Enhancing Object Discovery for Unsupervised Instance Segmentation and Object Detection

TL;DR

COLER introduces a novel, simple, and effective unsupervised approach for object discovery that leverages a single normalized cut step and self-supervised learning, outperforming previous methods on multiple benchmarks.

Contribution

The paper presents COLER, a zero-shot unsupervised model that uses a new CutOnce method with a single NCut application, eliminating the need for clustering or mask post-processing.

Findings

Outperforms previous state-of-the-art on multiple benchmarks.

Achieves strong performance without specialized loss functions.

Leverages self-supervised models for improved object discovery.

Abstract

We propose Cut-Once-and-LEaRn (COLER), a simple approach for unsupervised instance segmentation and object detection. COLER first uses our developed CutOnce to generate coarse pseudo labels, then enables the detector to learn from these masks. CutOnce applies Normalized Cut (NCut) only once and does not rely on any clustering methods (e.g., K-Means), but it can generate multiple object masks in an image. Our work opens a new direction for NCut algorithm in multi-object segmentation. We have designed several novel yet simple modules that not only allow CutOnce to fully leverage the object discovery capabilities of self-supervised model, but also free it from reliance on mask post-processing. During training, COLER achieves strong performance without requiring specially designed loss functions for pseudo labels, and its performance is further improved through self-training. COLER is a zero-shot unsupervised model that outperforms previous state-of-the-art methods on multiple benchmarks. We believe our method can help advance the field of unsupervised object localization. Code is available at: https://github.com/Quantumcraft616/COLER.