Person Re-identification: Past, Present and Future

Person re-identification (re-ID) has become increasingly popular in the community due to its application and research significance. It aims at spotting a person of interest in other cameras. In the early days, hand-crafted algorithms and small-scale evaluation were predominantly reported. Recent years have witnessed the emergence of large-scale datasets and deep learning systems which make use of large data volumes. Considering different tasks, we classify most current re-ID methods into two classes, i.e., image-based and video-based; in both tasks, hand-crafted and deep learning systems will be reviewed. Moreover, two new re-ID tasks which are much closer to real-world applications are described and discussed, i.e., end-to-end re-ID and fast re-ID in very large galleries. This paper: 1) introduces the history of person re-ID and its relationship with image classification and instance retrieval; 2) surveys a broad selection of the hand-crafted systems and the large-scale methods in both image- and video-based re-ID; 3) describes critical future directions in end-to-end re-ID and fast retrieval in large galleries; and 4) finally briefs some important yet under-developed issues

Viral video style: A closer look at viral videos on YouTube

Viral videos that gain popularity through the process of Internet sharing are having a profound impact on society. Existing studies on viral videos have only been on small or confidential datasets. We collect by far the largest open benchmark for viral video study called CMU Viral Video Dataset, and share it with researchers from both academia and industry. Having verified existing observations on the dataset, we discover some interesting characteristics of viral videos. Based on our analysis, in the second half of the paper, we propose a model to forecast the future peak day of viral videos. The application of our work is not only important for advertising agencies to plan advertising campaigns and estimate costs, but also for companies to be able to quickly respond to rivals in viral marketing campaigns. The proposed method is unique in that it is the first attempt to incorporate video metadata into the peak day prediction. The empirical results demonstrate that the proposed method outperforms the state-of-the-art methods, with statistically significant differences. Copyright 2014 ACM

Exploring semantic inter-class relationships (SIR) for zero-shot action recognition

© Copyright 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Automatically recognizing a large number of action categories from videos is of significant importance for video understanding. Most existing works focused on the design of more discriminative feature representation, and have achieved promising results when the positive samples are enough. However, very limited efforts were spent on recognizing a novel action without any positive exemplars, which is often the case in the real settings due to the large amount of action classes and the users' queries dramatic variations. To address this issue, we propose to perform action recognition when no positive exemplars of that class are provided, which is often known as the zero-shot learning. Different from other zero-shot learning approaches, which exploit attributes as the intermediate layer for the knowledge transfer, our main contribution is SIR, which directly leverages the semantic inter-class relationships between the known and unknown actions followed by label transfer learning. The inter-class semantic relationships are automatically measured by continuous word vectors, which learned by the skip-gram model using the large-scale text corpus. Extensive experiments on the UCF101 dataset validate the superiority of our method over fully-supervised approaches using few positive exemplars

Dynamic concept composition for zero-example event detection

© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. In this paper, we focus on automatically detecting events in unconstrained videos without the use of any visual training exemplars. In principle, zero-shot learning makes it possible to train an event detection model based on the assumption that events (e.g. birthday party) can be described by multiple mid-level semantic concepts (e.g. "blowing candle", "birthday cake"). Towards this goal, we first pre-Train a bundle of concept classifiers using data from other sources. Then we evaluate the semantic correlation of each concept w.r.t. the event of interest and pick up the relevant concept classifiers, which are applied on all test videos to get multiple prediction score vectors. While most existing systems combine the predictions of the concept classifiers with fixed weights, we propose to learn the optimal weights of the concept classifiers for each testing video by exploring a set of online available videos with freeform text descriptions of their content. To validate the effectiveness of the proposed approach, we have conducted extensive experiments on the latest TRECVID MEDTest 2014, MEDTest 2013 and CCV dataset. The experimental results confirm the superiority of the proposed approach

Expression of survivin detected by immunohistochemistry in the cytoplasm and in the nucleus is associated with prognosis of leiomyosarcoma and synovial sarcoma patients

