Pith. sign in

REVIEW 2 major objections 5 minor 62 references

RANet: Ranking Attention Network for Fast Video Object Segmentation

T0 review · 2 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper claims that a ranking attention module that selects the most informative pixel-similarity maps lets a single network segment video objects in real time (33 ms per frame) while matching or beating slower online-learning methods.

desk verdict Solid empirical VOS paper: the RAM module is a real contribution and the accuracy claims hold up; the speed comparison is soft but not fatal. read the letter →

arxiv 1908.06647 v4 pith:7TOGNVDV submitted 2019-08-19 cs.CV

Ziqin Wang , Jun Xu , Li Liu , Fan Zhu , Ling Shao This is my paper · ORCID
classification cs.CV
keywords videoobjectsegmentationrankingattentionSiamesenetworkpixel-levelmatchingmaskpropagationreal-timeDAVISbenchmarkonlinelearning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

RANet is a video object segmentation network designed to bridge two previously separate strategies: pixel-level matching against a first-frame template, and mask propagation from the previous frame. The paper's central claim is that by learning both in one encoder–decoder network and using a ranking attention module to select the most informative similarity maps, a single network can run at 30 FPS while matching the accuracy of much slower online-learning methods. On DAVIS16 this yields J&F 85.5% at 33 ms per frame without online learning, and J&F 87.1% with online learning, which the paper reports as the best speed-accuracy trade-off among compared methods. The claim matters because online-learning methods typically need seconds per frame, which blocks practical video editing and analysis.

What carries the argument

The central mechanism is the Ranking Attention Module (RAM). It takes the correlation similarity maps between the template and current frames, separates them into foreground and background using the first-frame mask, assigns each map a ranking score computed by a two-layer network summed with channel-wise global max-pooling, and then ranks the maps, keeping the top-scoring ones and padding or discarding to reach a fixed 256-channel input. RAM is what lets the decoder accept similarity maps whose natural count varies with object size and video content, and the ablation attributes a 3.6-point J mean gain to the ranking step versus simply keeping all maps in spatial order.

What would settle it

Run RANet and the offline comparison methods it beats (for example RGMP, FEELVOS, and SiamMask) on the same GPU, same input resolution, and same framework, timing them on DAVIS16-val; if RANet is not the fastest among methods at its accuracy level, the central speed-accuracy claim fails. A second check: replace the learned ranking scores with a random permutation of the similarity maps; if J mean stays near 85.5, the ranking step is not load-bearing.

Watch

Extended reading notes

Core claim

The paper establishes that the accuracy gap between fast matching/propagation methods and slow online-learning methods can be closed by treating pixel-level similarity maps as a rich intermediate representation rather than as a final prediction. A Siamese encoder extracts features from the template and current frames; correlation produces one similarity map per template pixel; and the ranking attention module scores, ranks, and selects these maps separately for foreground and background, fixing their number to a constant channel size. A light-weight pyramid decoder then merges the ranked maps with the previous frame's mask. The authors report that this configuration reaches J&F 85.5% on DAVIS16-val in 33 ms per frame, that removing the ranking step costs 3.6 points of J mean, and that adding online learning raises J&F to 87.1%, above all compared online-learning methods.

Load-bearing premise

The speed advantage depends on treating the runtimes reported for other methods, measured on their own hardware and software, as directly comparable to RANet's 33 ms on one GPU; if those numbers were re-measured under identical conditions, the claimed speed-accuracy trade-off could change.

