University of Electronic Science and Technology of China

Chongqing University

Star Institute of Intelligent Systems

Dibi (chongqing) Intelligent Technology Research Institute Co., Ltd.

Chongqing University, Star Institute of Intelligent Systems, Db (chongqing) Intelligent Technology Research Institute Co., Ltd.

Shichun Wang

Yongduan Song

Rui Li

Feng Yang

Jian Zhang

Zhengtao Yu

Siyu Li

Shengtao Pan

Yiwen Zhang

Hongyu Xia

Qin Chen

Caishi He

Shihao Pu

The present disclosure discloses an adaptive high-precision compression method and system based on a convolutional neural network model, and belongs to the fields of artificial intelligence, computer vision, and image processing. According to the method of the present disclosure, coarse-grained pruning is performed on a neural network model by using a differential evolution algorithm first, and the coarse-grained space is quickly searched through an entropy importance criterion and an objective function with good guidance to obtain a near-optimal neural network structure. Then fine-grained search space is built on the basis of an optimal individual obtained from the coarse-grained search, and fine-grained pruning is performed on the neural network model by a differential evolution algorithm to obtain a network model with an optimal structure. Finally, the performance of the optimal model is restored by using a multi-teacher multi-step knowledge distillation network to reach the precision of an original model.

Adaptive high-precision compression method and system based on convolutional neural network model

Embodiments of the present disclosure provide a quantitative method and system for attention based on a line-of-sight estimation neural network, which improves the stability and training efficiency of the line-of-sight estimation neural network. A few-sample learning method is applied to training of the line-of-sight estimation neural network, which improves generalization performance of the line-of-sight estimation neural network. A nonlinear division method for small intervals of angles of the line of sight is provided, which reduces an estimation error of the line-of-sight estimation neural network. Eye opening and closing detection is added to avoid the line-of-sight estimation error caused by an eye closing state. A method for solving a landing point of the line of sight is provided, which has high environmental adaptability and can be quickly used in actual deployment.

Quantitative analysis method and system for attention based on line-of-sight estimation neural network

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Chongqing, China

Shichun Wang