1,047,069 research outputs found
Towards Open World Recognition
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
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
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
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
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
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4th Workshop on human activity sensing corpus and applications: towards open-ended context awareness
Current motion sensors in wearable devices are primarily used for simple orientation and motion sensing. They provide however signals related to more complex and subtle human behaviours which will enable next-generation human-oriented computing in scenarios of high societal value. This requires large scale human activity corpuses and improved methods to recognise activities and their context. This workshop deals with the challenges of designing reproducible experimental setups, running large-scale dataset collection campaigns, designing robust activity and context recognition methods and evaluating systems in the real world. As a special topic, we wish to reflect on the challenges and approaches to recognise activities outside of a pre-defined set to achieve an open-ended activity and context awareness. Following the success of previous years, this workshop is the place to share experiences on human activity corpus and their applications and to discuss the future of activity sensing, in particular towards open-ended contextual intelligence
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