1. A Highly Efficient Model to Study the Semantics of Salient Object Detection
- Author
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Ming-Ming Cheng, Ali Borji, Zheng Lin, Meng Wang, Shang-Hua Gao, and Yong-Qiang Tan
- Subjects
Source code ,Computer science ,business.industry ,Applied Mathematics ,media_common.quotation_subject ,Feature extraction ,Semantics ,Machine learning ,computer.software_genre ,Object detection ,Reduction (complexity) ,Computational Theory and Mathematics ,Artificial Intelligence ,Information leakage ,Feature (machine learning) ,Redundancy (engineering) ,Neural Networks, Computer ,Computer Vision and Pattern Recognition ,Artificial intelligence ,business ,computer ,Algorithms ,Software ,media_common - Abstract
CNN-based salient object detection (SOD) methods achieve impressive performance. However, the way semantic information is encoded in them and whether they are category-agnostic is less explored. One major obstacle in studying these questions is the fact that SOD models are built on top of the ImageNet pre-trained backbones which may cause information leakage and feature redundancy. To remedy this, here we first propose an extremely light-weight holistic model tied to the SOD task that can be freed from classification backbones and trained from scratch, and then employ it to study the semantics of SOD models. With the holistic network and representation redundancy reduction by a novel dynamic weight decay scheme, our model has only 100K parameters, ∼ 0.2% of parameters of large models, and performs on par with SOTA on popular SOD benchmarks. Using CSNet, we find that a) SOD and classification methods use different mechanisms, b) SOD models are category insensitive, c) ImageNet pre-training is not necessary for SOD training, and d) SOD models require far fewer parameters than the classification models. The source code is publicly available at https://mmcheng.net/sod100k/.
- Published
- 2022