Co-Saliency Detection via a Self-Paced Multiple-Instance Learning Framework

By Dingwen Zhang, Deyu Meng, Junwei Han. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017

484 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Sensory Systems

View Paper: https://www.semanticscholar.org/paper/382fee19aac78874105950adac057afc6ed26faf

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Citations in the corpus

Weakly Supervised Object Localization and Detection: A Survey (2021, unknown): “In addition, the co-occurring patterns mined in co-saliency detection [196], [201] and object co-localization [118], [171] approaches can also be used as informative priors to guide the deep multiple instance learning process in weakly supervised object localization and detection.”
Unsupervised Person Re-identification (2017, methodology): “SPL is to prevent latent variable models from getting stuck in a bad local optimum or oscillating. Then SPL is widely used in weakly-supervised and semi-supervised learning. For example, Zhang et al. [44] integrate self-paced learning into multi-instance learning for co-Saliency detection. In this p”
What Objective Does Self-paced Learning Indeed Optimize? (2015, background): “5,1 norm on v, as utilized in [19] and [21], respectively.”
Learning to Detect Salient Objects with Image-Level Supervision (2017, background): “In co-saliency [10, 54], the assumption that a group of images contain common objects serves as a form of weak supervision.”
JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection (2020, background): “It has many useful applications, including object segmentation and recognition [27, 32, 39, 51, 70, 79], image/video compression [24], video detection/summarization [19, 41], content-based image editing [14, 23, 42, 57, 63], informative common object discovery [71,72], and image retrieval [8,22,37].”
Enhancing Sketch-Based Image Retrieval by CNN Semantic Re-ranking (2020, methodology): “CBIR systems often use the saliency detection techniques [55]–[57], [67] to detect the saliency regions of images, and then the regions are used for image retrieval.”
Progressive Meta-Learning With Curriculum (2022, background): “Evidence has shown that CL is instrumental in avoiding bad local minima as well as achieving better generalization results [12], [32].”
Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey (2018, methodology): “To address the noise issue in human annotation, further research could design weighting-based learning mechanisms (such as the models based on self-paced learning [94], [96] and curriculum learning [97], [98]) to further improve the learning robustness when the human annotations of the learned objec”
CNNs-Based RGB-D Saliency Detection via Cross-View Transfer and Multiview Fusion (2018, background): “, co-saliency detection [52], [53], [55], video saliency detection [54], and”
Salient Object Detection via Integrity Learning (2021, background): “[54] established a self-paced multi-instance learning framework to produce the proper metrics to measure the intra-image contrast and the inter-image consistency in a self-learning way.”
CycleSegNet: Object Co-Segmentation With Cycle Refinement and Region Correspondence (2021, background): “Co-saliency detection [12], [15], [51], [62]–[64] is a closely”
Progressively Complementarity-Aware Fusion Network for RGB-D Salient Object Detection (2018, methodology): “Traditional saliency detection models [3, 7-10] are performed merely on RGB images and can be categorized as bottom-up and top-down pipelines.”
Video Salient Object Detection via Fully Convolutional Networks (2017, background): “In recent years, the border of saliency detection has been extend to capturing common saliency among related images/videos [41]–[43], [45], [48], inferring the salient event with video sequences [40] or scene understanding [44], [50], [51].”