Semantic Object Parsing with Local-Global Long Short-Term Memory (1511.04510v1)

Abstract: Semantic object parsing is a fundamental task for understanding objects in detail in computer vision community, where incorporating multi-level contextual information is critical for achieving such fine-grained pixel-level recognition. Prior methods often leverage the contextual information through post-processing predicted confidence maps. In this work, we propose a novel deep Local-Global Long Short-Term Memory (LG-LSTM) architecture to seamlessly incorporate short-distance and long-distance spatial dependencies into the feature learning over all pixel positions. In each LG-LSTM layer, local guidance from neighboring positions and global guidance from the whole image are imposed on each position to better exploit complex local and global contextual information. Individual LSTMs for distinct spatial dimensions are also utilized to intrinsically capture various spatial layouts of semantic parts in the images, yielding distinct hidden and memory cells of each position for each dimension. In our parsing approach, several LG-LSTM layers are stacked and appended to the intermediate convolutional layers to directly enhance visual features, allowing network parameters to be learned in an end-to-end way. The long chains of sequential computation by stacked LG-LSTM layers also enable each pixel to sense a much larger region for inference benefiting from the memorization of previous dependencies in all positions along all dimensions. Comprehensive evaluations on three public datasets well demonstrate the significant superiority of our LG-LSTM over other state-of-the-art methods.

• The paper introduces a novel LG-LSTM architecture that integrates both local and global spatial dependencies for accurate semantic object parsing.
• By appending LG-LSTM layers to intermediate CNN layers, the model enhances pixel-level feature representations and contextual awareness.
• Experimental results on multiple public datasets demonstrate significant improvements in IoU and pixel-wise accuracy compared to state-of-the-art methods.

Semantic Object Parsing with Local-Global Long Short-Term Memory

The paper "Semantic Object Parsing with Local-Global Long Short-Term Memory" introduces a novel approach to semantic object parsing, a fundamental task within the computer vision domain. The task involves segmenting an object's image region into semantic parts to enhance detailed image content understanding. This paper proposes a deep learning architecture, Local-Global Long Short-Term Memory (LG-LSTM), which integrates long-distance and short-distance spatial dependencies into pixel-level feature learning, a critical aspect of achieving fine-grained recognition.

Semantic object parsing traditionally relies on CNN-based models because of their robustness in image classification and object segmentation tasks. However, these models typically operate on limited contextual information because of their small convolutional filters. This limitation is addressed by LG-LSTM, which incorporates a more significant contextual standpoint, both at local and global levels.

The architectural design of LG-LSTM layers within a neural network allows seamless inclusion of spatial dependencies across all pixel positions. Noteworthy is the innovative approach of the LG-LSTM, which uses distinct LSTMs for different spatial dimensions alongside a mechanism for capturing short-distance and long-distance spatial dependencies. By structuring these components, each position benefits from both local neighborhood and global image context to distinguish semantic parts accurately.

Structurally, the LG-LSTM layers are built on top of Grid LSTM principles and are appended to intermediate CNN layers, allowing for end-to-end learning and enhancement of visual feature representation. Consequently, each pixel's feature representation is enhanced incrementally, enabling it to gain contextual awareness from a broader area within the image. This methodological improvement addresses the information attenuation challenges inherent in deep CNNs.

Experimentally, LG-LSTM's efficacy is validated through comprehensive evaluations on three public datasets: Horse-Cow parsing dataset, ATR dataset, and Fashionista dataset. In these evaluations, LG-LSTM consistently shows superiority over state-of-the-art methods, showcasing its robustness in capturing semantic object parts with significant improvements in standard metrics, such as intersection over union (IoU) and pixel-wise accuracy.

The paper also engages in comparative analysis with current methods, underscoring the success of LG-LSTM in enhancing object parsing performance. Importantly, this research underscores several critical contributions, such as the intrinsic incorporation of local spatial dependencies and global context for robust feature enhancements, all within a structure that supports sequential inference throughout the network's depth.

The implications of this work are multifaceted. Practically, the LG-LSTM algorithm presents a scalable method for detailed image understanding necessary in applications like intelligent automation and detailed scene interpretation. Theoretically, it expands the current understanding of integrating recurrent networks into convolutional structures, potentially inspiring novel architectures that further blend sequential data processing capabilities with spatial image feature learning.

For future developments in AI, this work lays the groundwork for extending LSTM frameworks to tackle other vision-related tasks, leveraging the synergy between contextual feature learning and recurrent memory capabilities. Additionally, potential research directions could explore further decomposing global and local context influences or developing architectures where the entire network is built upon LSTM foundations to capitalize fully on the sequential capture of spatial dependencies.