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Register a new userImproving Video Instance Segmentation via Temporal Pyramid Routing
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Xiangtai Li
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Video Instance Segmentation (VIS) is a new and inherently multi-task problem, which aims to detect, segment and track each instance in a video sequence. Existing approaches are mainly based on single-frame features or single-scale features of multiple frames, where temporal information or multi-scale information is ignored. To incorporate both temporal and scale information, we propose a Temporal Pyramid Routing (TPR) strategy to conditionally align and conduct pixel-level aggregation from a feature pyramid pair of two adjacent frames. Specifically, TPR contains two novel components, including Dynamic Aligned Cell Routing (DACR) and Cross Pyramid Routing (CPR), where DACR is designed for aligning and gating pyramid features across temporal dimension, while CPR transfers temporally aggregated features across scale dimension. Moreover, our approach is a plug-and-play module and can be easily applied to existing instance segmentation methods. Extensive experiments on YouTube-VIS dataset demonstrate the effectiveness and efficiency of the proposed approach on several state-of-the-art instance segmentation methods. Codes and trained models will be publicly available to facilitate future research.(url{https://github.com/lxtGH/TemporalPyramidRouting}).
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Low level features like edges and textures play an important role in accurately localizing instances in neural networks. In this paper, we propose an architecture which improves feature pyramid networks commonly used instance segmentation networks by incorporating low level features in all layers of the pyramid in an optimal and efficient way. Specifically, we introduce a new layer which learns new correlations from feature maps of multiple feature pyramid levels holistically and enhances the semantic information of the feature pyramid to improve accuracy. Our architecture is simple to implement in instance segmentation or object detection frameworks to boost accuracy. Using this method in Mask RCNN, our model achieves consistent improvement in precision on COCO Dataset with the computational overhead compared to the original feature pyramid network.
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Instance segmentation is a promising yet challenging topic in computer vision. Recent approaches such as Mask R-CNN typically divide this problem into two parts -- a detection component and a mask generation branch, and mostly focus on the improvement of the detection part. In this paper, we present an approach that extends Mask R-CNN with five novel optimization techniques for improving the mask generation branch and reducing the conflicts between the mask branch and the detection component in training. These five techniques are independent to each other and can be flexibly utilized in building various instance segmentation architectures for increasing the overall accuracy. We demonstrate the effectiveness of our approach with tests on the COCO dataset.
Multi-instance video object segmentation is to segment specific instances throughout a video sequence in pixel level, given only an annotated first frame. In this paper, we implement an effective fully convolutional networks with U-Net similar structure built on top of OSVOS fine-tuned layer. We use instance isolation to transform this multi-instance segmentation problem into binary labeling problem, and use weighted cross entropy loss and dice coefficient loss as our loss function. Our best model achieves F mean of 0.467 and J mean of 0.424 on DAVIS dataset, which is a comparable performance with the State-of-the-Art approach. But case analysis shows this model can achieve a smoother contour and better instance coverage, meaning it better for recall focused segmentation scenario. We also did experiments on other convolutional neural networks, including Seg-Net, Mask R-CNN, and provide insightful comparison and discussion.
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