

A-SAM: Turning SAM’s inherent ambiguity into an advantage for controllable, diverse, and convincing ambiguous object segmentation.