1,047,069 research outputs found

    Towards Open World Recognition

    Full text link
    With the of advent rich classification models and high computational power visual recognition systems have found many operational applications. Recognition in the real world poses multiple challenges that are not apparent in controlled lab environments. The datasets are dynamic and novel categories must be continuously detected and then added. At prediction time, a trained system has to deal with myriad unseen categories. Operational systems require minimum down time, even to learn. To handle these operational issues, we present the problem of Open World recognition and formally define it. We prove that thresholding sums of monotonically decreasing functions of distances in linearly transformed feature space can balance "open space risk" and empirical risk. Our theory extends existing algorithms for open world recognition. We present a protocol for evaluation of open world recognition systems. We present the Nearest Non-Outlier (NNO) algorithm which evolves model efficiently, adding object categories incrementally while detecting outliers and managing open space risk. We perform experiments on the ImageNet dataset with 1.2M+ images to validate the effectiveness of our method on large scale visual recognition tasks. NNO consistently yields superior results on open world recognition

    Building Machines That Learn and Think Like People

    Get PDF
    Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

    Barnacle Geese and Sky Burials: Relativism in The Travels of Sir John Mandeville

    Get PDF
    As a medieval travel narrative, The Travels of Sir John Mandeville was immensely popular for everyone from bookworms to world travelers in 14th and 15th century Europe. Given its popularity, and the period in which it was produced, one might expect the fictitious travelogue to display an incredible level of intolerance towards the various peoples and cultures it depicts. However, the Travels frequently surprises modern readers with its message of tolerance towards greater humanity, and its recognition of the universality of human experience as it is mirrored in the lives of people of different ethnic and cultural groups. In order to understand Mandeville’s radical efforts to relate tales of the wider world through a relativistic lens, one must explore strange material, such as tales of geese that grow on trees, as well as the concept of sky burials. Mandeville\u27s account can open our eyes to the cultural sensitivity that was thinkable in the medieval period, and what such sensitivity can teach us today

    Challenges of Zero-Shot Recognition with Vision-Language Models: Granularity and Correctness

    Full text link
    This paper investigates the challenges of applying vision-language models (VLMs) to zero-shot visual recognition tasks in an open-world setting, with a focus on contrastive vision-language models such as CLIP. We first examine the performance of VLMs on concepts of different granularity levels. We propose a way to fairly evaluate the performance discrepancy under two experimental setups and find that VLMs are better at recognizing fine-grained concepts. Furthermore, we find that the similarity scores from VLMs do not strictly reflect the correctness of the textual inputs given visual input. We propose an evaluation protocol to test our hypothesis that the scores can be biased towards more informative descriptions, and the nature of the similarity score between embedding makes it challenging for VLMs to recognize the correctness between similar but wrong descriptions. Our study highlights the challenges of using VLMs in open-world settings and suggests directions for future research to improve their zero-shot capabilities

    Open Access: What Scientists Think? A survey of researcher's attitude towards Open Access

    Get PDF
    The Internet has changed how we conduct and share research, primarily by increasing the global reach of scholarly communication. Today the world of information is divided between two views on costs and business. One group believes that content should be freely accessible for the development of further knowledge. The other group believes that content should be maintained by market value for quality products and incentives to the intellectual content. Open Access (OA) has come from the growing interest of researchers in experimenting with innovative mechanisms to disseminate their research findings. However OA is still far behind what it should be in the country like India. At least the scientific community is still in a dilemma to embrace OA. This is what we find in our survey of researcher's attitude towards OA. There are many reasons ranging from lack of awareness, myths about OA and biasness towards traditional publishing model for prestige & recognition. We approached scientists of different research institutes and universities around Kolkata with different age groups in different ways. Interesting results have come out which clearly identified the major hurdles to adopt OA by scientific community in India. Keywords: Open Access, Scholarly Publications, Journal Publisher, Citation and Impact Factor, Research Impact, Open Access Initiative, Institutional Repositories and Usability Study
    corecore