<p>Abstract</p> <p>Background</p> <p>Survivin, a member of the inhibitor of apoptosis-protein family suppresses apoptosis and regulates cell division. It is strongly overexpressed in the vast majority of cancers. We were interested if survivin detected by immunohistochemistry has prognostic relevance especially for patients of the two soft tissue sarcoma entities leiomyosarcoma and synovial sarcoma.</p> <p>Methods</p> <p>Tumors of leiomyosarcoma (n = 24) and synovial sarcoma patients (n = 26) were investigated for their expression of survivin by immunohistochemistry. Survivin expression was assessed in the cytoplasm and the nucleus of tumor cells using an immunoreactive scoring system (IRS).</p> <p>Results</p> <p>We detected a survivin expression (IRS > 2) in the cytoplasm of 20 leiomyosarcomas and 22 synovial sarcomas and in the nucleus of 12 leiomyosarcomas and 9 synovial sarcomas, respectively. There was no significant difference between leiomyosarcoma and synovial sarcoma samples in their cytoplasmic or nuclear expression of survivin. Next, all sarcoma patients were separated in four groups according to their survivin expression in the cytoplasm and in the nucleus: group 1: negative (IRS 0 to 2); group 2: weak (IRS 3 to 4); group 3: moderate (IRS 6 to 8); group 4: strong (IRS 9 to 12). In a multivariate Cox's regression hazard analysis survivin expression detected in the cytoplasm or in the nucleus was significantly associated with overall survival of patients in group 3 (RR = 5.7; P = 0.004 and RR = 5.7; P = 0.022, respectively) compared to group 2 (reference). Patients whose tumors showed both a moderate/strong expression of survivin in the cytoplasm and a moderate expression of survivin in the nucleus (in both compartments IRS ≥ 6) possessed a 24.8-fold increased risk of tumor-related death (P = 0.003) compared to patients with a weak expression of survivin both in the cytoplasm and in the nucleus.</p> <p>Conclusion</p> <p>Survivin protein expression in the cytoplasma and in the nucleus detected by immunohistochemistry is significantly associated with prognosis of leiomyosarcoma and synovial sarcoma patients.</p

Preclinical discovery of apixaban, a direct and orally bioavailable factor Xa inhibitor

Apixaban (BMS-562247; 1-(4-methoxyphenyl)-7-oxo-6-(4-(2-oxopiperidin-1-yl)phenyl)-4,5,6,7-tetrahydro-1H-pyrazolo[3,4-c]pyridine-3-carboxamide), a direct inhibitor of activated factor X (FXa), is in development for the prevention and treatment of various thromboembolic diseases. With an inhibitory constant of 0.08 nM for human FXa, apixaban has greater than 30,000-fold selectivity for FXa over other human coagulation proteases. It produces a rapid onset of inhibition of FXa with association rate constant of 20 μM−1/s approximately and inhibits free as well as prothrombinase- and clot-bound FXa activity in vitro. Apixaban also inhibits FXa from rabbits, rats and dogs, an activity which parallels its antithrombotic potency in these species. Although apixaban has no direct effects on platelet aggregation, it indirectly inhibits this process by reducing thrombin generation. Pre-clinical studies of apixaban in animal models have demonstrated dose-dependent antithrombotic efficacy at doses that preserved hemostasis. Apixaban improves pre-clinical antithrombotic activity, without excessive increases in bleeding times, when added on top of aspirin or aspirin plus clopidogrel at their clinically relevant doses. Apixaban has good bioavailability, low clearance and a small volume of distribution in animals and humans, and a low potential for drug–drug interactions. Elimination pathways for apixaban include renal excretion, metabolism and biliary/intestinal excretion. Although a sulfate conjugate of Ο-demethyl apixaban (O-demethyl apixaban sulfate) has been identified as the major circulating metabolite of apixaban in humans, it is inactive against human FXa. Together, these non-clinical findings have established the favorable pharmacological profile of apixaban, and support the potential use of apixaban in the clinic for the prevention and treatment of various thromboembolic diseases