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Multimodal Sequential Generative Models for Semi-Supervised Language Instruction Following
Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo
Agents that can follow language instructions are expected to be useful in a variety of situations such as navigation. However, training neural network-based agents requires numerou…
Realtime Data-Efficient Portrait Stylization Based On Geometric Alignment
Xinrui Wang, Zhuoru Li, Xiao Zhou +2
Portrait Stylization aims to imbue portrait photos with vivid artistic effects drawn from style examples. Despite the availability of enormous training datasets and large network w…
A System for Morphology-Task Generalization via Unified Representation and Behavior Distillation
Hiroki Furuta, Yusuke Iwasawa, Yutaka Matsuo +1
The rise of generalist large-scale models in natural language and vision has made us expect that a massive data-driven approach could achieve broader generalization in other domain…
Langevin Autoencoders for Learning Deep Latent Variable Models
Shohei Taniguchi, Yusuke Iwasawa, Wataru Kumagai +1
Markov chain Monte Carlo (MCMC), such as Langevin dynamics, is valid for approximating intractable distributions. However, its usage is limited in the context of deep latent variab…
World Robot Challenge 2020 -- Partner Robot: A Data-Driven Approach for Room Tidying with Mobile Manipulator
Tatsuya Matsushima, Yuki Noguchi, Jumpei Arima +10
Tidying up a household environment using a mobile manipulator poses various challenges in robotics, such as adaptation to large real-world environmental variations, and safe and ro…
Robustifying Vision Transformer without Retraining from Scratch by Test-Time Class-Conditional Feature Alignment
Takeshi Kojima, Yutaka Matsuo, Yusuke Iwasawa
Vision Transformer (ViT) is becoming more popular in image processing. Specifically, we investigate the effectiveness of test-time adaptation (TTA) on ViT, a technique that has eme…
Large Language Models are Zero-Shot Reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid +2
Pretrained large language models (LLMs) are widely used in many sub-fields of natural language processing (NLP) and generally known as excellent few-shot learners with task-specifi…