arXiv subjects

Ximeng Sun

Publications and source records attributed to Ximeng Sun.

39 records · Page 3Linked to original sources

arXiv2019

Domain Agnostic Learning with Disentangled Representations

Unsupervised model transfer has the potential to greatly improve the generalizability of deep models to novel domains. Yet the current literature assumes that the separation of target data into distinct domains is known as a priori. In this paper, we propose the task of Domain-Agnostic Learning (DAL): How to transfer knowledge from a labeled source domain to unlabeled data from arbitrary target domains? To tackle this problem, we devise a novel Deep Adversarial Disentangled Autoencoder (DADA) capable of disentangling domain-specific features from class identity. We demonstrate experimentally that when the target domain labels are unknown, DADA leads to state-of-the-art performance on several image classification datasets.

cs.CV↗

arXiv2018

Similarity R-C3D for Few-shot Temporal Activity Detection

Many activities of interest are rare events, with only a few labeled examples available. Therefore models for temporal activity detection which are able to learn from a few examples are desirable. In this paper, we present a conceptually simple and general yet novel framework for few-shot temporal activity detection which detects the start and end time of the few-shot input activities in an untrimmed video. Our model is end-to-end trainable and can benefit from more few-shot examples. At test time, each proposal is assigned the label of the few-shot activity class corresponding to the maximum similarity score. Our Similarity R-C3D method outperforms previous work on three large-scale benchmarks for temporal activity detection (THUMOS14, ActivityNet1.2, and ActivityNet1.3 datasets) in the few-shot setting. Our code will be made available.

cs.CV↗

arXiv2018

A Temporally-Aware Interpolation Network for Video Frame Inpainting

We propose the first deep learning solution to video frame inpainting, a challenging instance of the general video inpainting problem with applications in video editing, manipulation, and forensics. Our task is less ambiguous than frame interpolation and video prediction because we have access to both the temporal context and a partial glimpse of the future, allowing us to better evaluate the quality of a model's predictions objectively. We devise a pipeline composed of two modules: a bidirectional video prediction module, and a temporally-aware frame interpolation module. The prediction module makes two intermediate predictions of the missing frames, one conditioned on the preceding frames and the other conditioned on the following frames, using a shared convolutional LSTM-based encoder-decoder. The interpolation module blends the intermediate predictions to form the final result. Specifically, it utilizes time information and hidden activations from the video prediction module to resolve disagreements between the predictions. Our experiments demonstrate that our approach produces more accurate and qualitatively satisfying results than a state-of-the-art video prediction method and many strong frame inpainting baselines.

cs.CV↗