Semantic Video CNNs through Representation Warping (1708.03088v1)

Abstract: In this work, we propose a technique to convert CNN models for semantic segmentation of static images into CNNs for video data. We describe a warping method that can be used to augment existing architectures with very little extra computational cost. This module is called NetWarp and we demonstrate its use for a range of network architectures. The main design principle is to use optical flow of adjacent frames for warping internal network representations across time. A key insight of this work is that fast optical flow methods can be combined with many different CNN architectures for improved performance and end-to-end training. Experiments validate that the proposed approach incurs only little extra computational cost, while improving performance, when video streams are available. We achieve new state-of-the-art results on the CamVid and Cityscapes benchmark datasets and show consistent improvements over different baseline networks. Our code and models will be available at http://segmentation.is.tue.mpg.de

• The paper introduces NetWarp, which enhances static image CNNs by warping intermediate representations using optical flow for improved temporal coherence.
• It achieves higher Intersection over Union and trimap-IoU scores on benchmarks like CamVid and Cityscapes, outperforming CRF-based approaches.
• The method offers a fast, end-to-end trainable solution for real-time video segmentation, promising practical applications in autonomous driving and surveillance.

Semantic Video CNNs through Representation Warping

The paper "Semantic Video CNNs through Representation Warping" presents a novel approach to enhancing convolutional neural networks (CNNs) for video data segmentation by introducing a method called NetWarp. This technique leverages temporal coherence in video frames and seeks to address the limitations of applying static image CNNs to video data.

The NetWarp module facilitates the transformation of static image CNNs into video CNNs with minimal additional computational cost. It utilizes the optical flow between sequential video frames to warp and align intermediate CNN representations across time, thereby enhancing the consistency and performance of semantic video segmentation. Notably, the researchers demonstrate the generalizability of NetWarp across a variety of CNN architectures and its integration ability with end-to-end training frameworks.

This research is substantiated through experiments showcasing new state-of-the-art performance metrics on established video segmentation benchmarks, including the CamVid and Cityscapes datasets. The approach improved Intersection over Union (IoU) scores compared to baseline methods and exhibited robust performance in capturing fine structures like poles and signs, evidenced by higher trimap-IoU scores. Additionally, the proposed method proved faster than existing conditional random field (CRF) models, which are computationally intensive and lack direct access to intermediate network representations.

The experimentation contrasted conventional optical flow with the transformed flow through a dedicated FlowCNN, leading to the realization that transformed flow significantly improves semantic segmentation stability over sequences. By reducing computational demands and maintaining high predictive accuracy, this method provides practical value in real-time video processing contexts where efficient resource use is paramount.

The implications of this paper are critical for advancing video segmentation methods. Practically, it offers a framework for deploying high-performing CNNs in time-sensitive or resource-constrained scenarios, such as in autonomous driving or surveillance. Theoretically, it stimulates further exploration into optimizing information transfer across temporal sequences in neural networks.

Potential future research directions include the extension of NetWarp to utilize multiple frames concurrently, beyond adjacent pairs, to further exploit temporal dynamics. Additionally, evolving the integration of optical flow estimations with adaptive transformations, potentially using attention mechanisms, could enhance the adaptability of CNNs to diverse video data complexities.

In conclusion, this paper significantly contributes to the field of video analysis through its efficient and effective adaptation of image-based networks to video segmentation tasks, challenging traditional methods while setting a foundation for ongoing research in neural network temporal integration.