Recurrent Pixel Embedding for Instance Grouping (1712.08273v1)

Abstract: We introduce a differentiable, end-to-end trainable framework for solving pixel-level grouping problems such as instance segmentation consisting of two novel components. First, we regress pixels into a hyper-spherical embedding space so that pixels from the same group have high cosine similarity while those from different groups have similarity below a specified margin. We analyze the choice of embedding dimension and margin, relating them to theoretical results on the problem of distributing points uniformly on the sphere. Second, to group instances, we utilize a variant of mean-shift clustering, implemented as a recurrent neural network parameterized by kernel bandwidth. This recurrent grouping module is differentiable, enjoys convergent dynamics and probabilistic interpretability. Backpropagating the group-weighted loss through this module allows learning to focus on only correcting embedding errors that won't be resolved during subsequent clustering. Our framework, while conceptually simple and theoretically abundant, is also practically effective and computationally efficient. We demonstrate substantial improvements over state-of-the-art instance segmentation for object proposal generation, as well as demonstrating the benefits of grouping loss for classification tasks such as boundary detection and semantic segmentation.

• The paper introduces a novel framework for instance grouping using hyper-spherical pixel embedding combined with recurrent mean-shift clustering.
• This method significantly improves instance segmentation, achieving higher average recall for object proposal generation compared to previous state-of-the-art.
• The recurrent pixel embedding approach provides a unified, computationally efficient architecture addressing challenges in pixel-level grouping for complex scene understanding.

Recurrent Pixel Embedding for Instance Grouping

The paper "Recurrent Pixel Embedding for Instance Grouping" introduces an innovative method to address pixel-level grouping challenges in computer vision, specifically focusing on instance segmentation tasks. Authored by Shu Kong and Charless Fowlkes, the research presents a robust, computationally efficient, and conceptually straightforward approach that combines spherical embedding with recurrent mean-shift clustering.

Overview

The proposed framework consists of two primary components:

1. Hyper-spherical Embedding: Pixels are regressed into a hyperspherical space, ensuring high cosine similarity for pixels within the same group and maintaining distance within different groups by enforcing similarity below a defined margin. This approach leverages theoretical insights related to uniform distribution of points on a sphere, providing a sound basis for selection of embedding dimension and margins.
2. Recurrent Mean-Shift Clustering: This clustering mechanism is implemented as a differentiable recurrent network, parameterized by kernel bandwidth, offering convergent dynamics and probabilistic interpretability. The structure of mean-shift clustering using von Mises-Fisher distributions naturally complements the embedding process by refining the pixel groupings.

Numerical Results and Claims

The paper presents substantial improvements in instance segmentation over previous state-of-the-art methods. The authors report heightened average recall for object proposal generation, demonstrating a leap from an average recall of 0.56 to 0.77 for generating 10 proposals per image. Additionally, the framework shows increased efficacy in related tasks such as boundary detection and semantic segmentation by integrating grouping loss mechanisms, which guide feature representations beyond traditional classification loss.

Implications and Future Directions

The practical implications of this work are significant in areas involving complex scene parsing, where instance segmentation provides vital object-level understanding. Theoretically, the embedding approach enriches metric learning and clustering theories, especially when applied to high-dimensional spaces with continuous embeddings.

The introduction of a unified architecture for grouping pixels directly addresses unresolved challenges in instance segmentation, such as handling variable outputs and ensuring invariance to instance label permutations. Although effective on its own, the recurrent mean-shift module provides avenues for future research into learnable variants. Furthermore, the concept of embedding for visual tasks can extend to other applications like depth estimation and surface structure understanding in AI.

The convergence properties of the proposed methods alleviate typical learning challenges faced by recurrent networks, such as vanishing/exploding gradients. As future work, exploring further theoretical bounds on margin settings and dimensional scalability for broader data distributions may reveal more nuanced guidelines enhancing the stability and scaling of similar models in computer vision.

In conclusion, the paper contributes a streamlined approach blending key theoretical insights with practical efficiency, positioning recurrent pixel embedding frameworks as an impactful direction in the ongoing development of sophisticated AI systems capable of nuanced, pixel-level analysis.