29 citations · 123 across the 37 of their papers we have counts for
7 papers · 1 filter
Character-Centric Story Visualization via Visual Planning and Token Alignment
Hong Chen, Rujun Han, Te-Lin Wu +2
Story visualization advances the traditional text-to-image generation by enabling multiple image generation based on a complete story. This task requires machines to 1) understand…
StoryER: Automatic Story Evaluation via Ranking, Rating and Reasoning
Hong Chen, Duc Minh Vo, Hiroya Takamura +2
Existing automatic story evaluation methods place a premium on story lexical level coherence, deviating from human preference. We go beyond this limitation by considering a novel \…
Towards Parameter-Efficient Integration of Pre-Trained Language Models In Temporal Video Grounding
Erica K. Shimomoto, Edison Marrese-Taylor, Hiroya Takamura +3
This paper explores the task of Temporal Video Grounding (TVG) where, given an untrimmed video and a natural language sentence query, the goal is to recognize and determine tempora…
Pixel to Binary Embedding Towards Robustness for CNNs
Ikki Kishida, Hideki Nakayama
There are several problems with the robustness of Convolutional Neural Networks (CNNs). For example, the prediction of CNNs can be changed by adding a small magnitude of noise to a…
OSSGAN: Open-Set Semi-Supervised Image Generation
Kai Katsumata, Duc Minh Vo, Hideki Nakayama
We introduce a challenging training scheme of conditional GANs, called open-set semi-supervised image generation, where the training dataset consists of two parts: (i) labeled data…
NOC-REK: Novel Object Captioning with Retrieved Vocabulary from External Knowledge
Duc Minh Vo, Hong Chen, Akihiro Sugimoto +1
Novel object captioning aims at describing objects absent from training data, with the key ingredient being the provision of object vocabulary to the model. Although existing metho…
PPCD-GAN: Progressive Pruning and Class-Aware Distillation for Large-Scale Conditional GANs Compression
Duc Minh Vo, Akihiro Sugimoto, Hideki Nakayama
We push forward neural network compression research by exploiting a novel challenging task of large-scale conditional generative adversarial networks (GANs) compression. To this en…