Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks (1512.04143v1)

Abstract: It is well known that contextual and multi-scale representations are important for accurate visual recognition. In this paper we present the Inside-Outside Net (ION), an object detector that exploits information both inside and outside the region of interest. Contextual information outside the region of interest is integrated using spatial recurrent neural networks. Inside, we use skip pooling to extract information at multiple scales and levels of abstraction. Through extensive experiments we evaluate the design space and provide readers with an overview of what tricks of the trade are important. ION improves state-of-the-art on PASCAL VOC 2012 object detection from 73.9% to 76.4% mAP. On the new and more challenging MS COCO dataset, we improve state-of-art-the from 19.7% to 33.1% mAP. In the 2015 MS COCO Detection Challenge, our ION model won the Best Student Entry and finished 3rd place overall. As intuition suggests, our detection results provide strong evidence that context and multi-scale representations improve small object detection.

• The paper introduces the ION model, which integrates multi-scale skip pooling and spatial RNNs to improve object detection performance.
• It leverages both local and global context to enhance detection of small and occluded objects, evidenced by notable mAP gains.
• The architecture achieves state-of-the-art results on PASCAL VOC and MS COCO benchmarks, outperforming previous methods.

Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

The paper "Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks" by Sean Bell et al. proposes an advanced object detection framework called Inside-Outside Net (ION). This approach effectively integrates contextual information using spatial recurrent neural networks (RNNs) and multi-scale representations via skip pooling. It achieves state-of-the-art performance on major benchmark datasets such as PASCAL VOC 2007, PASCAL VOC 2012, and MS COCO.

Key Contributions

The paper presents several noteworthy contributions:

1. ION Architecture: The ION model leverages contextual information and multi-scale skip pooling to provide enhanced object detection capabilities.
2. Contextual Integration: The use of spatial RNNs to capture contextual information beyond the region of interest (ROI).
3. Multi-scale Skip Pooling: Efficiently drawing information from multiple layers of the ConvNet to capture varying levels of feature abstraction.
4. State-of-the-art Performance: Achieving significant improvements over existing methods, reporting 79.2% mAP on PASCAL VOC 2007, 76.4% mAP on PASCAL VOC 2012, and a notable 33.1% mAP on the MS COCO dataset.

Technical Overview

Multi-scale Representation:

ION employs multi-scale representations to capture fine-grained details by pooling from multiple convolutional layers (e.g., conv3, conv4, conv5). These features are combined and normalized to maintain upstream compatibility with standard object detection layers like the fully connected layers (fc6).

Contextual Information:

To effectively incorporate contextual information, the model constructs spatial RNNs that traverse the image. This is implemented as 4-directional IRNNs, producing context features that preserve both local and global image dependencies. Such context features improve the detection of small and occluded objects significantly.

Design Space Evaluation:

The paper outlines an extensive evaluation of design choices, such as:

• Pooling from multiple layers to capture diverse feature information.
• Use of semantic segmentation loss to enhance training.
• Comparison of various context integration methods, solidifying the choice of IRNNs over additional convolutions or global pooling.

Numerical Results

The numerical results presented are impressive:

• PASCAL VOC 2007: Achieved mAP of 79.2%, showing a clear improvement over the MR-CNN baseline which reported 78.2%.
• PASCAL VOC 2012: Achieved mAP of 76.4%, outperforming the next best by several percentage points.
• MS COCO: The ION model shows substantial improvement from a baseline mAP of 20.5% to 33.1%, showcasing its effectiveness, particularly in challenging conditions with small object settings.

Theoretical and Practical Implications

Theoretical Implications:

The combination of spatial RNNs for context and multi-scale skip pooling addresses the limitations of previous detectors that primarily focused on local features. By integrating context and fine-grained details effectively, the ION model provides a stronger theoretical framework for understanding object detection in varying contexts and scales.

Practical Implications:

Practically, the model's design leads to efficient implementation and deployment. The ION architecture shows a compelling trade-off, balancing computational complexity with significant performance gains. The improvements are particularly pronounced for small object detection, advancing applications in densely packed scenes or where object scales vary widely.

Future Developments

Future research can build on the ION framework by:

• Exploring more sophisticated recurrent structures like LSTMs or GRUs within the spatial context integration.
• Enhancing multi-task learning frameworks to further benefit from auxiliary tasks like dense prediction tasks (e.g., segmentation).
• Investigating the application of ION in real-time systems and ensuring scalability for practical deployments in various domains such as autonomous driving, surveillance, and robotic vision.

Conclusion

The "Inside-Outside Net" paper elucidates an enhanced object detection architecture that leverages multi-scale and context-aware features effectively. Through rigorous design iterations and empirical validations, the model achieves superior performance across standard benchmarks, paving the way for more robust and contextually aware object detection methodologies in computer vision.