Knowledge Guided Disambiguation for Large-Scale Scene Classification with Multi-Resolution CNNs (1610.01119v2)

Abstract: Convolutional Neural Networks (CNNs) have made remarkable progress on scene recognition, partially due to these recent large-scale scene datasets, such as the Places and Places2. Scene categories are often defined by multi-level information, including local objects, global layout, and background environment, thus leading to large intra-class variations. In addition, with the increasing number of scene categories, label ambiguity has become another crucial issue in large-scale classification. This paper focuses on large-scale scene recognition and makes two major contributions to tackle these issues. First, we propose a multi-resolution CNN architecture that captures visual content and structure at multiple levels. The multi-resolution CNNs are composed of coarse resolution CNNs and fine resolution CNNs, which are complementary to each other. Second, we design two knowledge guided disambiguation techniques to deal with the problem of label ambiguity. (i) We exploit the knowledge from the confusion matrix computed on validation data to merge ambiguous classes into a super category. (ii) We utilize the knowledge of extra networks to produce a soft label for each image. Then the super categories or soft labels are employed to guide CNN training on the Places2. We conduct extensive experiments on three large-scale image datasets (ImageNet, Places, and Places2), demonstrating the effectiveness of our approach. Furthermore, our method takes part in two major scene recognition challenges, and achieves the second place at the Places2 challenge in ILSVRC 2015, and the first place at the LSUN challenge in CVPR 2016. Finally, we directly test the learned representations on other scene benchmarks, and obtain the new state-of-the-art results on the MIT Indoor67 (86.7\%) and SUN397 (72.0\%). We release the code and models at~\url{https://github.com/wanglimin/MRCNN-Scene-Recognition}.

• The paper introduces a multi-resolution CNN framework that integrates coarse and fine resolutions to capture diverse scene features and lower intra-class variability.
• It implements knowledge guided disambiguation strategies, merging ambiguous classes through confusion matrix analysis and using external soft labeling to enhance training.
• Experimental results on major datasets like Places and LSUN demonstrate significant accuracy improvements and state-of-the-art performance in scene classification.

Knowledge Guided Disambiguation for Large-Scale Scene Classification with Multi-Resolution CNNs

The paper, "Knowledge Guided Disambiguation for Large-Scale Scene Classification with Multi-Resolution CNNs," authored by Limin Wang, Sheng Guo, Weilin Huang, Yuanjun Xiong, and Yu Qiao, addresses the challenges inherent in large-scale scene recognition, specifically visual inconsistence and label ambiguity, through a novel architectural framework and strategic implementations.

Summary of Contributions

This research provides two primary contributions to the field of computer vision: the development of a multi-resolution Convolutional Neural Network (CNN) architecture and the introduction of knowledge guided disambiguation techniques.

1. Multi-Resolution Network Architecture: The authors propose a hierarchical approach to scene classification that leverages CNNs operating at multiple resolutions. This involves two distinct networks: one trained at a coarse resolution to capture broader spatial layout and object distributions, and another at a fine resolution to capture detailed textures and small objects. These networks are then integrated to provide a comprehensive representation of the scene that effectively captures multi-level information, reducing intra-class variability.
2. Knowledge Guided Disambiguation: To address the issue of label ambiguity, the paper introduces two effective strategies:
   • Class Merging via Confusion Matrix Analysis: By analyzing the confusion matrix, ambiguous classes are merged into super categories, reducing the complexity associated with similar scene classes and optimizing classification accuracy.
   • Utilization of Extra Networks for Soft Labeling: Pre-trained models from external data sources (such as ImageNet and Places) are used to generate soft labels that offer auxiliary supervision during training. This soft-labeling process aids in transferring knowledge across domains and enhances generalization capability.

Experimental Validation

The performance of the proposed methods is evaluated on multiple large-scale datasets, including ImageNet, Places, and Places2. The results highlight that:

• The multi-resolution CNN framework consistently outperformed single-resolution models across datasets, achieving state-of-the-art performance with substantial reductions in classification error rates.
• The knowledge guided disambiguation strategies further improved the CNN's robustness against label ambiguity, with soft labeling providing a significant boost in classification accuracy over hard labeling approaches.

Notably, the application of this research led to remarkable success at the Places2 Challenge, securing second place, and achieving the best results at the LSUN Challenge, thereby demonstrating its efficacy in competitive real-world settings.

Implications and Future Directions

This work reinforces the significance of addressing scale and ambiguity in scene classification. The proposed multi-resolution approach and knowledge guided methodologies provide a scalable solution to tackle the complexities of large-scale datasets, bridging the gap between object and scene recognition tasks.

Future research can build upon this framework by exploring additional sources of auxiliary knowledge, further refining soft labeling techniques, and applying these strategies to more generalized domains beyond scene recognition. Additionally, leveraging advancements in model interpretability, researchers can aim to understand the decision-making processes within these multi-resolution architectures, enhancing their reliability and applicability to diverse applications.

In conclusion, this paper contributes valuable insights into scalable scene classification, offering methodologies that improve model accuracy and robustness through innovative use of hierarchical networks and informed disambiguation techniques.