Anatomical Therapeutic Chemical (ATC) classification for compounds/drugs plays an important role in drug development and basic research. However, previous methods depend on interactions extracted from STITCH dataset which may make it depend on lab experiments. We present a pilot study to explore the possibility of conducting the ATC prediction solely based on the molecular structures. The motivation is to eliminate the reliance on the costly lab experiments so that the characteristics of a drug can be pre-assessed for better decision-making and effort-saving before the actual development. To this end, we construct a new benchmark consisting of 4545 compounds which is with larger scale than the one used in previous study. A light-weight prediction model is proposed. The model is with better explainability in the sense that it is consists of a straightforward tokenization that extracts and embeds statistically and physicochemically meaningful tokens, and a deep network backed by a set of pyramid kernels to capture multi-resolution chemical structural characteristics. Its efficacy has been validated in the experiments where it outperforms the state-of-the-art methods by 15.53% in accuracy and by 69.66% in terms of efficiency. We make the benchmark dataset, source code and web server open to ease the reproduction of this study.

Immunofixation Electrophoresis (IFE) analysis is of great importance to the diagnosis of Multiple Myeloma, which is among the top-9 cancer killers in the United States, but has rarely been studied in the context of deep learning. Two possible reasons are: 1) the recognition of IFE patterns is dependent on the co-location of bands that forms a binary relation, different from the unary relation (visual features to label) that deep learning is good at modeling; 2) deep classification models may perform with high accuracy for IFE recognition but is not able to provide firm evidence (where the co-location patterns are) for its predictions, rendering difficulty for technicians to validate the results. We propose to address these issues with collocative learning, in which a collocative tensor has been constructed to transform the binary relations into unary relations that are compatible with conventional deep networks, and a location-label-free method that utilizes the Grad-CAM saliency map for evidence backtracking has been proposed for accurate localization. In addition, we have proposed Coached Attention Gates that can regulate the inference of the learning to be more consistent with human logic and thus support the evidence backtracking. The experimental results show that the proposed method has obtained a performance gain over its base model ResNet18 by 741.30% in IoU and also outperformed popular deep networks of DenseNet, CBAM, and Inception-v3.

In this paper, we have proposed a novel method which utilizes the contextual relationship among visual words for reducing the Quantization errors in near-duplicate image retrieval (NDR). Instead of following the track of conventional NDR techniques which usually search new solutions by borrowing ideas from the text domain, we propose to model the problem back to image domain, which results in a more natural way of solution search. The idea of the proposed method is to construct a context graph that encapsulates the contextual relationship within an image and treat the graph as a pseudo-image, so that classical image filters can be adopted to reduce the mismapped visual words which are contextually inconsistent with others.With these contextual noises reduced, the method provides purified inputs to the subsequent processes in NDR, and improves the overall accuracy. More importantly, the purification further increases the sparsity of the image feature vectors, which thus speeds up the conventional methods by 1662% times and makes NDR practical to online applications on merchandize images where the requirement of response time is critical. The way of considering contextual noise reduction in image domain also makes the problem open to all sophisticated filters. Our study shows the classic anisotropic diffusion filter can be employed to address the cross-domain issue, resulting in the superiority of the method to conventional ones in both effectiveness and efficiency.

Near-duplicate retrieval (NDR) in merchandize images is of great importance to a lot of online applications on e-Commerce websites. In those applications where the requirement of response time is critical, however, the conventional techniques developed for a general purpose NDR are limited, because expensive post-processing like spatial verification or hashing is usually employed to compromise the quantization errors among the visual words used for the images. In this paper, we argue that most of the errors are introduced because of the quantization process where the visual words are considered individually, which has ignored the contextual relations among words. We propose a "spelling or phrase correction" like process for NDR, which extends the concept of collocations to visual domain for modeling the contextual relations. Binary quadratic programming is used to enforce the contextual consistency of words selected for an image, so that the errors (typos) are eliminated and the quality of the quantization process is improved. The experimental results show that the proposed method can improve the efficiency of NDR by reducing vocabulary size by 1000% times, and under the scenario of merchandize image NDR, the expensive local interest point feature used in conventional approaches can be replaced by color-moment feature, which reduces the time cost by 9202% while maintaining comparable performance to the state-of-the-art methods.

Conventional active learning approaches for interactive video/image retrieval usually assume the query distribution is unknown, as it is difficult to estimate with only a limited number of labeled instances available. Thus, it is easy to put the system in a dilemma whether to explore the feature space in uncertain areas for a better understanding of the query distribution or to harvest in certain areas for more relevant instances. In this paper, we propose a novel approach called coached active learning that makes the query distribution predictable through training and, therefore, avoids the risk of searching on a completely unknown space. The estimated distribution, which provides a more global view of the feature space, can be used to schedule not only the timing but also the step sizes of the exploration and the exploitation in a principled way. The results of the experiments on a large-scale data set from TRECVID 2005-2009 validate the efficiency and effectiveness of our approach, which demonstrates an encouraging performance when facing domain-shift, outperforms eight conventional active learning methods, and shows superiority to six state-of-the-art interactive video retrieval systems.

Modeling the in-class student social networks is a highly desired goal in educational literature. However, due to the difficulty to collect social data, most of the conventional studies can only be conducted in a qualitative way on a small-scale of dataset obtained through questionnaires or interviews. We propose to solve the problems of data collection, social network construction and analysis with multimedia technology, in the way that we can automatically recognize the positions and identities of the students in classroom and construct the in-class social networks accordingly. With the social networks and the statistics on a large-scale dataset, we have demonstrated that the pedagogical analysis for investigating the co-learning patterns among the students can be conducted in a quantitative way, which provides the statistical clues about why prior studies reach conflicting conclusions on the relation between the students' positions in social networks and their academic performances. The experimental results have validated the effectiveness of the proposed approaches in both technical and pedagogical senses.

Active learning with uncertainty sampling has been popularly employed in implementing interactive video search, due to its promise to reduce labeling efforts. However, since the ultimate goal of interactive search is to find as many relevant shots as possible, the purely explorative learning strategy always places conventional active learning in a dilemma whether to explore uncertain areas for a better understanding of query distribution or to harvest in certain areas for more relevant instances. In this paper, we propose a novel paradigm of active learning, where a coaching process is introduced to guide the leaner by jointly consulting an estimated prior query distribution and a posterior query distribution indicated by current classifier outcomes. To bypass the difficulty of estimating the prior query distribution from a limited number of labeled relevant instances, we propose to estimate the distribution using a set of semantic distributions which are statistically from the same distributions as the labeled relevant instances. With the coaching of both prior and posterior query distributions, the learning can be conducted and scheduled with a global perspective, and thus can explicitly balance the trade-off between exploitation and exploration. The results of the experiments on TRECVID 2005--2009 datasets validate the efficiency and effectiveness of our approach, which outperforms the conventional active learning methods with uncertainty sampling and also shows superiority to several state-of-the art interactive video search systems.