Bilateral Space Video Segmentation

Marki, Nicolas; Perazzi, Federico; Wang, Oliver; Sorkine-Hornung, Alexander

doi:10.1109/cvpr.2016.87

Cited by 206 publications

(206 citation statements)

References 33 publications

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“…For single object VOS, we compare our RANet with 6 state-of-the-art OL based and 11 offline methods [1, 3, 8-10, 19, 22, 23, 35, 37, 38, 40, 45, 49-51, 59] in Table 1, including OSVOS-S [37], PReMVOS [35], RGMP [38], FEELVOS [49], etc. To evaluate our RANet trained with static images, we compare it with some methods [22,23,36,40,47] without using DAVIS training set. For multi-object VOS, we compare with some state-of-theart offline methods [3,9,19,50,59], and also list results of some OL based methods [1,3,19,37,50] for reference.…”

Section: Comparison To the State Of The Artmentioning

confidence: 99%

RANet: Ranking Attention Network for Fast Video Object Segmentation

Wang¹,

Xu²,

Liu³

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

225

101

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Despite online learning (OL) techniques have boosted the performance of semi-supervised video object segmentation (VOS) methods, the huge time costs of OL greatly restrict their practicality. Matching based and propagation based methods run at a faster speed by avoiding OL techniques. However, they are limited by sub-optimal accuracy, due to mismatching and drifting problems. In this paper, we develop a real-time yet very accurate Ranking Attention Network (RANet) for VOS. Specifically, to integrate the insights of matching based and propagation based methods, we employ an encoder-decoder framework to learn pixellevel similarity and segmentation in an end-to-end manner. To better utilize the similarity maps, we propose a novel ranking attention module, which automatically ranks and selects these maps for fine-grained VOS performance. Experiments on DAVIS 16 and DAVIS 17 datasets show that our RANet achieves the best speed-accuracy trade-off, e.g., with 33 milliseconds per frame and J &F=85.5% on DAVIS 16 . With OL, our RANet reaches J &F=87.1% on DAVIS 16

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Section: Comparison To the State Of The Artmentioning

confidence: 99%

RANet: Ranking Attention Network for Fast Video Object Segmentation

Wang¹,

Xu²,

Liu³

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

225

101

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“…We compare the proposed STCNN method to 11 stateof-the-art semi-supervised algorithms, namely BVS [34], JFS [35], SCF [20], MRFCNN [2], LT [25], OSVOS [3], MSK [38], OFL [46], CRN [15], DRL [12], and OnAVOS [47] in Table 2. As shown in Table 2, we observe that the STCNN method produces the best results with 0.796 mean IoU, which surpasses the state-of-the-art results, i.e., MR-FCNN [2] (0.784 mean IoU), with 0.012 mIoU.…”

Section: Youtube-objects Datasetmentioning

confidence: 99%

“…The symbol ↑ means higher scores indicate better performance. Bold font indicates the best result.Method BVS[34] JFS[35] SCF[20] MRFCNN[2] LT[25] OSVOS[3] MSK[38] OFL[46] CRN[15] DRL[12] OnAVOS[47] Ours…”

mentioning

confidence: 99%

Spatiotemporal CNN for Video Object Segmentation

Xu¹,

Wen²,

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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In this paper, we present a unified, end-to-end trainable spatiotemporal CNN model for VOS, which consists of two branches, i.e., the temporal coherence branch and the spatial segmentation branch. Specifically, the temporal coherence branch pretrained in an adversarial fashion from unlabeled video data, is designed to capture the dynamic appearance and motion cues of video sequences to guide object segmentation. The spatial segmentation branch focuses on segmenting objects accurately based on the learned appearance and motion cues. To obtain accurate segmentation results, we design a coarse-to-fine process to sequentially apply a designed attention module on multi-scale feature maps, and concatenate them to produce the final prediction. In this way, the spatial segmentation branch is enforced to gradually concentrate on object regions. These two branches are jointly fine-tuned on video segmentation sequences in an end-to-end manner. Several experiments are carried out on three challenging datasets (i.e., DAVIS-2016, DAVIS-2017 and Youtube-Object) to show that our method achieves favorable performance against the stateof-the-arts. Code is available at https://github. com/longyin880815/STCNN .

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“…Moving away from the single-object hypothesis of DAVIS 2016, these datasets are increasingly focused on the segmentation of multiple objects, which increases the need for a userprovided annotation to specify each object of interest and has led to the development of more semi-supervised VOS methods using an annotated frame. With some exceptions [1,13,27,32], the majority of semi-supervised VOS methods use an artificial neural network.…”

Section: Video Object Segmentationmentioning

confidence: 99%

BubbleNets: Learning to Select the Guidance Frame in Video Object Segmentation by Deep Sorting Frames

Griffin

Corso

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Semi-supervised video object segmentation has made significant progress on real and challenging videos in recent years. The current paradigm for segmentation methods and benchmark datasets is to segment objects in video provided a single annotation in the first frame. However, we find that segmentation performance across the entire video varies dramatically when selecting an alternative frame for annotation. This paper address the problem of learning to suggest the single best frame across the video for user annotation-this is, in fact, never the first frame of video. We achieve this by introducing BubbleNets, a novel deep sorting network that learns to select frames using a performance-based loss function that enables the conversion of expansive amounts of training examples from already existing datasets. Using BubbleNets, we are able to achieve an 11% relative improvement in segmentation performance on the DAVIS benchmark without any changes to the underlying method of segmentation.

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Bilateral Space Video Segmentation

Cited by 206 publications

References 33 publications

RANet: Ranking Attention Network for Fast Video Object Segmentation

RANet: Ranking Attention Network for Fast Video Object Segmentation

Spatiotemporal CNN for Video Object Segmentation

BubbleNets: Learning to Select the Guidance Frame in Video Object Segmentation by Deep Sorting Frames

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