Matching images and text with multi-modal tensor fusion and re-ranking

A major challenge in matching images and text is that they have intrinsically different data distributions and feature representations. Most existing approaches are based either on embedding or classification, the first one mapping image and text instances into a common embedding space for distance measuring, and the second one regarding...

From Deterministic to Generative: Multimodal Stochastic RNNs for Video Captioning

Video captioning, in essential, is a complex natural process, which is affected by various uncertainties stemming from video content, subjective judgment, and so on. In this paper, we build on the recent progress in using encoder-decoder framework for video captioning and address what we find to be a critical deficiency of the existing...

Unified Binary Generative Adversarial Network for Image Retrieval and Compression

Binary codes have often been deployed to facilitate large-scale retrieval tasks, but not that often for image compression. In this paper, we propose a unified framework, BGAN+, that restricts the input noise variable of generative adversarial networks to be binary and conditioned on the features of each input image, and simultaneously learns...