Discriminative Feature Learning for Unsupervised Video Summarization

In this paper, we address the problem of unsupervised video summarization that automatically extracts key-shots from an input video. Specifically, we tackle two critical issues based on our empirical observations: (i) Ineffective feature learning due to flat distributions of output importance scores for each frame, and (ii) training difficulty when dealing with long-length video inputs. To alleviate the first problem, we propose a simple yet effective regularization loss term called variance loss. The proposed variance loss allows a network to predict output scores for each frame with high discrepancy which enables effective feature learning and significantly improves model performance. For the second problem, we design a novel two-stream network named Chunk and Stride Network (CSNet) that utilizes local (chunk) and global (stride) temporal view on the video features. Our CSNet gives better summarization results for long-length videos compared to the existing methods. In addition, we introduce an attention mechanism to handle the dynamic information in videos. We demonstrate the effectiveness of the proposed methods by conducting extensive ablation studies and show that our final model achieves new state-of-the-art results on two benchmark datasets.Comment: Accepted to AAAI 2019 !!

Hide-and-Tell: Learning to Bridge Photo Streams for Visual Storytelling

Visual storytelling is a task of creating a short story based on photo streams. Unlike existing visual captioning, storytelling aims to contain not only factual descriptions, but also human-like narration and semantics. However, the VIST dataset consists only of a small, fixed number of photos per story. Therefore, the main challenge of visual storytelling is to fill in the visual gap between photos with narrative and imaginative story. In this paper, we propose to explicitly learn to imagine a storyline that bridges the visual gap. During training, one or more photos is randomly omitted from the input stack, and we train the network to produce a full plausible story even with missing photo(s). Furthermore, we propose for visual storytelling a hide-and-tell model, which is designed to learn non-local relations across the photo streams and to refine and improve conventional RNN-based models. In experiments, we show that our scheme of hide-and-tell, and the network design are indeed effective at storytelling, and that our model outperforms previous state-of-the-art methods in automatic metrics. Finally, we qualitatively show the learned ability to interpolate storyline over visual gaps.Comment: AAAI 2020 pape

Observation of tW production in the single-lepton channel in pp collisions at root s=13 TeV

A measurement of the cross section of the associated production of a single top quark and a W boson in final states with a muon or electron and jets in proton-proton collisions at root s = 13 TeV is presented. The data correspond to an integrated luminosity of 36 fb(-1) collected with the CMS detector at the CERN LHC in 2016. A boosted decision tree is used to separate the tW signal from the dominant t (t) over bar background, whilst the subleading W+jets and multijet backgrounds are constrained using data-based estimates. This result is the first observation of the tW process in final states containing a muon or electron and jets, with a significance exceeding 5 standard deviations. The cross section is determined to be 89 +/- 4 (stat) +/- 12 (syst) pb, consistent with the standard model.Peer reviewe

Energy Metabolism Changes and Dysregulated Lipid Metabolism in Postmenopausal Women

Aging women experience hormonal changes, such as decreased estrogen and increased circulating androgen, due to natural or surgical menopause. These hormonal changes make postmenopausal women vulnerable to body composition changes, muscle loss, and abdominal obesity; with a sedentary lifestyle, these changes affect overall energy expenditure and basal metabolic rate. In addition, fat redistribution due to hormonal changes leads to changes in body shape. In particular, increased bone marrow-derived adipocytes due to estrogen loss contribute to increased visceral fat in postmenopausal women. Enhanced visceral fat lipolysis by adipose tissue lipoprotein lipase triggers the production of excessive free fatty acids, causing insulin resistance and metabolic diseases. Because genes involved in β-oxidation are downregulated by estradiol loss, excess free fatty acids produced by lipolysis of visceral fat cannot be used appropriately as an energy source through β-oxidation. Moreover, aged women show increased adipogenesis due to upregulated expression of genes related to fat accumulation. As a result, the catabolism of ATP production associated with β-oxidation decreases, and metabolism associated with lipid synthesis increases. This review describes the changes in energy metabolism and lipid metabolic abnormalities that are the background of weight gain in postmenopausal women

A Balanced Term-Weighting Scheme for Effective Document Matching

A new weighting scheme for vector space model is presented to improve retrieval performance for an information retrieval system. In addition, a dimension compression method is introduced to reduce the computational cost of the weighting approach. The main idea of this approach is to consider not only occurrence terms but also absent terms in finding similarity patterns among document and query vectors. With a basic information retrieval development system which we are now developing, we evaluate the effect of the balanced weighting scheme and compare it with various combinations of weighting schemes in terms of retrieval performance. The experimental results show that the proposed scheme produces similar recall-precision results to the cosine measure, but more importantly enhances retrieval effectiveness. Since the scheme is based on the cosine measure, it is certain that it has insensitivity to weight variance. The results have convincingly illustrated that the new approach is effective and applicable

Counterfactual Mix-Up for Visual Question Answering

Counterfactuals have been shown to be a powerful method in Visual Question Answering in the alleviation of Visual Question Answering’s unimodal bias. However, existing counterfactual methods tend to generate samples that are not diverse or require auxiliary models to synthesize additional data. In this regard, we propose a more diverse and simple counterfactual sample synthesis method called Counterfactual Mix-Up (CoMiU), which generates counterfactual image features and questions through batch-wise swapping in local object- and word-level. This method efficiently facilitates the generation of more abundant and diverse counterfactual samples, which help improve the robustness of Visual Question Answering models. Moreover, with the creation of diverse counterfactual samples, we introduce two more robust and stable contrastive loss functions, namely Batch-Contrastive loss and Answer-Contrastive loss. We test our method on various challenging Visual Question Answering robustness testing setups to show the advantages of the proposed method compared with the current state-of-the-art methods

Optimal Consecutive-k-out-of-(2k+1): G Cycle

We present a complete proof for the invariant optimal assignment for consecutive-k-out-of-(2k+1): G Cycle, which was proposed by Zuo and Kao in 1990 with an incomplete proof, pointed out recently by Jalali, Hawkes, Cui, and Hwang