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"Triple critical feature capture network: A triple critical feature cap" by Zhoufeng Liu, Kaihua Wang et al.

Weakly supervised object detection (WSOD) is becoming increasingly important for computer vision tasks, as it alleviates the burden of manual annotation. Most WSOD techniques rely on multiple instance learning (MIL), which tends to localise the discriminative parts of salient objects instead of the whole object. In addition, network training is often supervised using simple image-level annotations, without including object quantities or location information. However, this can lead to ambiguous differentiation of object instances, both in terms of location and semantics. To address these issues, propose an end-to-end triple critical feature capture network (TCFCNet) for WSOD is proposed. Specifically, a multi-task branch, which can perform fully supervised classification and regression task, was integrated with a PCL in an end-to-end network for refining object locations in an online method. A cyclic parametric dropblock module (CPDM) was then designed to help the detector focus on the ....

Computer Vision , Image Processing , Object Detection ,