The rapid development of short-form video platforms has provided new avenues for advertisement recommendation, where advertisements are typically displayed as a thumbnail button at the beginning of a video and expand after a certain period. The audio-visual content of a video usually impacts the emotions and attention of users, which further partially determines user behaviors after the advertisement expands. This impact varies depending on the moment of the expansion. Hence, the task of predicting the expansion moment for each video is essential to improve the quality of advertisement recommendation, which is the focus of this paper. The main challenge of this task lies in the requirement to understand both audio-visual video content and user behaviors. To facilitate our research on this task, we first collect a large-scale dataset containing 18,901 videos with over 1.58 billion real user-watching records as the annotations. We also propose a quantitative metric for the task. To address the challenge of the task as well as predict a proper expansion moment for each video, we develop a prediction framework. Firstly, we utilize pretrained models to extract features, then refine extracted features via intra-modal and inter-modal interactions to enhance the understanding of the audio-visual content. Secondly, we propose an uncertainty-based logit adjustment strategy, which leverages prior preferences of user behaviors to adjust the output logit of the model adaptively, achieving a better prediction of user behaviors. Extensive experiments demonstrate the effectiveness of our proposed framework. We hope this work could be a meaningful step towards better video understanding and advertisement recommendation in short-form video platforms. Code and dataset are available at https://github.com/GeWu-Lab/WCAE.

Audio-Visual Question Answering (AVQA) task aims to answer questions about different visual objects, sounds, and their associations in videos. Such naturally multi-modal videos are composed of rich and complex dynamic audio-visual components, where most of which could be unrelated to the given questions, or even play as interference in answering the content of interest. Oppositely, only focusing on the question-aware audio-visual content could get rid of influence, meanwhile enabling the model to answer more efficiently. In this paper, we propose a Progressive Spatio-Temporal Perception Network (PSTP-Net), which contains three modules that progressively identify key spatio-temporal regions w.r.t. questions. Specifically, a temporal segment selection module is first introduced to select the most relevant audio-visual segments related to the given question. Then, a spatial region selection module is utilized to choose the most relevant regions associated with the question from the selected temporal segments. To further refine the selection of features, anaudio-guided visual attention module is employed to perceive the association between auido and selected spatial regions. Finally, the spatio-temporal features from these modules are integrated for answering the question. Extensive experimental results on the public MUSIC-AVQA and AVQA datasets provide compelling evidence of the effectiveness and efficiency of PSTP-Net.

Dental age estimation is widely used in forensic identification, but the accuracy of traditional methods cannot satisfy the demand for accuracy, especially for age estimation of adults. We introduce a deep learning-based methodology to estimate the age based on collected X-ray images of the teeth. We present a new dental dataset, which contains labeled orthopan-tomograms (OPGs) of 27,957 people, including 16,383 OPGs for females as well as 11,574 OPGs for males. All ages range from 0 to 93-year-old with a median of 27. The accuracy of the age labels is guaranteed by the ID card information. Aiming at the characteristics of the dental data itself, we explore various neural network elements that are effective for age estimation, including proper network depth, convolution kernel size, multi-branch structure, and the feature reusing of early layers. Based on the characteristic exploration, we further search models for dental age estimation by using the popular Neural Architecture Search (NAS) method. Experiment results show that our model achieves a mean absolute error (MAE) of 1.64 years, surpass all existing CNN models. Compared with Inception-v4 with an MAE of 1.70 and 20.46B FLOPs (inputs size 384×384), the FLOPs of our model can be reduced by 2.7 times (7.49B FLOPs). To our best knowledge, this is the first study for age estimation by exploring and searching the DNN model. Our results have surpassed legal medical expert-level performance (with an MAE of more than 2) for age estimation. Our methodology and results in this paper are very meaningful to forensic medicine for aging estimation with panoramic radiograph images.

Pruning can remove redundant parameters and structures of Deep Neural Networks (DNNs) to reduce inference time and memory overhead. As an important component of neural networks, the feature map (FM) has stated to be adopted for network pruning. However, the majority of FM-based pruning methods do not fully investigate effective knowledge in the FM for pruning. In addition, it is challenging to design a robust pruning criterion with a small number of images and achieve parallel pruning due to the variability of FMs. In this paper, we propose Adaptive Knowledge Extraction for Channel Pruning (AKECP), which can compress the network fast and efficiently. In AKECP, we first investigate the characteristics of FMs and extract effective knowledge with an adaptive scheme. Secondly, we formulate the effective knowledge of FMs to measure the importance of corresponding network channels. Thirdly, thanks to the effective knowledge extraction, AKECP can efficiently and simultaneously prune all the layers with extremely few or even one image. Experimental results show that our method can compress various networks on different datasets without introducing additional constraints, and it has advanced the state-of-the-arts. Notably, for ResNet-110 on CIFAR-10, AKECP achieves 59.9% of parameters and 59.8% of FLOPs reduction with negligible accuracy loss. For ResNet-50 on ImageNet, AKECP saves 40.5% of memory footprint and reduces 44.1% of FLOPs with only 0.32% of Top-1 accuracy drop.