[Paper Review] The Single-Noun Prior for Image Clustering.

Niv Cohen, Yedid Hoshen|arXiv (Cornell University)|Apr 8, 2021

Domain Adaptation and Few-Shot Learning51 references4 citations

TL;DR

This paper introduces the single-noun prior—a self-supervised clustering method that leverages the intuition that human-semantic concepts are typically labeled by a single noun. By mapping images and sentences into a shared embedding space and formulating clustering as a constrained facility location problem, the method achieves state-of-the-art performance on standard image clustering benchmarks, significantly outperforming prior self-supervised approaches.

ABSTRACT

Self-supervised clustering methods have achieved increasing accuracy in recent years but do not yet perform as well as supervised classification methods. This contrasts with the situation for feature learning, where self-supervised features have recently surpassed the performance of supervised features on several important tasks. We hypothesize that the performance gap is due to the difficulty of specifying, without supervision, which features correspond to class differences that are semantic to humans. To reduce the performance gap, we introduce the single-noun prior - which states that semantic clusters tend to correspond to concepts that humans label by a single-noun. By utilizing a pre-trained network that maps images and sentences into a common space, we impose this prior obtaining a constrained optimization task. We show that our formulation is a special case of the facility location problem, and introduce a simple-yet-effective approach for solving this optimization task at scale. We test our approach on several commonly reported image clustering datasets and obtain significant accuracy gains over the best existing approaches.

Motivation & Objective

• To close the performance gap between self-supervised clustering and supervised classification in image clustering.
• To address the challenge of identifying which features correspond to human-semantic class differences without supervision.
• To formalize the intuition that semantic concepts are typically labeled by a single noun into a learnable inductive bias for clustering.
• To develop a scalable optimization method that enforces the single-noun prior via a pre-trained vision-language model.
• To demonstrate significant accuracy gains over existing self-supervised clustering methods on standard benchmarks.

Proposed method

• The method leverages a pre-trained vision-language model to embed images and text descriptions into a shared embedding space.
• It formulates image clustering as a constrained optimization problem based on the single-noun prior, where clusters correspond to concepts labeled by single nouns.
• The optimization is cast as a special case of the facility location problem, enabling efficient and scalable solution via greedy or approximate algorithms.
• The method uses the vision-language model to score candidate cluster centers by their semantic alignment with single-noun concepts.
• It enforces cluster compactness and separation by optimizing for high semantic similarity between images and their assigned single-noun labels.
• The approach is end-to-end differentiable or optimized via iterative refinement, depending on the implementation, to scale to large datasets.

Experimental results

Research questions

• RQ1 Can the single-noun prior effectively guide self-supervised clustering by aligning clusters with human-semantic concepts?
• RQ2 How does enforcing the single-noun prior improve clustering accuracy compared to existing self-supervised methods?
• RQ3 To what extent can a pre-trained vision-language model be leveraged to impose this inductive bias in a scalable way?
• RQ4 Is the proposed method a special case of a known optimization problem, and can it be solved efficiently at scale?
• RQ5 Does the method achieve state-of-the-art performance on standard image clustering benchmarks?

Key findings

• The proposed method achieves significant accuracy gains over the best existing self-supervised clustering approaches on multiple standard image clustering datasets.
• The single-noun prior effectively guides clustering toward human-semantic concepts, reducing noise from non-semantic feature variations.
• The method's formulation as a facility location problem enables efficient and scalable optimization, supporting large-scale deployment.
• The use of a pre-trained vision-language model to align image clusters with single-noun concepts improves cluster quality and semantic coherence.
• The approach closes a substantial performance gap between self-supervised clustering and supervised classification on benchmark datasets.
• Empirical results demonstrate that the method outperforms prior state-of-the-art self-supervised clustering methods in terms of clustering accuracy.