Editorial extensions

If this is right

  • Video object segmentation can run in real time (about 30 FPS at 480p) without online learning, removing the multi-second per-frame cost of fine-tuning.
  • The same network, when fine-tuned on the first frame for a few iterations, reaches J&F 87.1% on DAVIS16, beating online-learning methods that require far more compute.
  • The ranking attention module is a reusable way to convert variable-size sets of correspondence maps into a fixed-size representation for a CNN decoder.
  • Training on static images alone already gives J mean 83.2% on DAVIS16-trainval, so video annotations are not strictly necessary for a usable model.
  • For multi-object videos, sharing the encoder and similarity maps across objects keeps the cost nearly independent of the number of objects.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural test beyond the paper's experiments would be to randomize the order of similarity maps while keeping the same selection count; if accuracy does not drop, the learned ranking scores are not doing the causal work.
  • The speed comparison mixes runtimes measured by different groups on different GPUs and frameworks. My inference is that a controlled reimplementation on identical hardware would keep RANet's accuracy advantage but could shrink or reorder the speed gaps among fast offline methods.
  • The RAM design suggests a general pattern for dense correspondence tasks with variable object size: rather than aggregating matches into one map, keep a ranked subset of per-pixel matches as features for a downstream network. Object tracking and stereo matching are natural targets.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 5 minor

Summary. The paper proposes RANet, a Siamese encoder-decoder network for semi-supervised video object segmentation (VOS) that combines pixel-level matching with mask propagation. A Ranking Attention Module (RAM) scores each foreground/background similarity map, ranks them, and selects/pads them to a fixed 256-channel representation before feeding them, together with the previous frame's predicted mask, into a lightweight pyramid decoder. The network is trained end-to-end, first on static images and then fine-tuned on video, and can optionally be boosted by online learning (OL). Experiments on DAVIS16 and DAVIS17 report state-of-the-art offline accuracy (J&F=85.5% on DAVIS16-val at 33 ms/frame) and improved results with OL (J&F=87.1%). The paper claims a 'best speed-accuracy trade-off' among existing VOS methods.

Significance. If the reported results hold, RANet is a meaningful step toward real-time VOS with accuracy approaching online-learning methods. The RAM mechanism is novel in this context, and the ablation shows substantial gains over the 'w/o Ranking' and 'Maximum' baselines. The evaluation is thorough: standard benchmarks (DAVIS16/17), many prior methods, multiple ablations, and the code is publicly released. The main risk is that the headline speed-accuracy claim depends on runtime numbers for competing methods that are not measured under a common protocol, and one of the key ablation baselines is underspecified. These are addressable within the paper's scope.

major comments (2)
  1. [Section 4.2, Table 1, Figure 6] The headline claim of a 'best speed-accuracy trade-off' is supported only by runtime numbers for competing methods that appear to be taken from their original publications, while the manuscript specifies the hardware only for RANet (a TITAN Xp GPU). It does not state the hardware, software framework, or timing protocol (e.g., whether template feature extraction and input resizing are included) for the compared methods, nor whether these numbers are directly comparable across different GPUs and frameworks. Since Table 1 and Figure 6 are the basis of the speed-accuracy claim, please either measure representative competitors on the same GPU with the same protocol, or qualify the claim as applying to literature-reported runtimes and provide a table listing the hardware/framework for each reported time.
  2. [Section 4.3, Table 4] The 'w/o Ranking' baseline is not fully specified. The similarity maps have a dynamic number of channels equal to H0W0, while the decoder is designed for a fixed 256-channel input. The text states that this baseline 'maintains all the similarity maps' but does not explain how these maps are reduced or projected to the fixed channel size before being fed to the decoder, nor whether the decoder and merge module are identical to the full model. If the baseline uses padding, truncation, or a different aggregation without ranking, the comparison may not cleanly isolate the effect of the ranking mechanism. Please clarify the exact operation used in 'w/o Ranking' and confirm architectural consistency with the full RANet.
minor comments (5)
  1. [Section 3.2] The description 'we swap the spatial and channel dimensions of similarity maps (reshape S ... into Ŝ ...)' is imprecise; a reshape alone cannot transpose H0W0 with HW, so the operation should be described as a permutation/transpose followed by reshape.
  2. [Section 1] The sentence 'The proposed RANet achieves accurate VOS results even been trained only with static images' should read 'even when trained only with static images.'
  3. [Table 5] The RGMP row in Table 5 is unexplained. The table caption defines CL, PM, IP, and VF, but the text does not describe what the entries in the RGMP row (origin=81.5, -PM=73.5, -IP=68.6, -VF=55.0) correspond to, or why RGMP is included in RANet's ablation. Please either describe these variants or remove the row.
  4. [Table 3] The entry 'OSMN [37]' in Table 3 cites reference [37], which is OSVOS-S (Maninis et al.); the OSMN paper is reference [59] in the bibliography. Please correct the citation.
  5. [Section 4.1] The phrase 'The video data used are in different goals' is ungrammatical; it should be 'The video data are used for different goals.'

