no code implementations • ICML 2020 • Jiaxian Guo, Mingming Gong, Tongliang Liu, Kun Zhang, DaCheng Tao
Distribution shift is a major obstacle to the deployment of current deep learning models on real-world problems.
no code implementations • 26 Jan 2025 • