Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge

This paper presents a state-of-the-art model for visual question answering (VQA), which won the first place in the 2017 VQA Challenge. VQA is a task of significant importance for research in artificial intelligence, given its multimodal nature, clear evaluation protocol, and potential real-world applications. The performance of deep neural networks for VQA is very dependent on choices of architectures and hyperparameters. To help further research in the area, we describe in detail our high-performing, though relatively simple model. Through a massive exploration of architectures and hyperparameters representing more than 3,000 GPU-hours, we identified tips and tricks that lead to its success, namely: sigmoid outputs, soft training targets, image features from bottom-up attention, gated tanh activations, output embeddings initialized using GloVe and Google Images, large mini-batches, and smart shuffling of training data. We provide a detailed analysis of their impact on performance to assist others in making an appropriate selection.Comment: Winner of the 2017 Visual Question Answering (VQA) Challenge at CVP

Magnetic phase diagram of doped CMR manganites

The magnetic phase diagram of the colossal magnetoresistance (CMR) manganites is determined based on the Hamiltonian incorporating the double-exchange (DE) interaction between degenerate Mn $e_g$ orbitals and the antiferromagnetic (AF) superexchange interaction between Mn $t_{2g}$ spins. We have employed the rigorous quantum mechanical formalism and obtained the finite temperature phase diagram which describes well the commonly observed features in CMR manganites. We have also shown that the CE-type AF structure cannot be stabilized at $x$=0.5 in this model.Comment: 2 pages, 1 figure; Transport and Thermal Properties of Advanced Materials(Aug. 2002; Hiroshima, Japan

An intrinsic limit to quantum coherence due to spontaneous symmetry breaking

We investigate the influence of spontaneous symmetry breaking on the decoherence of a many-particle quantum system. This decoherence process is analyzed in an exactly solvable model system that is known to be representative of symmetry broken macroscopic systems in equilibrium. It is shown that spontaneous symmetry breaking imposes a fundamental limit to the time that a system can stay quantum coherent. This universal timescale is $t_{spon} \simeq 2\pi N \hbar / (k_B T)$, given in terms of the number of microscopic degrees of freedom $N$, temperature $T$, and the constants of Planck ($\hbar$) and Boltzmann ($k_B$).Comment: 4 pages, 3 figure

Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments

A robot that can carry out a natural-language instruction has been a dream since before the Jetsons cartoon series imagined a life of leisure mediated by a fleet of attentive robot helpers. It is a dream that remains stubbornly distant. However, recent advances in vision and language methods have made incredible progress in closely related areas. This is significant because a robot interpreting a natural-language navigation instruction on the basis of what it sees is carrying out a vision and language process that is similar to Visual Question Answering. Both tasks can be interpreted as visually grounded sequence-to-sequence translation problems, and many of the same methods are applicable. To enable and encourage the application of vision and language methods to the problem of interpreting visually-grounded navigation instructions, we present the Matterport3D Simulator -- a large-scale reinforcement learning environment based on real imagery. Using this simulator, which can in future support a range of embodied vision and language tasks, we provide the first benchmark dataset for visually-grounded natural language navigation in real buildings -- the Room-to-Room (R2R) dataset.Comment: CVPR 2018 Spotlight presentatio

The Efficacy of Simulation as a Pedagogy in Facilitating Pre-Service Teachers’ Learning About Emotional Self-Regulation and its Relevance to the Teaching Profession

This study was undertaken in response to the imperative of teacher education courses incorporating National Professional Standards for Teachers, in particular Standard 7, which deals with the professional engagement of teachers (AITSL, 2011). It aimed to evaluate the efficacy of simulation and active recall as a learner-centred pedagogy in facilitating pre-service teachers’ learning about their capacity to self-regulate emotionally and its relevance to the profession. A simulated ‘critical incident’ was used in a lecture to guide students (n=106) to analyse and understand their emotional responses to an altercation between the lecturer and a colleague. The evaluation involved both quantitative and qualitative data collection. The study generated six useful insights associated with the efficacy of simulation pedagogy and revealed convincingly that this pedagogy can engage students actively in learning about the importance of emotional self-regulation in relation to their professional role as a teacher