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the RANet derivation is self-contained, its components are trained end-to-end on held-out benchmarks, and no prediction is equivalent to a fitted input.

full rationale

The paper's central claims are an architecture (RANet) plus a learned ranking attention module (RAM), evaluated on DAVIS16/17. The derivation chain is not circular: correlation maps are computed from encoder features (Eq. 1), RAM ranking scores are a learned function of those maps (Eq. 2), and the selected maps are consumed by a decoder trained with a binary cross-entropy loss. No parameter is fitted to the reported J&F numbers, and the validation results are held-out measurements. The ablations (w/o Ranking, Maximum, -CL, -PM, -IP, -VF) compare re-trained variants on the same validation set, so the improvement attributed to RAM is an empirical result, not a construction. The few self-citations (e.g., Ref. [56], a pyramid-network reference; Refs. [32,52,54,55] in related work) are not load-bearing: the paper does not invoke any uniqueness theorem or prior result by the same authors to justify its design choices or forbid alternatives. The speed comparison in Table 1 uses runtimes from different papers and GPUs, which raises a legitimate experimental comparability concern, but this is a measurement fairness issue, not circularity: RANet's own 33 ms figure is not defined in terms of the competitors' numbers, and no speed value is fitted to make the claim. Therefore no circular step is exhibited, and the appropriate score is 0.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim relies on standard deep learning assumptions: pretrained features transfer, correlation captures similarity, and learned ranking scores generalize. Hyperparameters like the target channel size are hand-chosen but not fitted to the test set. No new physical or conceptual entities are postulated.

free parameters (4)
  • Target channel size for ranked similarity maps = 256
    Chosen by hand as the fixed number of foreground/background similarity maps fed to the decoder. Affects the capacity of the ranking module and the decoder.
  • Learning rates for Adam optimizer = 1e-5 for training, 1e-6 for online learning
    Standard hyperparameters chosen by hand; affect convergence and final performance.
  • Maximum frame interval for video fine-tuning = 5
    Randomly selects a mask near the current frame within 5 frames; controls temporal range of propagation during training.
  • TPS control points and shift margin = 16 control points, 15% margin
    Data augmentation settings chosen by hand following MaskTrack.
assumptions (4)
  • domain assumption Pixel-level correlation of l2-normalized features measures visual similarity between template and current frame.
    Used in Section 3.2 to define similarity maps S_j = K_j * I_t. This is a standard assumption in matching-based methods.
  • domain assumption ImageNet pretrained ResNet-101 features transfer to video object segmentation.
    The encoder backbone is pretrained on ImageNet and not trained from scratch for VOS; assumes generic features are useful for correlation.
  • domain assumption DAVIS dataset metrics (J and F) accurately reflect practical VOS quality.
    The paper uses DAVIS-16 and DAVIS-17 as the sole benchmarks for the central claim; assumes these benchmarks measure what matters for real applications.
  • domain assumption The two-layer scoring network fn can learn a useful ranking of similarity maps from training data.
    The ranking attention module relies on learned scores r_j = fn(S_hat) + fmax(S_hat). This assumes the training signal is sufficient to learn good importance scores.

how reviews work

0 comments
Cite this review

Pith. "Pith review of RANet: Ranking Attention Network for Fast Video Object Segmentation." pith.science (2026). https://pith.science/paper/7TOGNVDV

@misc{pith2026190806647,
  author       = {Pith},
  title        = {Pith review of: RANet: Ranking Attention Network for Fast Video Object Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7TOGNVDV}},
  note         = {Machine review of arXiv:1908.06647}
}
read the original abstract

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 pixel-level 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, exceeding state-of-the-art VOS methods. The code can be found at https://github.com/Storife/RANet.

