WaterNet: An adaptive matching pipeline for segmenting water with volatile appearance

Liang, Yongqing; Jafari, Navid H.; Luo, Xing; Chen, Qin; Cao, Yanan; Li, Xin

doi:10.1007/s41095-020-0156-x

Cited by 14 publications

(18 citation statements)

References 19 publications

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“…Since the webcams provided images with different resolutions, the images were resized with padding to a size of 512 × 512 × 3 making them compatible with the models. For the segmentation model training, two additional datasets were provided by Kaggle Water Segmentation Dataset (see Liang et al., 2020) as well as Flood Segmentation Dataset (Pally et al., 2022) was used. Liang et al.…”

Section: Case Studies and Datamentioning

confidence: 99%

An End‐To‐End Flood Stage Prediction System Using Deep Neural Networks

Windheuser

Karanjit

Phillips

et al. 2023

Earth and Space Science

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Floods are on the rise globally with the frequent record-breaking events occurring during the past few years in the US alone. These extreme events pose a considerable threat to human life and result in destructive damage to property, communities, and the built environment (e.g., Phillips et al., 2018). The south and the southeast US have experienced frequent storms with annually, on average, more than 85 named and unnamed thunderstorms (NWS, 2020). These events happened in quick succession (∼2 weeks apart) and produced catastrophic flooding in wide geographic areas (∼1,000 km swath) and within short timespans (less than a 48-hr period; Donratanapat et al., 2020). Successive flood events can even lead to higher costs in terms of repairing and rebuilding destroyed buildings and critical infrastructures (CIs) due to a lack of early warning systems (e.g., Donratanapat et al., 2020;Field et al., 2012;Hinkel et al., 2014). This necessitates the importance of detecting flood magnitudes ahead of the event to protect communities and CIs. The flood stage is the height of the water surface in a stream gaging station, not the height throughout the stream. A vast amount of research has been conducted to develop different tools and test their reliability in predicting near real-time flood stage estimation (Krzysztofowicz et al., 1994).

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Section: Case Studies and Datamentioning

confidence: 99%

An End‐To‐End Flood Stage Prediction System Using Deep Neural Networks

Windheuser

Karanjit

Phillips

et al. 2023

Earth and Space Science

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“…(Lin, Qi, and Jia 2019;Voigtlaender et al 2019;Wang et al 2019;Yang, Wei, and Yang 2020) further leverage both the first and the previous frames. Several recent methods (Hu, Huang, and Schwing 2018;Liang et al 2020a;Duke et al 2021) turn to use several latest frames to further improve the local temporal guidance. Moreover, STM-based networks (Oh et al 2019;Seong, Hyun, and Kim 2020;Lu et al 2020;Liang et al 2020b,c;Cheng, Tai, and Tang 2021b;Wang et al 2021;Xie et al 2021;Hu et al 2021;Seong et al 2021) boost the performance with memory networks that memorize information from past frames for further reuse, which relieve the error propagation to some extent.…”

Section: Related Workmentioning

confidence: 99%

Reliable Propagation-Correction Modulation for Video Object Segmentation

Wang

Xiao

et al. 2022

AAAI

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Error propagation is a general but crucial problem in online semi-supervised video object segmentation. We aim to suppress error propagation through a correction mechanism with high reliability. The key insight is to disentangle the correction from the conventional mask propagation process with reliable cues. We introduce two modulators, propagation and correction modulators, to separately perform channel-wise recalibration on the target frame embeddings according to local temporal correlations and reliable references respectively. Specifically, we assemble the modulators with a cascaded propagation-correction scheme. This avoids overriding the effects of the reliable correction modulator by the propagation modulator. Although the reference frame with the ground truth label provides reliable cues, it could be very different from the target frame and introduce uncertain or incomplete correlations. We augment the reference cues by supplementing reliable feature patches to a maintained pool, thus offering more comprehensive and expressive object representations to the modulators. In addition, a reliability filter is designed to retrieve reliable patches and pass them in subsequent frames. Our model achieves state-of-the-art performance on YouTube-VOS18, YouTube-VOS19 and DAVIS17-Val/Test benchmarks. Extensive experiments demonstrate that the correction mechanism provides considerable performance gain by fully utilizing reliable guidance.

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“…For propagation-based models [3,23,42], the guidance of segmentation masks from past frames are introduced during the process of mask decoding. For matching-based models [5,10,17,17,26,27,30,35,49,55,63,65,67], an embedding space is learnt for target objects. Recently, STM-based networks [6, 16, 28, 32, 39, 46, 47, 52? ] achieve impressive results with memory networks that memorize and read information from past frames.…”

Section: Related Workmentioning

confidence: 99%

Towards Robust Video Object Segmentation with Adaptive Object Calibration

Xu¹,

Wang²,

Xiang³

et al. 2022

Preprint

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In the booming video era, video segmentation attracts increasing research attention in the multimedia community. Semi-supervised video object segmentation (VOS) aims at segmenting objects in all target frames of a video, given annotated object masks of reference frames. Most existing methods build pixel-wise reference-target correlations and then perform pixel-wise tracking to obtain target masks. Due to neglecting object-level cues, pixel-level approaches make the tracking vulnerable to perturbations, and even indiscriminate among similar objects. Towards robust VOS, the key insight is to calibrate the representation and mask of each specific object to be expressive and discriminative. Accordingly, we propose a new deep network, which can adaptively construct object representations and calibrate object masks to achieve stronger robustness. First, we construct the object representations by applying an adaptive object proxy (AOP) aggregation method, where the proxies represent arbitrary-shaped segments at multi-levels for reference. Then, prototype masks are initially generated from the reference-target correlations based on AOP. Afterwards, such proto-masks are further calibrated through network modulation, conditioning on the object proxy representations. We consolidate this conditional mask calibration process in a progressive manner, where the object representations and proto-masks evolve to be discriminative iteratively. Extensive experiments are conducted on the standard VOS benchmarks, YouTube-VOS-18/19 and DAVIS-17. Our model achieves the state-of-the-art performance among existing published works, and also exhibits superior robustness against perturbations. CCS CONCEPTS• Computing methodologies → Video segmentation.

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WaterNet: An adaptive matching pipeline for segmenting water with volatile appearance

Cited by 14 publications

References 19 publications

An End‐To‐End Flood Stage Prediction System Using Deep Neural Networks

An End‐To‐End Flood Stage Prediction System Using Deep Neural Networks

Reliable Propagation-Correction Modulation for Video Object Segmentation

Towards Robust Video Object Segmentation with Adaptive Object Calibration

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