报告名称：Training mixture of weighted SVM for object detection using EM algorithm
Inspired by the idea of divide-and-conquer approach and discriminatively trained SVM model for object detection, we introduce a method of training mixture of weighted SVM models using EM algorithm. In this talk, we introduce a new part weighted SVM with logistic function to convert its prediction score into pseudo-probability. The part weight is computed by an energy estimation method to reflect the discriminative power of different object parts, and the conversion of prediction score to probability enables the input to be assigned to a proper SVM based on unbiased prediction scores among multiple SVM models. More importantly, the two modifications fit the joint training process of multiple SVMs into the EM framework, where we could iteratively reassign the object examples into different sub-regions of the entire input space, and then retrain the SVM models corresponding to that sub-region. In this way, the mixture of SVM models becomes a set of “experts” to form the mixture of DPMs. Experimental results show that our proposed method made noticeable improvements over the baseline method, which demonstrates the advantage of our proposed method for training MDPM based models for object detection.
De Cheng received the B.S. degree in automation control, in 2011, from Xi’an Jiaotong University, Xi’an, China, where he obtained his Ph.D. degree with the Institute of Artificial Intelligence and Robotics in 2017. He is now a faculty member in the Department of Computer Science and Technology. His research interests include pattern recognition and machine learning, specifically in the areas of person re-id, object detection and image classification.
报告名称：Bridging saliency detection to weakly supervised object detection based on self-paced
Weakly-supervised object detection (WOD) is a challenging problems in computer vision. The key problem is to simultaneously infer the exact object locations in the training images and train the object detectors, given only the training images with weak image-level labels. Intuitively, by simulating the selective attention mechanism of human visual system, saliency detection technique can select attractive objects in scenes and thus is a potential way to provide useful priors for WOD. However, the way to adopt saliency detection in WOD is not trivial since the detected saliency region might be possibly highly ambiguous in complex cases. To this end, this work first comprehensively analyzes the challenges in applying saliency detection to WOD. Then, we make one of the earliest efforts to bridge saliency detection to WOD via the self-paced curriculum learning, which can guide the learning procedure to gradually achieve faithful knowledge of multi-class objects from easy to hard. The experimental results demonstrate that the proposed approach can successfully bridge saliency detection and WOD tasks and achieve the state-of-the-art object detection results under the weak supervision.
Dingwen Zhang received the B.E. and Ph.D degrees from the Northwestern Polytechnical University, Xi’an, China, in 2012 and 2018, respectively. He is currently an associate professor in Xidian University. His research interests include computer vision and multimedia processing, especially on saliency detection, co-saliency detection, and weakly supervised learning.