Semantic Object Parsing with Graph LSTM (1603.07063v1)

Abstract: By taking the semantic object parsing task as an exemplar application scenario, we propose the Graph Long Short-Term Memory (Graph LSTM) network, which is the generalization of LSTM from sequential data or multi-dimensional data to general graph-structured data. Particularly, instead of evenly and fixedly dividing an image to pixels or patches in existing multi-dimensional LSTM structures (e.g., Row, Grid and Diagonal LSTMs), we take each arbitrary-shaped superpixel as a semantically consistent node, and adaptively construct an undirected graph for each image, where the spatial relations of the superpixels are naturally used as edges. Constructed on such an adaptive graph topology, the Graph LSTM is more naturally aligned with the visual patterns in the image (e.g., object boundaries or appearance similarities) and provides a more economical information propagation route. Furthermore, for each optimization step over Graph LSTM, we propose to use a confidence-driven scheme to update the hidden and memory states of nodes progressively till all nodes are updated. In addition, for each node, the forgets gates are adaptively learned to capture different degrees of semantic correlation with neighboring nodes. Comprehensive evaluations on four diverse semantic object parsing datasets well demonstrate the significant superiority of our Graph LSTM over other state-of-the-art solutions.

• The paper introduces a Graph LSTM model that leverages superpixel graphs to enhance semantic object parsing.
• The methodology employs confidence-driven node updates and adaptive forget gates within superpixel-based graph structures, yielding a 2-3% boost in IoU and F-1 scores.
• The approach outperforms state-of-the-art solutions across multiple datasets and opens avenues for dynamic graph structures in vision tasks.

Semantic Object Parsing with Graph LSTM

The paper "Semantic Object Parsing with Graph LSTM" presents a significant advancement in the domain of computer vision, specifically focusing on semantic object parsing. The authors propose a novel extension of Long Short-Term Memory (LSTM) networks to graph-structured data, referred to as Graph LSTM, as opposed to the traditional sequential or multi-dimensional data applications of LSTMs.

Methodology Overview

The core idea of this approach is to replace the conventional pixel-based grid structure with a graph-based representation using superpixels. Superpixels serve as semantically consistent nodes which form the basis for constructing an undirected graph. This approach naturally aligns with the visual context of images, including object boundaries and appearance similarities, allowing for more efficient information propagation.

In their Graph LSTM, the spatial relationships between superpixels are leveraged to form graph edges, which allows the model to capture semantic correlations more naturally and economically. Unlike traditional LSTMs, which rely on fixed neighborhood topologies, Graph LSTM adapts to the image content and its topology, providing a more flexible and contextually relevant information flow.

To optimize the graph topology, the authors introduce a confidence-driven scheme to determine the node updating sequence per image, enhancing the progressive update of node states. Additionally, each node in the Graph LSTM learns adaptive forget gates, allowing the network to capture varying semantic correlations with neighboring nodes more effectively.

Experimental Results

The Graph LSTM network was tested comprehensively on four diverse semantic object parsing datasets: PASCAL-Person-Part, Horse-Cow, ATR, and Fashionista. The evaluations demonstrate that the proposed method outperforms existing state-of-the-art solutions in most cases, especially in scenarios involving complex image content with overlapping semantic parts. The results indicated an approximate improvement of 2-3% in the Intersection over Union (IoU) and F-1 score metrics over previous methods. Notably, the approach showed enhanced performance in distinguishing challenging part labels, such as differentiating "upper-arms" from "lower-arms" due to the more robust contextual understanding.

Implications and Future Directions

This work signifies a step forward in semantic parsing tasks by utilizing superpixel-based graph structures that better emulate the inherent semantic patterns in image content. The introduction of adaptive topologies to the LSTM, guided by node confidence, suggests potential enhancements in other vision tasks that benefit from spatial coherence, such as depth prediction and action recognition.

The future development of this research could involve extending Graph LSTM models to handle dynamic graph structures that adapt during the inference phase, potentially integrating directly with outputs for generating semantic masks. Additionally, there are opportunities to refine node updating schemes further or explore alternative neighborhood aggregation methods that could provide even richer contextual understanding.

This paper contributes to the continual evolution of methods aimed at improving the comprehension of complex visual data, adding a robust tool for researchers tackling semantic object parsing and related visual recognition challenges.