Multi-Label Zero-Shot Learning with Structured Knowledge Graphs

In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance. Inspired by the way humans utilize semantic knowledge between objects of interests, we propose a framework that incorporates knowledge graphs for describing the relationships between multiple labels. Our model learns an information propagation mechanism from the semantic label space, which can be applied to model the interdependencies between seen and unseen class labels. With such investigation of structured knowledge graphs for visual reasoning, we show that our model can be applied for solving multi-label classification and ML-ZSL tasks. Compared to state-of-the-art approaches, comparable or improved performances can be achieved by our method.Comment: CVPR 201

Learning Deep Latent Spaces for Multi-Label Classification

Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance. We propose a novel deep neural networks (DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this task. Aiming at better relating feature and label domain data for improved classification, we uniquely perform joint feature and label embedding by deriving a deep latent space, followed by the introduction of label-correlation sensitive loss function for recovering the predicted label outputs. Our C2AE is achieved by integrating the DNN architectures of canonical correlation analysis and autoencoder, which allows end-to-end learning and prediction with the ability to exploit label dependency. Moreover, our C2AE can be easily extended to address the learning problem with missing labels. Our experiments on multiple datasets with different scales confirm the effectiveness and robustness of our proposed method, which is shown to perform favorably against state-of-the-art methods for multi-label classification.Comment: published in AAAI-201

Low-rank matrix recovery with structural incoherence for robust face recognition

We address the problem of robust face recognition, in which both training and test image data might be corrupted due to occlusion and disguise. From standard face recog-nition algorithms such as Eigenfaces to recently proposed sparse representation-based classification (SRC) methods, most prior works did not consider possible contamination of data during training, and thus the associated performance might be degraded. Based on the recent success of low-rank matrix recovery, we propose a novel low-rank matrix ap-proximation algorithm with structural incoherence for ro-bust face recognition. Our method not only decomposes raw training data into a set of representative basis with corre-sponding sparse errors for better modeling the face images, we further advocate the structural incoherence between the basis learned from different classes. These basis are en-couraged to be as independent as possible due to the regu-larization on structural incoherence. We show that this pro-vides additional discriminating ability to the original low-rank models for improved performance. Experimental re-sults on public face databases verify the effectiveness and robustness of our method, which is also shown to outper-form state-of-the-art SRC based approaches. 1

The Taiwan ECDFS Near-Infrared Survey: Ultra-deep J and Ks Imaging in the Extended Chandra Deep Field-South

We present ultra-deep J and Ks imaging observations covering a 30' * 30' area of the Extended Chandra Deep Field-South (ECDFS) carried out by our Taiwan ECDFS Near-Infrared Survey (TENIS). The median 5-sigma limiting magnitudes for all detected objects in the ECDFS reach 24.5 and 23.9 mag (AB) for J and Ks, respectively. In the inner 400 arcmin^2 region where the sensitivity is more uniform, objects as faint as 25.6 and 25.0 mag are detected at 5-sigma. So this is by far the deepest J and Ks datasets available for the ECDFS. To combine the TENIS with the Spitzer IRAC data for obtaining better spectral energy distributions of high-redshift objects, we developed a novel deconvolution technique (IRACLEAN) to accurately estimate the IRAC fluxes. IRACLEAN can minimize the effect of blending in the IRAC images caused by the large point-spread functions and reduce the confusion noise. We applied IRACLEAN to the images from the Spitzer IRAC/MUSYC Public Legacy in the ECDFS survey (SIMPLE) and generated a J+Ks selected multi-wavelength catalog including the photometry of both the TENIS near-infrared and the SIMPLE IRAC data. We publicly release the data products derived from this work, including the J and Ks images and the J+Ks selected multiwavelength catalog.Comment: 25 pages, 25 figures, ApJS in pres