segmentation

This paper presents MAOAM, a unified vision-language model framework that enables precise object and material selection through text or click interactions for interactive image editing. It introduces a scalable data generation pipeline and shows emergent improvement when combining text and clicks at inference.

Group Prompting introduces a training-free framework for cell instance segmentation that requires only one click per cell type, using the Segment Anything Model's feature space to recursively expand prompts, achieving competitive performance without training.

InstructSAM presents a unified framework for multi-instance segmentation using instruction-driven queries that bridge vision-language models and SAM3, achieving strong results across complex benchmarks.

Introduces Semantic Generative Tuning (SGT), a paradigm that uses image segmentation as a generative proxy to align visual understanding and generation in unified multimodal models, improving both comprehension and fidelity.

AuralSAM2 integrates audio into SAM2 via an AuralFuser module that generates sparse and dense prompts from audio-visual features, enhancing cross-modal segmentation while maintaining interactive efficiency.

Introduces CAFE, a benchmark for evaluating whether promptable segmentation models truly understand concepts by using counterfactual attribute manipulation, revealing that accurate mask prediction does not guarantee faithful semantic grounding.

TwinTrack is a post-hoc calibration framework for pancreatic cancer segmentation that aligns ensemble model probabilities with the empirical mean human response across multiple annotators, improving interpretability and calibration metrics on multi-rater benchmarks.

Falcon Perception is a 0.6B-parameter early-fusion Transformer model released by TII UAE for open-vocabulary grounding and segmentation from natural language prompts, utilizing hybrid attention and specialized heads.