Self-Sufficient Feature Enhancing Networks for Video Salient Object Detection

Kong, Yongqiang; Wang, Yunlong; Li, Annan; Huang, Qiuyu

doi:10.1109/tmm.2021.3129052

Cited by 6 publications

(2 citation statements)

References 74 publications

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“…Unlike image-based saliency object detection that only uses spatial information within a single frame to predict saliency maps, VSOD needs to explore the motion information hidden in video sequences. However, many works [9][10][11][12][13] have not focused on the impact of motion information on saliency detection results. For example, Zhang et al [14] proposed the determination of temporal and spatial misalignment by fusing the temporal alignment feature and spatial feature of adjacent frames.…”

Section: Introductionmentioning

confidence: 99%

Video Saliency Object Detection with Motion Quality Compensation

Wang

Chen

et al. 2023

Electronics

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Video saliency object detection is one of the classic research problems in computer vision, yet existing works rarely focus on the impact of input quality on model performance. As optical flow is a key input for video saliency detection models, its quality significantly affects model performance. Traditional optical flow models only calculate the optical flow between two consecutive video frames, ignoring the motion state of objects over a period of time, leading to low-quality optical flow and reduced performance of video saliency object detection models. Therefore, this paper proposes a new optical flow model that improves the quality of optical flow by expanding the flow perception range and uses high-quality optical flow to enhance the performance of video saliency object detection models. Experimental results on the datasets show that the proposed optical flow model can significantly improve optical flow quality, with the S-M values on the DAVSOD dataset increasing by about 39%, 49%, and 44% compared to optical flow models such as PWCNet, SpyNet, and LFNet. In addition, experiments that fine-tuning the benchmark model LIMS demonstrate that improving input quality can further improve model performance.

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Section: Introductionmentioning

confidence: 99%

Video Saliency Object Detection with Motion Quality Compensation

Wang

Chen

et al. 2023

Electronics

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“…The video salient object detection (VSOD), also known as zero-shot video segmentation [1], [2], [3], [4], [5], [6], has received extensive research attention in recent years, whose primary objective is to segment video objects that attract the human visual attention most [7], [8], [9]. Different from the widely studied image salient object detection (ISOD) using spatial information only [10], [11], [12], the temporal information provided by the video data makes the saliency detection task more difficult [13], [14], [15], and we give an in-depth discussion regarding this issue to clearly demonstrate our motivation.…”

Section: Introductionmentioning

confidence: 99%

A Novel Long-term Iterative Mining Scheme for Video Salient Object Detection

Chen¹,

Wang²,

Fang³

et al. 2022

Preprint

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The existing state-of-the-art (SOTA) video salient object detection (VSOD) models have widely followed short-term methodology, which dynamically determines the balance between spatial and temporal saliency fusion by solely considering the current consecutive limited frames. However, the short-term methodology has one critical limitation, which conflicts with the real mechanism of our visual system -a typical longterm methodology. As a result, failure cases keep showing up in the results of the current SOTA models, and the short-term methodology becomes the major technical bottleneck. To solve this problem, this paper proposes a novel VSOD approach, which performs VSOD in a complete long-term way. Our approach converts the sequential VSOD, a sequential task, to a data mining problem, i.e., decomposing the input video sequence to object proposals in advance and then mining salient object proposals as much as possible in an easy-to-hard way. Since all object proposals are simultaneously available, the proposed approach is a complete long-term approach, which can alleviate some difficulties rooted in conventional short-term approaches. In addition, we devised an online updating scheme that can grasp the most representative and trustworthy pattern profile of the salient objects, outputting framewise saliency maps with rich details and smoothing both spatially and temporally. The proposed approach outperforms almost all SOTA models on five widely used benchmark datasets.

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A Novel Divide and Conquer Solution for Long-term Video Salient Object Detection

Li,

Chen,

et al. 2024

Mach. Intell. Res.

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Self-Sufficient Feature Enhancing Networks for Video Salient Object Detection

Cited by 6 publications

References 74 publications

Video Saliency Object Detection with Motion Quality Compensation

Video Saliency Object Detection with Motion Quality Compensation

A Novel Long-term Iterative Mining Scheme for Video Salient Object Detection

A Novel Divide and Conquer Solution for Long-term Video Salient Object Detection

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