Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection

By Hongmei Song, Wenguan Wang, Sanyuan Zhao, Jianbing Shen, Kin-Man Lam. European Conference on Computer Vision, 2018

461 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b2807613304c0ee8dcedf0a78d448b95f33816be

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In Computer Vision and Pattern Recognition

Citations in the corpus

Salient Object Detection in the Deep Learning Era: An In-Depth Survey (2019, unknown): “In computer vision, sampled applications of SOD include image understanding [1], [2], image cap-tioning [3]–[5], object detection [6], [7], un-supervised video object segmentation [8], [9], semantic segmentation [10]– [12], person re-identiﬁcation [13], [14], etc .”
Learning Video Object Segmentation From Unlabeled Videos (2020, methodology): “PDB[45], and slightly slower than RVOS [53] (0.”
Survey on Deep Neural Networks in Speech and Vision Systems (2019, background): “One of the recent studies has used 3D CNN [116] to capture the temporal information, while another study [117] incorporates LSTM on top of CNN to capture the temporal information.”
A Survey of Deep Learning-Based Object Detection (2019, background): “provide benchmarks for video salient object detection, and existing good algorithms [230] [231] [224] [232] [227] [233] [234] [235] [236] [237] [238][239][240][241] devote in this domain.”
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks (2019, methodology): “depth-depurator unit to explicitly exclude low-quality depth maps when learning complementary feature by PDC [84] module.”
Opening up Open World Tracking (2021, methodology): “5 J & F), outperforming several recent methods [2, 25, 72, 77, 81, 92], except UnOVOST, all fine-tuned for segmenting dominantly moving regions (67.”
Suppress and Balance: A Simple Gated Network for Salient Object Detection (2020, methodology): “We follow the training strategy as AGS [56], COSNet [31], PDB [44] and MATNet [73] to use extra datasets.”
Learning Unsupervised Video Object Segmentation Through Visual Attention (2019, methodology): “Later, with the renaissance of neural network, many deep learning based models were proposed, which typically use multilayer perceptron based moving objectness detector, adopt two-stream architecture [67, 33], or CNN encoder-decoder structure [66, 11, 45, 46, 64].”
AMP: Adaptive Masked Proxies for Few-Shot Segmentation (2019, methodology): “Measure FSeg [10] LVO [36] MOTAdapt [30] ARP [13] PDB [33] AMP + CRF (Ours) J Mean 70.7 75.9 77.2 76.2 77.2 78.9 Recall 83.5 89.1 87.8 91.1 90.1 91.6 Decay 1.5 7.0 5.0 7.0 0.9 4.7 F Mean 65.3 72.1 77.4 70.6 74.5 78.4 Recall 73.8 83.4 84.4 83.5 84.4 87.3 Decay 1.8 1.3 3.3 7.9 0.2 2.7 Table 6: Quantit”
Robust Lane Detection From Continuous Driving Scenes Using Deep Neural Networks (2019, unknown): “In [61], a spatio-temporal saliency learning module”
Spatiotemporal Knowledge Distillation for Efficient Estimation of Aerial Video Saliency (2019, background): “In the past decades, many models have been constructed in visual saliency estimation by defining comprehensive rules [6]–[9] or using deep learning frameworks [10]–[12].”
Revisiting Video Saliency Prediction in the Deep Learning Era (2021, background): “A related topic is salient object detection [63], [64] that uniformly highlights salient object regions in images [65], [66], [67], [68], [69], videos [70], [71], [72], [73], image/video groups [74], or RGBD data [75], [76].”
Video Object Segmentation with Episodic Graph Memory Networks (2020, methodology): “Following common practice [8, 42, 45], we employ CRF [20] binarization and the whole processing speed is about 0.”
See More, Know More: Unsupervised Video Object Segmentation With Co-Attention Siamese Networks (2019, methodology): “Compared with the second best method, PDB [49], our COSNet achieves gains of 2.”
Salient Object Detection With Pyramid Attention and Salient Edges (2019, unknown): “Other methods leverage global and local saliency information [21, 34, 54, 29], combine pixel- and segment-level features [22], inspire connections between network layers [14], or explore more complex deep architectures [18, 26, 55, 37, 32].”
Salient Object Detection via Integrity Learning (2021, background): “[49] combined pyramid dilated convolution with convolutional LSTM [50] to capture multi-scale spatiotemporal information for video salient object detection.”
JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection (2020, background): “Many SOD models have been developed under the assumption that the inputs are individual RGB/color images [21, 47, 66, 74–76] or sequences [56, 62, 67, 68].”