DeepFix: A Fully Convolutional Neural Network for predicting Human Eye Fixations (1510.02927v1)

Abstract: Understanding and predicting the human visual attentional mechanism is an active area of research in the fields of neuroscience and computer vision. In this work, we propose DeepFix, a first-of-its-kind fully convolutional neural network for accurate saliency prediction. Unlike classical works which characterize the saliency map using various hand-crafted features, our model automatically learns features in a hierarchical fashion and predicts saliency map in an end-to-end manner. DeepFix is designed to capture semantics at multiple scales while taking global context into account using network layers with very large receptive fields. Generally, fully convolutional nets are spatially invariant which prevents them from modeling location dependent patterns (e.g. centre-bias). Our network overcomes this limitation by incorporating a novel Location Biased Convolutional layer. We evaluate our model on two challenging eye fixation datasets -- MIT300, CAT2000 and show that it outperforms other recent approaches by a significant margin.

• The paper introduces an end-to-end deep learning model using a 20-layer FCN with Location Biased Convolution to predict visual saliency.
• It leverages large receptive fields and location-specific bias to capture both global context and intrinsic center-bias in human attention.
• Experimental results on MIT300 and CAT2000 datasets show superior performance using metrics like EMD and NSS compared to traditional methods.

DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations

DeepFix presents an innovative approach in the domain of visual saliency prediction by leveraging a fully convolutional neural network (FCN) to predict human eye fixations. This research addresses the limitations of traditional methods that rely heavily on hand-crafted features by proposing an end-to-end learning strategy capable of extracting meaningful patterns directly from image data.

Key Contributions and Methodology

The architecture of DeepFix builds on several foundational advancements in deep learning, notably the use of convolutional layers inspired by the VGG network. It employs large receptive fields to encapsulate the global context, a critical factor in accurately predicting visual saliency. The network's depth, consisting of 20 layers, enables the extraction of complex and hierarchical semantic features necessary for capturing both low-level and high-level stimuli affecting human attention.

A novel aspect of DeepFix is the introduction of Location Biased Convolutional (LBC) layers. Unlike standard FCNs, which are spatially invariant, the LBC layers allow the model to learn location-specific patterns, thereby effectively modeling behaviors like the center-bias observed in human visual attention. This bias is inherent due to both external cues in imagery and intrinsic viewing strategies.

To evaluate its efficacy, DeepFix was tested against prominent datasets—MIT300 and CAT2000—and demonstrated superior performance across several metrics, notably Earth Mover's Distance (EMD) and Normalized Scanpath Saliency (NSS). The network achieved state-of-the-art results in handling scenarios containing diverse visual stimuli.

Results and Implications

Experimental results show that DeepFix outperforms existing models by a significant margin in most key metrics, although gains in AUC metrics were less pronounced due to their inherent limitations in penalizing false positives effectively. The architecture's ability to predict saliency in an end-to-end fashion without relying on pre-defined features points to a shift in strategy for similar problems in computer vision.

Practically, the capability of DeepFix to predict human fixations on images with high accuracy can enhance applications in various areas such as autonomous systems, image summarization, and adaptive displays. Theoretically, this model sets a precedent for future research in saliency by demonstrating the importance of learning location-dependent patterns through architectural innovations like LBC.

Speculation on Future Developments

As AI continues to evolve, models akin to DeepFix will likely undergo refinement to handle increasingly complex tasks involving multi-modal inputs and dynamic scenes. Considering the vast data availability and computational advancements, future research may expand on DeepFix’s framework to encompass real-time processing constraints or integrate more contextual learning elements.

The ability to predict and understand human attention mechanisms using deep learning not only pushes the boundaries of computational models in visual perception but also enhances our understanding of human cognitive processes. DeepFix thus represents a pivotal step toward more sophisticated and nuanced artificial intelligence systems.