Saliency Detection by Multitask Sparsity Pursuit

By Congyan Lang, Guangcan Liu, Jian Yu, Shuicheng Yan. 2012

199 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Sensory Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Junwei Han
3. Ming-Ming Cheng
4. Ming-Hsuan Yang
5. Luc Van Gool
6. Ling Shao
7. Huchuan Lu
8. Yi Yang

In Sensory Systems

1. Junwei Han
2. Dingwen Zhang
3. Ming-Ming Cheng
4. Huan Fu
5. Dong Xu
6. Haibin Ling
7. Huaizu Jiang
8. Yuan Xie

Citations in the corpus

• An Overview of Multi-task Learning (2018, background): “…on non-facial images include object categorization [86], image segmentation [89,90], identifying brain imaging predictors [91], saliency detection [92], action recognition [93], scene classification [94], multi-attribute prediction [95], multi-camera person re-identification [96], and immediacy…”
• A Survey on Multi-Task Learning (2017, methodology): “In [168], a multi-task low-rank subspace clustering, where different tasks are related via the structural sparsity among the spanning matrices in subspace clustering, is proposed for image segmentation and a similar model is used in [169] for saliency detection.”
• Cluster-based Co-saliency Detection (2013, unknown): “Most of existing saliency algorithms formulate on detecting the salient object from an individual image [6]–[8].”
• Background Prior-Based Salient Object Detection via Deep Reconstruction Residual (2015, background): “A similar phenomenon is also discovered in [48] and [49].”
• A Generalized Low-Rank Appearance Model for Spatio-temporally Correlated Rain Streaks (2013, methodology): “Although our method may lose small-scale features, we could still use the proposed detail enhancement to compensate such information.”
• Saliency Detection via a Multiple Self-Weighted Graph-Based Manifold Ranking (2020, methodology): “Some methods simulate saliency detection in Bayesian inference models [10], [11] and sparse reconstruction [12], [13].”
• Salient Object Detection via Structured Matrix Decomposition (2017, methodology): “The resulting saliency maps of previous solutions (LRR [27], ULR [26] and SLR [28]) are scattered and incomplete, while our algorithm (SMD) overcomes these difficulties and performs close to the ground truth (GT).”
• Salient Object Detection: A Discriminative Regional Feature Integration Approach (2013, methodology): “Recent developments include using isocentric curvedness and color [44], adopting image histogram [32], quaternion-based spectral analysis [40], utilizing depth cues [30], multitask sparsity pursuit [29], statistically modeling [42], exploring patch rarities [6], combing bottom-up and top-down featur”
• Generalized Latent Multi-View Subspace Clustering (2020, background): “This enforces ES and EV to be with the same pattern of column-wise sparsity [45].”