Image Segmentation

CAPI: a novel masked image modeling framework boosts self-supervised visual representation learning by predicting latent clusterings, achieving state-of-the-art ImageNet accuracy and mIoU.

3CAD: A new large-scale, real-world dataset with diverse 3C product anomalies boosts unsupervised anomaly detection, enabling superior algorithm development via a novel Coarse-to-Fine framework.

ViLU-Net, a novel U-Net modification using Vision-xLSTM, achieves superior retroperitoneal tumor segmentation accuracy and efficiency, exceeding existing state-of-the-art methods.

MRGen, a novel diffusion-based data engine, controllably synthesizes MRI data for unannotated modalities, boosting segmentation model performance.

EfficientTAMs achieve comparable video object segmentation accuracy to SAM 2 with ~2x speedup using lightweight ViTs and efficient cross-attention.

MedNeXt, a novel model ensemble, optimizes brain tumor segmentation in diverse populations, achieving state-of-the-art results on the BraTS 2024 SSA and pediatric datasets.

SegBook: a large-scale benchmark, reveals that fine-tuning full-body CT pre-trained models significantly improves performance on various downstream medical image segmentation tasks, particularly for s…

ITACLIP boosts training-free semantic segmentation by architecturally enhancing CLIP, integrating LLM-generated class descriptions, and employing image engineering; achieving state-of-the-art results.