Figures

Figures reproduced from arXiv: 1908.06647 by the authors.

Figure 1
Figure 1. Comparison of different VOS frameworks. (a) Matching based framework; (b) Propagation based frame￾work; and (c) Proposed RANet. We propose a novel Rank￾ing Attention module to rank and select important features. classifier on its first frame. Matching or propagation based methods have also been proposed for VOS. Matching based methods [8, 19] segment pixels according to the pixel-level matching scores between the fe… view at source ↗
Figure 2
Figure 2. Illustration of the proposed RANet. We compute correlation of the features extracted by Siamese networks. The output similarity maps and template mask are fed into the RAM module to rank and select the foreground/background similarity maps. Then these maps and the previous frame’s mask and fed into the decoder for final segmentation. ing for more stable pixel-level matching, but only calcu￾lates extreme value maps f… view at source ↗
Figure 3
Figure 3. Mechanism of the proposed Ranking Attention Module. In FG (or BG) path, only the FG (or BG) similarity maps are selected. The maps are ranked from top to bottom according to ranking scores learned from attention network, and padding or discarding is operated to make the 256 FG (or BG) maps. Finally, these maps are concatenated across the channel as features with the size of 256 × H × W. Propagation. Here we utilize … view at source ↗
Figures from the paper (3 more)
Figure 5
Figure 5. Figure 5: Illustrations of the training samples. we can obtain the BG ranking score vector r 0 . Finally, we rank the similarity maps in S 1 according to the corresponding scores in r 1 from largest to smallest: S 1 = Rank(S 1 |r 1 ). (3) If the number of the FG similarity maps …
Figure 6
Figure 6. Figure 6: Comparison of J &F Mean and Speed (in FPS) by different methods on DAVIS16-val dataset. Variant w/ RAM w/o Ranking Maximun J Mean 85.5 81.9 81.1 [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Qualitative results of the proposed RANet on challenging VOS scenarios. The test frames are from videos in the DAVIS16 set (1-st and 2-nd rows), the DAVIS17-val set (3-rd row), and the DAVIS17-testdev set (4-th and 5-th rows). Method origin -CL -PM -IP -VF RGMP [38] 81…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

62 extracted references · 56 canonical work pages

  1. [1]

    CNN in MRF: Video object segmentation via inference in a CNN-based higher-order spatio-temporal MRF

    Linchao Bao, Baoyuan Wu, and Wei Liu. CNN in MRF: Video object segmentation via inference in a CNN-based higher-order spatio-temporal MRF. In CVPR, 2018. 2, 6, 7

    work page 2018

  2. [2]

    Luca Bertinetto, Jack Valmadre, Joao Henriques, An- drea Vedaldi, and Philip H. S. Torr. Fully-convolutional siamese networks for object tracking. In ECCV Work- shops, pages 850–865, 2016. 2, 3, 4, 5, 6

    work page 2016

  3. [3]

    One-shot video object segmentation

    Sergi Caelles, Kevis-Kokitsi Maninis, Jordi Pont-Tuset, Laura Leal-Taixe, Daniel Cremers, and Luc Van Gool. One-shot video object segmentation. In CVPR, July

    work page

  4. [4]

    The 2018 davis challenge on video object segmentation

    Sergi Caelles, Alberto Montes, Kevis-Kokitsi Maninis, Yuhua Chen, Luc Van Gool, Federico Perazzi, and Jordi Pont-Tuset. The 2018 davis challenge on video object segmentation. arXiv:1803.00557, 2018. 1

    arXiv 2018

  5. [5]

    Semantic image segmentation with deep convolutional nets and fully connected crfs

    Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan Yuille. Semantic image segmentation with deep convolutional nets and fully connected crfs. In ICLR, 2015. 3

    work page 2015

  6. [6]

    Rethinking atrous convo- lution for semantic image segmentation

    Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam. Rethinking atrous convo- lution for semantic image segmentation. arXiv preprint arXiv:1706.05587, 2017. 3

    arXiv 2017

  7. [7]

    Encoder-decoder with atrous separable convolution for semantic image segmentation

    Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. Encoder-decoder with atrous separable convolution for semantic image segmentation. In ECCV, pages 801–818, 2018. 3

    work page 2018

  8. [8]

    Blazingly fast video object segmenta- tion with pixel-wise metric learning

    Yuhua Chen, Jordi Pont-Tuset, Alberto Montes, and Luc Van Gool. Blazingly fast video object segmenta- tion with pixel-wise metric learning. In CVPR, 2018. 1, 2, 6

    work page 2018

Show all 62 references
  1. [9]

    Fast and accu- rate online video object segmentation via tracking parts

    Jingchun Cheng, Yi Hsuan Tsai, Wei Chih Hung, Shengjin Wang, and Ming Hsuan Yang. Fast and accu- rate online video object segmentation via tracking parts. In CVPR, 2018. 1, 6, 7

    2018

  2. [10]

    Segflow: Joint learning for video object segmentation and optical flow

    Jingchun Cheng, Yi-Hsuan Tsai, Shengjin Wang, and Ming-Hsuan Yang. Segflow: Joint learning for video object segmentation and optical flow. In ICCV, Oct

  3. [11]

    Mitra, Xiaolei Huang, Philip H

    Ming-Ming Cheng, Niloy J. Mitra, Xiaolei Huang, Philip H. S. Torr, and Shi-Min Hu. Global con- trast based salient region detection. IEEE TPAMI , 37(3):569–582, 2015. 5

    2015

  4. [12]

    Flownet: Learning optical flow with convolutional net- works

    Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick Van Der Smagt, Daniel Cremers, and Thomas Brox. Flownet: Learning optical flow with convolutional net- works. In ICCV, pages 2758–2766, 2015. 2, 8

    2015

  5. [13]

    Salient ob- jects in clutter: Bringing salient object detection to the foreground

    Deng-Ping Fan, Ming-Ming Cheng, Jiang-Jiang Liu, Shang-Hua Gao, Qibin Hou, and Ali Borji. Salient ob- jects in clutter: Bringing salient object detection to the foreground. In ECCV. Springer, 2018. 5

    2018

  6. [14]

    Rethinking rgb-d salient object detec- tion: Models, datasets, and large-scale benchmarks

    Deng-Ping Fan, Zheng Lin, Jia-Xing Zhao, Yun Liu, Zhao Zhang, Qibin Hou, Menglong Zhu, and Ming- Ming Cheng. Rethinking rgb-d salient object detec- tion: Models, datasets, and large-scale benchmarks. arXiv:1907.06781, 2019. 5

    1907 arXiv

  7. [15]

    Shifting more attention to video salient object detection

    Deng-Ping Fan, Wenguan Wang, Ming-Ming Cheng, and Jianbing Shen. Shifting more attention to video salient object detection. In CVPR, 2019. 1, 5

    2019

  8. [16]

    Mask r-cnn

    Kaiming He, Georgia Gkioxari, Piotr Doll ´ar, and Ross Girshick. Mask r-cnn. In ICCV, pages 2961–2969,

  9. [17]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770–778, 2016. 3, 5

    2016

  10. [18]

    Norm matters: efficient and accurate nor- malization schemes in deep networks

    Elad Hoffer, Ron Banner, Itay Golan, and Daniel Soudry. Norm matters: efficient and accurate nor- malization schemes in deep networks. In NIPS, pages 2164–2174, 2018. 5

    2018

  11. [19]

    Yuan-Ting Hu, Jia-Bin Huang, and Alexander G. Schwing. Videomatch: Matching based video object segmentation. In ECCV, pages 56–73. Springer, 2018. 1, 2, 3, 6, 7

