Search

In this article, we propose BNAS-v2 to further improve the efficiency of broad neural architecture search (BNAS), which employs a broad convolutional neural network (BCNN) as the search space. In BNAS, the single-path sampling-updating strategy of an overparameterized BCNN leads to terrible unfair training issue, which restricts the efficiency improvement. To mitigate the unfair training issue, we employ a continuous relaxation strategy to optimize all paths of the overparameterized BCNN simultaneously. However, continuous relaxation leads to a performance collapse issue that leads to the unsatisfactory performance of the learned BCNN. For that, we propose the confident learning rate (CLR) and introduce the combination of partial channel connections and edge normalization. Experimental results show that 1) BNAS-v2 delivers state-of-the-art search efficiency on both CIFAR-10 (0.05 GPU days, which is $4\times $ faster than BNAS) and ImageNet (0.19 GPU days) with better or competitive performance; 2) the above two solutions are effectively alleviating the performance collapse issue; and 3) BNAS-v2 achieves powerful generalization ability on multiple transfer tasks, e.g., MNIST, FashionMNIST, NORB, and SVHN. The code is available at https://github.com/zixiangding/BNASv2 . [ABSTRACT FROM AUTHOR]

Spatial–temporal action detection in videos is a challenging problem that has attracted considerable attention in recent years. Most current approaches address action detection as an object detection problem, which utilizes successful object detection frameworks such as Faster R-CNN to operate action detection at every single frame first, and then generates action tubes by linking bounding boxes across the whole video in an offline fashion. However, unlike object detection in static images, temporal context information is vital for action detection in videos. Therefore, we propose an online action detection model that leverages the spatial–temporal context information existing in videos to perform action inference and localization. More specifically, we try to depict the spatial–temporal context pattern of actions via an encoder–decoder model that is based on a convolutional recurrent neural network. The model accepts a video snippet as input and encodes the dynamic information inside the snippet in the forward pass. During the backward pass, the decoder resolves the information for action detection with the current appearance or motion cue at each time stamp. In addition, we devise an incremental action-tube construction algorithm that enables our model to accomplish action prediction ahead of time and performs action detection in an online fashion. To evaluate the performance of our method, we conduct experiments on three popular public datasets UCF-101, UCF-Sports, and J-HMDB-21. The experimental results demonstrate that our method can achieve competitive or superior performance when compared to the state-of-the-art methods. To encourage further research, we release our project on “ https://github.com.hjjpku.OATD.” [ABSTRACT FROM AUTHOR]