Learning Robust Object Recognition Using Composed Scenes from Generative Models

Recurrent feedback connections in the mammalian visual system have been hypothesized to play a role in synthesizing input in the theoretical framework of analysis by synthesis. The comparison of internally synthesized representation with that of the input provides a validation mechanism during perceptual inference and learning. Inspired by these ideas, we proposed that the synthesis machinery can compose new, unobserved images by imagination to train the network itself so as to increase the robustness of the system in novel scenarios. As a proof of concept, we investigated whether images composed by imagination could help an object recognition system to deal with occlusion, which is challenging for the current state-of-the-art deep convolutional neural networks. We fine-tuned a network on images containing objects in various occlusion scenarios, that are imagined or self-generated through a deep generator network. Trained on imagined occluded scenarios under the object persistence constraint, our network discovered more subtle and localized image features that were neglected by the original network for object classification, obtaining better separability of different object classes in the feature space. This leads to significant improvement of object recognition under occlusion for our network relative to the original network trained only on un-occluded images. In addition to providing practical benefits in object recognition under occlusion, this work demonstrates the use of self-generated composition of visual scenes through the synthesis loop, combined with the object persistence constraint, can provide opportunities for neural networks to discover new relevant patterns in the data, and become more flexible in dealing with novel situations.Comment: Accepted by 14th Conference on Computer and Robot Visio

Isolation And Establishment Of A New Embryonic-Like Stem Cell Line From Zebrafish

Embryonic stem (ES) cells established from various fish species using feederfree method had been reported in many cases. However, zebrafish ES-like cell lines were currently limited and required the addition of fish embryo extract (FEE) in medium formulation. FEE was not easily available to laboratories that lack of aquatic housing system. Sel tunjang embrionik (ES) yang dihasilkan daripada pelbagai spesies ikan dengan menggunakan kaedah tanpa sel penyuap telah dilaporkan dalam banyak kes. Walau bagaimanapun, titisan sel tunjang embrionik ikan zebra yang dihasilkan masih terhad dan memerlukan penambahan ekstrak embrio ikan (FEE) dalam formulasi media. FEE tidak mudah didapati oleh makmal-makmal yang kekurangan sistem perumahan akuatik

Predictive Encoding of Contextual Relationships for Perceptual Inference, Interpolation and Prediction

We propose a new neurally-inspired model that can learn to encode the global relationship context of visual events across time and space and to use the contextual information to modulate the analysis by synthesis process in a predictive coding framework. The model learns latent contextual representations by maximizing the predictability of visual events based on local and global contextual information through both top-down and bottom-up processes. In contrast to standard predictive coding models, the prediction error in this model is used to update the contextual representation but does not alter the feedforward input for the next layer, and is thus more consistent with neurophysiological observations. We establish the computational feasibility of this model by demonstrating its ability in several aspects. We show that our model can outperform state-of-art performances of gated Boltzmann machines (GBM) in estimation of contextual information. Our model can also interpolate missing events or predict future events in image sequences while simultaneously estimating contextual information. We show it achieves state-of-art performances in terms of prediction accuracy in a variety of tasks and possesses the ability to interpolate missing frames, a function that is lacking in GBM

Learning to Associate Words and Images Using a Large-scale Graph

We develop an approach for unsupervised learning of associations between co-occurring perceptual events using a large graph. We applied this approach to successfully solve the image captcha of China's railroad system. The approach is based on the principle of suspicious coincidence. In this particular problem, a user is presented with a deformed picture of a Chinese phrase and eight low-resolution images. They must quickly select the relevant images in order to purchase their train tickets. This problem presents several challenges: (1) the teaching labels for both the Chinese phrases and the images were not available for supervised learning, (2) no pre-trained deep convolutional neural networks are available for recognizing these Chinese phrases or the presented images, and (3) each captcha must be solved within a few seconds. We collected 2.6 million captchas, with 2.6 million deformed Chinese phrases and over 21 million images. From these data, we constructed an association graph, composed of over 6 million vertices, and linked these vertices based on co-occurrence information and feature similarity between pairs of images. We then trained a deep convolutional neural network to learn a projection of the Chinese phrases onto a 230-dimensional latent space. Using label propagation, we computed the likelihood of each of the eight images conditioned on the latent space projection of the deformed phrase for each captcha. The resulting system solved captchas with 77% accuracy in 2 seconds on average. Our work, in answering this practical challenge, illustrates the power of this class of unsupervised association learning techniques, which may be related to the brain's general strategy for associating language stimuli with visual objects on the principle of suspicious coincidence.Comment: 8 pages, 7 figures, 14th Conference on Computer and Robot Vision 201

Constructive Consciousness of Gen-pro: Transforming Political Engagement with a Proactive Behavior, a Progressive Attitude, and a Professional Mindset

Studies on young people’s political engagement commonly fall along the binary of engagement or disengagement. Young people’s political disengagement is typically captured by declining membership in political parties, low voter turnout, and political apathy. The engagement paradigm maintains that young people are increasingly turning to the digital space to engage politically. Though the representation of young people’s disengagement in politics may seem clear, how today’s young people understand politics, political engagement, and what meaningful political engagement means to them continue to be contested. Specifically in recent years, East Asian and Southeast Asian young people’s relationship with politics is experiencing significant transformation. Young people in these regions are increasingly at the forefront seeking for political changes, standing up to authoritarianism, and demanding accountability from their leaders. They are exhibiting attitudes and behaviors that depart from the Asian Values concept that demands obedience to authority and political consensus over confrontation. Young people from Hong Kong, Malaysia, and Taiwan are ideal research participants considering the deep influence of the Asian Values concept in these societies. This study uses online focus group interview to gain a deeper understanding of young people’s attitudes towards politics, political engagement, and digital engagement, how young people perceive the challenges to their political engagement, and what being politically engaged truly means to them. To understand if there is a difference between how young people and the older generation perceive politics and political engagement, this study recruits young people, non-Millennials, and non-Gen-Z participants for an online survey. The interviews reveal that while young people from different societies perceive politics differently, they largely associate political engagement with digital engagement. They share similar challenges to engaging in politics – institution-, personal-, and society-related challenges. The online survey uncovers an interesting finding. Not only do young people and the older generation have similar perceptions of politics, but they also share similar perceptions of political engagement. This study proposes two policy recommendations to better include young people in politics. Today’s young people represent a generation ready for opportunities. We must recognize them as agents of change, capable of making meaningful contributions

The Nature of Illusory Contour Computation

AbstractNeural correlates of illusory contour perception have been found in both the early and the higher visual areas. But the locus and the mechanism for its computation remain elusive. Psychophysical evidence provided in this issue of Neuron shows that perceptual contour completion is likely done in the early visual cortex in a cascade manner using horizontal connections