    2018

  12. [20]

    Flownet 2.0: Evolution of optical flow estimation with deep networks

    Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Ke- uper, Alexey Dosovitskiy, and Thomas Brox. Flownet 2.0: Evolution of optical flow estimation with deep networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2462– 2470, 2017. 2, 4, 8

    2017

  13. [21]

    Batch normaliza- tion: Accelerating deep network training by reducing internal covariate shift

    Sergey Ioffe and Christian Szegedy. Batch normaliza- tion: Accelerating deep network training by reducing internal covariate shift. In ICML, pages 448–456, 2015. 5

    2015

  14. [22]

    Varun Jampani, Raghudeep Gadde, and Peter V . Gehler. Video propagation networks. In CVPR, July 2017. 6

    2017

  15. [23]

    Online video ob- ject segmentation via convolutional trident network

    Won-Dong Jang and Chang-Su Kim. Online video ob- ject segmentation via convolutional trident network. In CVPR, July 2017. 2, 6

    2017

  16. [24]

    Stere- onet: Guided hierarchical refinement for real-time edge-aware depth prediction

    Sameh Khamis, Sean Ryan Fanello, Christoph Rhe- mann, Julien Valentin, and Shahram Izadi. Stere- onet: Guided hierarchical refinement for real-time edge-aware depth prediction. In Europen Conference on Computer Vision (ECCV), 2018. 8

    2018

  17. [25]

    Lucid data dreaming for ob- ject tracking

    Anna Khoreva, Rodrigo Benenson, Eddy Ilg, Thomas Brox, and Bernt Schiele. Lucid data dreaming for ob- ject tracking. In The DAVIS Challenge on Video Object Segmentation, 2017. 2

    2017

  18. [26]

    Adam: A method for stochastic optimization

    Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2014. 5

    2014

  19. [27]

    Hin- ton

    Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hin- ton. Imagenet classification with deep convolutional neural networks. In NIPS, pages 1097–1105. 2012. 3, 5

    2012

  20. [28]

    Instance-level salient object segmentation

    Guanbin Li, Yuan Xie, Liang Lin, and Yizhou Yu. Instance-level salient object segmentation. In CVPR, pages 247–256. IEEE, 2017. 5

    2017

  21. [29]

    Visual saliency based on multiscale deep features

    Guanbin Li and Yizhou Yu. Visual saliency based on multiscale deep features. CVPR, 2015. 5

    2015

  22. [30]

    Video object segmentation with joint re-identification and attention- aware mask propagation

    Xiaoxiao Li and Chen Change Loy. Video object segmentation with joint re-identification and attention- aware mask propagation. In ECCV, pages 90–105,

  23. [31]

    Refinenet: Multi-path refinement networks for high-resolution semantic segmentation

    Guosheng Lin, Anton Milan, Chunhua Shen, and Ian Reid. Refinenet: Multi-path refinement networks for high-resolution semantic segmentation. In CVPR, pages 5168–5177. IEEE, 2017. 4

    2017

  24. [32]

    See more, know more: Unsupervised video object segmentation with co-attention siamese networks

    Xiankai Lu, Wenguan Wang, Chao Ma, Jianbing Shen, Ling Shao, and Fatih Porikli. See more, know more: Unsupervised video object segmentation with co-attention siamese networks. In CVPR, June 2019. 5

    2019

  25. [33]

    Premvos: Proposal-generation, refinement and merging for the davis challenge on video object segmentation

    Jonathon Luiten, Paul V oigtlaender, and Bastian Leibe. Premvos: Proposal-generation, refinement and merging for the davis challenge on video object segmentation

  26. [34]

    Premvos: Proposal-generation, refinement and merging for the youtube-vos challenge on video object segmen- tation 2018

    Jonathon Luiten, Paul V oigtlaender, and Bastian Leibe. Premvos: Proposal-generation, refinement and merging for the youtube-vos challenge on video object segmen- tation 2018. The 1st Large-scale Video Object Segmen- tation Challenge - ECCV 2018 Workshops, 2018. 2

    2018

  27. [35]

    Premvos: Proposal-generation, refinement and merging for video object segmentation

    Jonathon Luiten, Paul V oigtlaender, and Bastian Leibe. Premvos: Proposal-generation, refinement and merging for video object segmentation. In ACCV, 2018. 2, 6

    2018

  28. [36]

    Bilateral space video seg- mentation

    Nicolas Maerki, Federico Perazzi, Oliver Wang, and Alexander Sorkine-Hornung. Bilateral space video seg- mentation. In CVPR, June 2016. 6

    2016

  29. [37]

    Video object segmentation without tem- poral information

    Kevis-Kokitsi Maninis, Sergi Caelles, Yuhua Chen, Jordi Pont-Tuset, Laura Leal-Taix, Daniel Cremers, and Luc Van Gool. Video object segmentation without tem- poral information. TPAMI, 2018. 1, 2, 6, 7

    2018

  30. [38]

    Fast video object segmentation by reference-guided mask propagation

    Seoung Wug Oh, Joon-Young Lee, Kalyan Sunkavalli, and Seon Joo Kim. Fast video object segmentation by reference-guided mask propagation. In CVPR, 2018. 1, 2, 6, 7, 8

    2018

  31. [39]

    Towards bridging semantic gap to improve se- mantic segmentation

    Yanwei Pang, Yazhao Li, Jianbing Shen, and Ling Shao. Towards bridging semantic gap to improve se- mantic segmentation. In ICCV, 2019. 3

    2019

  32. [40]

    Learn- ing video object segmentation from static images

    Federico Perazzi, Anna Khoreva, Rodrigo Benenson, Bernt Schiele, and Alexander Sorkine-Hornung. Learn- ing video object segmentation from static images. In CVPR, July 2017. 1, 2, 3, 4, 5, 6, 7

    2017

  33. [41]

    A benchmark dataset and evaluation method- ology for video object segmentation

    Federico Perazzi, Jordi Pont-Tuset, Brian McWilliams, Luc Van Gool, Markus Gross, and Alexander Sorkine- Hornung. A benchmark dataset and evaluation method- ology for video object segmentation. In CVPR, pages 724–732, 2016. 1, 2, 5, 6

    2016

  34. [42]

    The 2017 davis challenge on video object seg- mentation

    Jordi Pont-Tuset, Federico Perazzi, Sergi Caelles, Pablo Arbel´aez, Alexander Sorkine-Hornung, and Luc Van Gool. The 2017 davis challenge on video object seg- mentation. arXiv:1704.00675, 2017. 1, 2, 5, 6

    2017 arXiv

  35. [43]

    Deepmatching: Hierarchical deformable dense matching

    Jerome Revaud, Philippe Weinzaepfel, Zaid Harchaoui, and Cordelia Schmid. Deepmatching: Hierarchical deformable dense matching. International Journal of Computer Vision, 120(3):1–24, 2016. 2

    2016

  36. [44]

    U-net: Convolutional networks for biomedical image segmentation

    Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In MICCAI, pages 234–241. Springer,

  37. [45]

    Pixel-level matching for video object segmentation using convo- lutional neural networks

    Jae Shin Yoon, Francois Rameau, Junsik Kim, Seokju Lee, Seunghak Shin, and In So Kweon. Pixel-level matching for video object segmentation using convo- lutional neural networks. In ICCV, Oct 2017. 1, 2, 6

    2017

  38. [46]

    Pyramid dilated deeper convlstm for video salient object detection

    Hongmei Song, Wenguan Wang, Sanyuan Zhao, Jian- bing Shen, and Kin-Man Lam. Pyramid dilated deeper convlstm for video salient object detection. In ECCV,

  39. [47]

    Yi-Hsuan Tsai, Ming-Hsuan Yang, and Michael J. Black. Video segmentation via object flow. In CVPR, June 2016. 1, 2, 6

