f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation (2001.10331v3)

Abstract: Deep neural networks have become a mainstream approach to interactive segmentation. As we show in our experiments, while for some images a trained network provides accurate segmentation result with just a few clicks, for some unknown objects it cannot achieve satisfactory result even with a large amount of user input. Recently proposed backpropagating refinement (BRS) scheme introduces an optimization problem for interactive segmentation that results in significantly better performance for the hard cases. At the same time, BRS requires running forward and backward pass through a deep network several times that leads to significantly increased computational budget per click compared to other methods. We propose f-BRS (feature backpropagating refinement scheme) that solves an optimization problem with respect to auxiliary variables instead of the network inputs, and requires running forward and backward pass just for a small part of a network. Experiments on GrabCut, Berkeley, DAVIS and SBD datasets set new state-of-the-art at an order of magnitude lower time per click compared to original BRS. The code and trained models are available at https://github.com/saic-vul/fbrs_interactive_segmentation .

Overview of f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation

The paper "f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation" presents a significant enhancement in interactive segmentation methodologies by introducing a novel approach termed feature backpropagating refinement scheme (f-BRS). This advances the existing backpropagating refinement scheme (BRS) used for refining segmentation results with user input.

Core Contributions and Methodology

f-BRS innovates by addressing the primary limitation of the original BRS—computational inefficiency. BRS, while effectively utilizing both optimization-based and learning-based approaches, incurred high computational costs due to backpropagation through the entire network per user input click. The authors propose f-BRS, which reparameterizes the optimization problem to focus only on auxiliary parameters impacting a small subset of a network, specifically its final layers, thereby reducing the time per click by an order of magnitude.

This strategic reduction in computational demand does not sacrifice performance. The authors demonstrate that f-BRS achieves state-of-the-art results across several benchmarks, including GrabCut, Berkeley, DAVIS, and SBD, while requiring fewer resources compared to the full-network BRS.

Experimental Results and Performance Analysis

The experiments reveal f-BRS setting new standards in both speed and accuracy for interactive segmentation tasks. Notable datasets like GrabCut and Berkeley showed substantial improvements in metrics like the average Number of Clicks (NoC) required to attain target IoU thresholds. For instance, f-BRS achieves superior results with a ResNet backbone by integrating auxiliary parameters efficiently into backpropagation.

A central aspect of the evaluation was the method's convergence analysis. f-BRS demonstrated enhanced predictability with respect to performance across various unseen and challenging object categories, a known limitation in standard feed-forward network-based approaches. By implementing the convergence metric NoC\textsubscript{100}, the authors emphasize the robustness of f-BRS, wherein it notably outperforms prior models in scenarios requiring extensive user input.

Implications and Future Directions

The implementation of f-BRS shows not only practical implications in enhancing annotation workflows (e.g., in autonomous driving or medical imaging) but also theoretical insights into the balance between computational efficiency and performance. The authors' proposal of Zoom-In and adjustment of visible network clicks further broadens the method's applicability by addressing scale and focus during segmentation.

Looking forward, f-BRS could influence developments in various AI applications where interaction and rapid convergence are critical. The methodology may be extended to different neural network architectures or adaptation to more complex segmentation tasks that involve temporally or spatially variant data.

In conclusion, by simplifying the complexity of the optimization problem and focusing computational resources effectively, f-BRS represents a valuable step forward in interactive segmentation research. Its promising results suggest broad applicability and efficiency, setting a foundation for future advancements in the field.