Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark

Most existing works on few-shot object detection (FSOD) focus on a setting where both pre-training and few-shot learning datasets are from a similar domain. However, few-shot algorithms are important in multiple domains; hence evaluation needs to reflect the broad applications. We propose a Multi-dOmain Few-Shot Object Detection (MoFSOD) benchmark consisting of 10 datasets from a wide range of domains to evaluate FSOD algorithms. We comprehensively analyze the impacts of freezing layers, different architectures, and different pre-training datasets on FSOD performance. Our empirical results show several key factors that have not been explored in previous works: 1) contrary to previous belief, on a multi-domain benchmark, fine-tuning (FT) is a strong baseline for FSOD, performing on par or better than the state-of-the-art (SOTA) algorithms; 2) utilizing FT as the baseline allows us to explore multiple architectures, and we found them to have a significant impact on down-stream few-shot tasks, even with similar pre-training performances; 3) by decoupling pre-training and few-shot learning, MoFSOD allows us to explore the impact of different pre-training datasets, and the right choice can boost the performance of the down-stream tasks significantly. Based on these findings, we list possible avenues of investigation for improving FSOD performance and propose two simple modifications to existing algorithms that lead to SOTA performance on the MoFSOD benchmark. The code is available at https://github.com/amazon-research/few-shot-object-detection-benchmark.Comment: Accepted at ECCV 202

The Phylogenetic Analysis Of Genes Encoding Specific Steps In The Glutathione Pathway Within Industrial Beer Brewing Strains: Saccharomyces Cerevisiae, Saccharomyces Pastorinus, And The Spoilage Microbe Brettanomyces

The Glutathione pathway, (GSH) is an antioxidant system in yeast that increases cell viability and contributes to the production of desirable beer flavors during industrial fermentation. Despite its importance, studies using the GSH pathway: GSH1, GSH2, GLR1, and SOD1 genes, to trace the evolutionary history of beer strains are lacking. As a result, the investigator sought to elucidate the phylogenetic relationships between four commonly used industrial beer strains: California Ale, London Ale, Oktoberfest, and Brettanomyces bruxellensis through single-gene sequencing analysis of the GSH pathway. It was hypothesized that the actions of these GSH genes are unique and potentially upregulated in beer brewing yeasts when compared to non-brewing yeasts strains. In order to assess this theory, GSH pathway genes from the experimental industrial strains, were sequenced in order to demonstrate that brewing yeast exhibit identical GSH pathway sequences as an adaptation to their shared brewing environments. Following genome sequencing and phylogenetic analyses, the investigator found that these strains possessed identical GSH pathways as a result of various physiological adaptations and prolonged use within industrial settings. The investigator’s results highlight the evolutionary significance and functionality of GSH pathway genes and demonstrate the essentialness of antioxidant activity in industrial yeast strains. These results also revealed that important strain related associations can be inferred through an analysis of essential metabolic pathways

Kajian Pengaruh Dataset dan Bias Dataset terhadap Performa Akurasi Deteksi Objek

Deteksi objek merupakan kemampuan sistem yang dapat mengenali objek tertentu yang berada dalam suatu gambar atau video. Dalam proses deteksi objek, sistem bisa memberikan hasil yang tidak sesuai atau tidak dapat mendeteksi suatu objek yang disebabkan oleh dataset yang tidak optimal. Penelitian ini bertujuan mengkaji proses pembuatan dataset dan bias yang muncul. Pencarian dan analisis dilakukan terhadap literatur yang berkaitan dengan dataset deteksi objek. Proses pencarian literatur dilakukan pada Google Scholar, Science Direct, dan DSpace Repository dengan memasukkan kata kunci utama “data centric”, “object detection dataset”, dan “dataset bias”. Hasil analisis literatur meliputi dataset dan bias dataset. Pada penelitian sebelumnya terdapat kekurangan seperti belum adanya peningkatan performa sistem deteksi objek melalui pengoptimalan dataset. Dari kajian literatur, pembuatan dataset yang baik dapat dilakukan dengan cara menyesuaikan kondisi pengambilan gambar saat pengumpulan data dan pengujian di lapangan. Selain itu, untuk dapat menambah kemampuan generalisasi sistem dengan cara menambahkan variasi gambar dalam dataset melalui teknik augmentasi. Selanjutnya, dalam proses pembuatan dataset pasti akan selalu ada bias dalam data sehingga mempengaruhi kemampuan deteksi objek. Oleh karena itu, dalam proses pembuatan sistem deteksi objek, data memiliki pengaruh yang cukup besar terhadap performa akurasi deteksi objek

Vol. 62, No. 2, September 28, 2011

•Gabbing with Dean Z: 1Ls & Beyond •Res Gestae Mailbag •Dean Caminker’s Speech Celebrating the Opening of Aikens Commons •OCI SOCSS: The Job Search Survey •SOCSS: Pretty Graphs •Fall-ing All Over Yourself: Oktober Beers •Winter 2011 Grade Curves! •Law Library Pick-Up Lines •A2SO: The New Kids on the Bach? •Crosswor