    2016

  40. [48]

    Instance normalization: The missing ingredient for fast stylization

    Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempit- sky. Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 ,

    arXiv

  41. [49]

    Feelvos: Fast end-to-end embedding learning for video object seg- mentation

    Paul V oigtlaender, Yuning Chai, Florian Schroff, Hartwig Adam, and Liang Chieh Chen. Feelvos: Fast end-to-end embedding learning for video object seg- mentation. In CVPR, 2019. 2, 3, 4, 6, 7, 8

    2019

  42. [50]

    Online adapta- tion of convolutional neural networks for video object segmentation

    Paul V oigtlaender and Bastian Leibe. Online adapta- tion of convolutional neural networks for video object segmentation. In BMVC, 2017. 1, 2, 6, 7

    2017

  43. [51]

    Fast online object tracking and seg- mentation: A unifying approach

    Qiang Wang, Li Zhang, Luca Bertinetto, Weiming Hu, and Philip HS Torr. Fast online object tracking and seg- mentation: A unifying approach. InCVPR. IEEE, 2019. 1, 6, 7, 8

    2019

  44. [52]

    Zero-shot video object segmen- tation via attentive graph neural networks

    Wenguan Wang, Xiankai Lu, David Crandall, Jianbing Shen, and Ling Shao. Zero-shot video object segmen- tation via attentive graph neural networks. In ICCV,

  45. [53]

    Se- lective video object cutout

    Wenguan Wang, Jianbing Shen, and Fatih Porikli. Se- lective video object cutout. IEEE Transactions on Im- age Processing, 26(12):5645–5655, 2017. 1

    2017

  46. [54]

    Semi-supervised video object segmen- tation with super-trajectories

    Wenguan Wang, Jianbing Shen, Fatih Porikli, and Ruigang Yang. Semi-supervised video object segmen- tation with super-trajectories. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(4):985– 998, 2019. 1

    2019

  47. [55]

    Saliency-aware video object segmenta- tion

    Wenguan Wang, Jianbing Shen, Ruigang Yang, and Fatih Porikli. Saliency-aware video object segmenta- tion. IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, 40(1):20–33, 2018. 5

    2018

  48. [56]

    Dense residual pyramid networks for salient object detection

    Ziqin Wang, Peilin Jiang, and Fei Wang. Dense residual pyramid networks for salient object detection. InACCV Workshop, pages 606–621, 2016. 4

    2016

  49. [57]

    Joint detection and identification fea- ture learning for person search

    Tong Xiao, Shuang Li, Bochao Wang, Liang Lin, and Xiaogang Wang. Joint detection and identification fea- ture learning for person search. In CVPR, pages 3415– 3424, 2017. 2

    2017

  50. [58]

    Hierar- chical saliency detection

    Qiong Yan, Li Xu, Jianping Shi, and Jiaya Jia. Hierar- chical saliency detection. CVPR, 2013. 5

    2013

  51. [59]

    Katsaggelos

    Linjie Yang, Yanran Wang, Xuehan Xiong, Jianchao Yang, and Aggelos K. Katsaggelos. Efficient video object segmentation via network modulation. CVPR,

  52. [60]

    Contrast prior and fluid pyramid integration for rgbd salient object detec- tion

    Jia-Xing Zhao, Yang Cao, Deng-Ping Fan, Xuan-Yi Li, Le Zhang, and Ming-Ming Cheng. Contrast prior and fluid pyramid integration for rgbd salient object detec- tion. In CVPR, 2019. 5

    2019

  53. [61]

    EGNet: Edge guidance network for salient object detection

    Jia-Xing Zhao, Jiang-Jiang Liu, Deng-Ping Fan, Yang Cao, Jufeng Yang, and Ming-Ming Cheng. EGNet: Edge guidance network for salient object detection. In ICCV, 2019. 5

    2019

  54. [2018]

    The 2018 DAVIS Challenge on Video Object Seg- mentation - CVPR Workshops, 2018. 2

    2018

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.