How can we teach a computer to recognize 10,000 different actions? Deep learning has evolved from supervised and unsupervised to self-supervised approaches. In this paper, we present a new contrastive learning-based framework for decision tree-based classification of actions, including human-human interactions (HHI) and human-object interactions (HOI). The key idea is to translate the original multi-class action recognition into a series of binary classification tasks on a pre-constructed decision tree. Under the new framework of contrastive learning, we present the design of an interaction adjacent matrix (IAM) with skeleton graphs as the backbone for modeling various action-related attributes such as periodicity and symmetry. Through the construction of various pretext tasks, we obtain a series of binary classification nodes on the decision tree that can be combined to support higher-level recognition tasks. Experimental justification for the potential of our approach in real-world applications ranges from interaction recognition to symmetry detection. In particular, we have demonstrated the promising performance of video-based autism spectrum disorder (ASD) diagnosis on the CalTech interview video database.

Social media such as Instagram and Twitter have become important platforms for marketing and selling illicit drugs. Detection of online illicit drug trafficking has become critical to combat the online trade of illicit drugs. However, the legal status often varies spatially and temporally; even for the same drug, federal and state legislation can have different regulations about its legality. Meanwhile, more drug trafficking events are disguised as a novel form of advertising - commenting leading to information heterogeneity. Accordingly, accurate detection of illicit drug trafficking events (IDTEs) from social media has become even more challenging. In this work, we conduct the first systematic study on fine-grained detection of IDTEs on Instagram. We propose to take a deep multimodal multilabel learning (DMML) approach to detect IDTEs and demonstrate its effectiveness on a newly constructed dataset called multimodal IDTE (MM-IDTE). Specifically, our model takes text and image data as the input and combines multimodal information to predict multiple labels of illicit drugs. Inspired by the success of BERT, we have developed a self-supervised multimodal bidirectional transformer by jointly fine-tuning pretrained text and image encoders. We have constructed a large-scale dataset MM-IDTE with manually annotated multiple drug labels to support fine-grained detection of illicit drugs. Extensive experimental results on the MM-IDTE dataset show that the proposed DMML methodology can accurately detect IDTEs even in the presence of special characters and style changes attempting to evade detection.

We have witnessed rapid advances in both face presentation attack models and presentation attack detection (PAD) in recent years. When compared with widely studied 2D face presentation attacks, 3D face spoofing attacks are more challenging because face recognition systems are more easily confused by the 3D characteristics of materials similar to real faces. In this work, we tackle the problem of detecting these realistic 3D face presentation attacks and propose a novel anti-spoofing method from the perspective of fine-grained classification. Our method, based on factorized bilinear coding of multiple color channels (namely MC_FBC), targets at learning subtle fine-grained differences between real and fake images. By extracting discriminative and fusing complementary information from RGB and YCbCr spaces, we have developed a principled solution to 3D face spoofing detection. A large-scale wax figure face database (WFFD) with both images and videos has also been collected as super realistic attacks to facilitate the study of 3D face presentation attack detection. Extensive experimental results show that our proposed method achieves the state-of-the-art performance on both our own WFFD and other face spoofing databases under various intra-database and inter-database testing scenarios.

Illicit drug trafficking via social media sites such as Instagram have become a severe problem, thus drawing a great deal of attention from law enforcement and public health agencies. How to identify illicit drug dealers from social media data has remained a technical challenge for the following reasons. On the one hand, the available data are limited because of privacy concerns with crawling social media sites; on the other hand, the diversity of drug dealing patterns makes it difficult to reliably distinguish drug dealers from common drug users. Unlike existing methods that focus on posting-based detection, we propose to tackle the problem of illicit drug dealer identification by constructing a large-scale multimodal dataset named Identifying Drug Dealers on Instagram (IDDIG). Nearly 4,000 user accounts, of which more than 1,400 are drug dealers, have been collected from Instagram with multiple data sources including post comments, post images, homepage bio, and homepage images. We then design a quadruple-based multimodal fusion method to combine the multiple data sources associated with each user account for drug dealer identification. Experimental results on the constructed IDDIG dataset demonstrate the effectiveness of the proposed method in identifying drug dealers (almost 95% accuracy). Moreover, we have developed a hashtag-based community detection technique for discovering evolving patterns, especially those related to geography and drug types.