Current techniques for explainable AI have been applied with some success to image processing. The recent rise of research in video processing has called for similar work n deconstructing and explaining spatio-temporal models. While many techniques are designed for 2D convolutional models, others are inherently applicable to any input domain. One such body of work, deep Taylor decomposition, propagates relevance from the model output distributively onto its input and thus is not restricted to image processing models. However, by exploiting a simple technique that removes motion information, we show that it is not the case that this technique is effective as-is for representing relevance in non-image tasks. We instead propose a discriminative method that produces a naive representation of both the spatial and temporal relevance of a frame as two separate objects. This new discriminative relevance model exposes relevance in the frame attributed to motion, that was previously ambiguous in the original explanation. We observe the effectiveness of this technique on a range of samples from the UCF-101 action recognition dataset, two of which are demonstrated in this paper.
Work on explainability methods outside of image tasks is still developing. Papers such as @cite_1 use feature visualisation techniques to provide insight into the models they have trained, but to our knowledge @cite_16 is still one of the only instances of an LRP based method applied to a video task. In this work, the difference between frames in relevance is highlighted by flattening the explanation block and plotting the overall relevance, which shows frames at certain points in an activity are more relevant overall. Saliency tubes, as proposed in @cite_9 , adapts the CAM technique of @cite_7 @cite_6 to localise salient motion in video frames. This method is the most similar to ours in that it highlights motion in 3D CNNs.
@cite_7 @cite_9 @cite_1 @cite_6 @cite_16
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A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. We introduce the term video co-saliency to denote the task of extracting the common noticeable, or salient, regions from multiple relevant videos. The proposed video co-saliency approach accounts for both inter-video foreground correspondences and intra-video saliency stimuli to emphasize the salient foreground regions of video frames and, at the same time, disregard irrelevant visual information of the background. Compared with image co-saliency, it is more reliable due to the utilization of temporal information of video sequence. Benefiting from the discriminability of video co-saliency, we present a unified framework for segmenting out the common salient regions of relevant videos, guided by video co-saliency prior. Unlike naive video co-segmentation approaches employing simple color differences and local motion features, the presented video co-saliency provides a more powerful indicator for the common salient regions, thus conducting video co-segmentation efficiently. Extensive experiments show that the proposed method successfully infers video co-saliency and extracts the common salient regions, outperforming the state-of-the-art methods. With the tremendous amount of research publications, recommending relevant papers to researchers to fulfill their information need becomes a significant problem. The major challenge to be tackled by our work is that given a target paper, how to effectively recommend a set of relevant papers from an existing citation network. In this paper, we propose a novel method to address the problem by incorporating various citation relations for a proper set of papers, which are more relevant but with a very limited size. The proposed method has two unique properties. Firstly, a metric called Local Relation Strength is defined to measure the dependency between cited and citing papers. Secondly, a model called Global Relation Strength is proposed to capture the relevance between two papers in the whole citation graph. We evaluate our proposed model on a real-world publication dataset and conduct an extensive comparison with the state-of-the-art baseline methods. The experimental results demonstrate that our method can have a promising improvement over the state-of-the-art techniques. In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. Layer-wise relevance propagation (LRP) is a recently proposed technique for explaining predictions of complex non-linear classifiers in terms of input variables. In this paper, we apply LRP for the first time to natural language processing (NLP). More precisely, we use it to explain the predictions of a convolutional neural network (CNN) trained on a topic categorization task. Our analysis highlights which words are relevant for a specific prediction of the CNN. We compare our technique to standard sensitivity analysis, both qualitatively and quantitatively, using a word deleting perturbation experiment, a PCA analysis, and various visualizations. All experiments validate the suitability of LRP for explaining the CNN predictions, which is also in line with results reported in recent image classification studies.