Vol. 62, No. 2, September 28, 2011

•Gabbing with Dean Z: 1Ls & Beyond •Res Gestae Mailbag •Dean Caminker’s Speech Celebrating the Opening of Aikens Commons •OCI SOCSS: The Job Search Survey •SOCSS: Pretty Graphs •Fall-ing All Over Yourself: Oktober Beers •Winter 2011 Grade Curves! •Law Library Pick-Up Lines •A2SO: The New Kids on the Bach? •Crosswor

Segmenting Markets by Bagged Clustering: Young Chinese Travelers to Western Europe.

Market segmentation is ubiquitous in marketing. Hierarchical and nonhierarchical methods are popular for segmenting tourism markets. These methods are not without controversy. In this study, we use bagged clustering on the push and pull factors of Western Europe to segment potential young Chinese travelers. Bagged clustering overcomes some of the limitations of hierarchical and nonhierarchical methods. A sample of 403 travelers revealed the existence of four clusters of potential visitors. The clusters were subsequently profiled on sociodemographics and travel characteristics. The findings suggest a nascent young Chinese independent travel segment that cannot be distinguished on push factors but can be differentiated on perceptions of the current independent travel infrastructure in Western Europe. Managerial implications are offered on marketing and service provision to the young Chinese outbound travel market

Bittm: A core biterms-based topic model for targeted analysis

While most of the existing topic models perform a full analysis on a set of documents to discover all topics, it is noticed recently that in many situations users are interested in fine-grained topics related to some specific aspects only. As a result, targeted analysis (or focused analysis) has been proposed to address this problem. Given a corpus of documents from a broad area, targeted analysis discovers only topics related with user-interested aspects that are expressed by a set of user-provided query keywords. Existing approaches for targeted analysis suffer from problems such as topic loss and topic suppression because of their inherent assumptions and strategies. Moreover, existing approaches are not designed to address computation efficiency, while targeted analysis is supposed to provide responses to user queries as soon as possible. In this paper, we propose a core BiTerms-based Topic Model (BiTTM). By modelling topics from core biterms that are potentially relevant to the target query, on one hand, BiTTM captures the context information across documents to alleviate the problem of topic loss or suppression; on the other hand, our proposed model enables the efficient modelling of topics related to specific aspects. Our experiments on nine real-world datasets demonstrate BiTTM outperforms existing approaches in terms of both effectiveness and efficiency

Preisbildung im Lebensmitteleinzelhandel

On the supply side, the German retail business is characterized by high consolidation and fierce competition. On the demand side, German consumers are extremely price-sensitive. Against this background, food retail pricing is of major importance for the retailers’ marketing strategy. A key property of retail prices is the high level of price dispersion (VARIAN 1980): Retail prices for a given product vary not only over time but also across retailers. This price dispersion is predominantly caused by promotional prices . HOSKEN AND REIFFEN (2004) determine that 25% to 50% of the annual variation in retail prices is attributable to price promotions. Over time, the usage and importance of price promotions as a strategic marketing instrument has risen substantially, particularly in the German retailing business (HOFFMANN AND LOY 2010). Consequently, a wide range of theoretical models aim to identify the rationales behind offering a product on promotion and to explain the pricing strategies as a whole. As these models result in contradictive hypotheses (BERCK ET AL. 2008), empirical research is essential in identifying the driving forces of particular pricing strategies. However, this type of study is particularly scarce for the German retail market (SCHMEDES 2005). Thus, the central contribution of this dissertation is the empirical assessment of pricing patterns in the German retail business and the identification of the causes triggering the discovered pricing strategies.Der deutsche Lebensmitteleinzelhandel (LEH) ist angebotsseitig durch intensiven Wettbewerb und nachfrageseitig durch preissensitive Konsumenten gekennzeichnet. Folglich spielt die Preissetzung eine wichtige Rolle für das Marketing. Trotzdem oder gerade deshalb weisen die Preise im LEH eine erhebliche Dispersion sowohl innerhalb der Geschäfte als auch zwischen den Geschäften auf. Sonderangebote tragen im besonderen Maße zu diesem Phänomen bei. Eine Vielzahl theoretischer Ansätze zielt auf die Erklärung der Preisdispersion im LEH. Der Kernbeitrag der vorliegenden kumulativen Dissertation besteht in der empirischen Prüfung und Weiterentwicklung dieser Ansätze am Beispiel des deutschen LEH unter Verwendung von Scannerkassendaten. Insbesondere wird in dieser Arbeit aufgezeigt, wie die Markentreue der Konsumenten, die Produktdifferenzierung und die Absatzkanäle die Preissetzung systematisch beeinflussen und somit einen Teil der beobachteten Preisdispersion erklären