SegSort: Segmentation by Discriminative Sorting of Segments

Jyh-Jing Hwang        Stella X. Yu        Jianbo Shi
Maxwell D. Collins         Tien-Ju Yang         Xiao Zhang         Liang-Chieh Chen
UC Berkeley / ICSI        UPenn        Google Research        MIT
International Conference on Computer Vision (ICCV) 2019


Almost all existing deep learning approaches for semantic segmentation tackle this task as a pixel-wise classification problem. Yet humans understand a scene not in terms of pixels, but by decomposing it into perceptual groups and structures that are the basic building blocks of recognition. This motivates us to propose an end-to-end pixel-wise metric learning approach that mimics this process. In our approach, the optimal visual representation determines the right segmentation within individual images and associates segments with the same semantic classes across images. The core visual learning problem is therefore to maximize the similarity within segments and minimize the similarity between segments. Given a model trained this way, inference is performed consistently by extracting pixel-wise embeddings and clustering, with the semantic label determined by the majority vote of its nearest neighbors from an annotated set.

As a result, we present the SegSort, as a first attempt using deep learning for unsupervised semantic segmentation, achieving 76% performance of its supervised counterpart. When supervision is available, SegSort shows consistent improvements over conventional approaches based on pixel-wise softmax training. Additionally, our approach produces more precise boundaries and consistent region predictions. The proposed SegSort further produces an interpretable result, as each choice of label can be easily understood from the retrieved nearest segments.


Code and Models


  title={SegSort: Segmentation by Discriminative Sorting of Segments},
  author={Hwang, Jyh-Jing and Yu, Stella X and Shi, Jianbo and Collins, Maxwell D and Yang, Tien-Ju and Zhang, Xiao and Chen, Liang-Chieh},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},

Supervised SegSort Retrieval Results

Supervised SegSort t-SNE Visualization

Unsupervised SegSort Retrieval Results

Unsupervised SegSort t-SNE Visualization