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Yan-Ying Chen

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4 papers

2 author rows

AAAI Conference 2026 Conference Paper

ShaLa: Multimodal Shared Latent Generative Modelling

Jiali Cui
Yan-Ying Chen
Yanxia Zhang
Matthew Klenk

This paper presents a novel generative framework for learning shared latent representations across multimodal data. Many advanced multimodal methods focus on capturing all combinations of modality-specific details across inputs, which can inadvertently obscure the high-level semantic concepts that are shared across modalities. Notably, Multimodal VAEs with low-dimensional latent variables are designed to capture shared representations, enabling various tasks such as joint multimodal synthesis and cross-modal inference. However, multimodal VAEs often struggle to design expressive joint variational posteriors and suffer from low-quality synthesis. In this work, ShaLa addresses these challenges by integrating a novel architectural inference model and a second-stage expressive diffusion prior, which not only facilitates effective inference of shared latent representation but also significantly improves the quality of downstream multimodal synthesis. We validate ShaLa extensively across multiple benchmarks, demonstrating superior coherence and synthesis quality compared to state-of-the-art multimodal VAEs. Furthermore, ShaLa scales to many more modalities while prior multimodal VAEs have fallen short in capturing the increasing complexity of the shared latent space.

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ICRA Conference 2018 Conference Paper

ContextualNet: Exploiting Contextual Information Using LSTMs to Improve Image-Based Localization

Mitesh Patel
Brendan Emery
Yan-Ying Chen

Convolutional Neural Networks (CNN) have successfully been utilized for localization using a single monocular image [1]. Most of the work to date has either focused on reducing the dimensionality of data for better learning of parameters during training or on developing different variations of CNN models to improve pose estimation. Many of the best performing works solely consider the content in a single image, while the context from historical images is ignored. In this paper, we propose a combined CNN-LSTM which is capable of incorporating contextual information from historical images to better estimate the current pose. Experimental results achieved using a dataset collected in an indoor office space improved the overall system results to 0. 8 m & 2. 5° at the third quartile of the cumulative distribution as compared with 1. 5 m & 3. 0° achieved by PoseNet [1]. Furthermore, we demonstrate how the temporal information exploited by the CNN-LSTM model assists in localizing the robot in situations where image content does not have sufficient features.

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AAAI Conference 2016 Conference Paper

Business-Aware Visual Concept Discovery from Social Media for Multimodal Business Venue Recognition

Bor-Chun Chen
Yan-Ying Chen
Francine Chen
Dhiraj Joshi

Image localization is important for marketing and recommendation of local business; however, the level of granularity is still a critical issue. Given a consumer photo and its rough GPS information, we are interested in extracting the ﬁne-grained location information, i. e. business venues, of the image. To this end, we propose a novel framework for business venue recognition. The framework mainly contains three parts. First, business-aware visual concept discovery: we mine a set of concepts that are useful for business venue recognition based on three guidelines including business awareness, visually detectable, and discriminative power. We deﬁne concepts that satisfy all of these three criteria as business-aware visual concept. Second, businessaware concept detection by convolutional neural networks (BA-CNN): we propose a new network conﬁguration that can incorporate semantic signals mined from business reviews for extracting semantic concept features from a query image. Third, multimodal business venue recognition: we extend visually detected concepts to multimodal feature representations that allow a test image to be associated with business reviews and images from social media for business venue recognition. The experiments results show the visual concepts detected by BA-CNN can achieve up to 22. 5% relative improvement for business venue recognition compared to the state-of-the-art convolutional neural network features. Experiments also show that by leveraging multimodal information from social media we can further boost the performance, especially when the database images belonging to each business venue are scarce.

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AAAI Conference 2015 Conference Paper

Visually Interpreting Names as Demographic Attributes by Exploiting Click-Through Data

Yan-Ying Chen
Yin-Hsi Kuo
Chun-Che Wu
Winston Hsu

Name of an identity is strongly influenced by his/her cultural background such as gender and ethnicity, both vital attributes for user profiling, attribute-based retrieval, etc. Typically, the associations between names and attributes (e. g. , people named “Amy” are mostly females) are annotated manually or provided by the census data of governments. We propose to associate a name and its likely demographic attributes by exploiting click-throughs between name queries and images with automatically detected facial attributes. This is the first work attempting to translate an abstract name to demographic attributes in visual-data-driven manner, and it is adaptive to incremental data, more countries and even unseen names (the names out of click-through data) without additional manual labels. In the experiments, the automatic name-attribute associations can help gender inference with competitive accuracy by using manual labeling. It also benefits profiling social media users and keyword-based face image retrieval, especially for contributing 12% relative improvement of accuracy in adapting to unseen names.

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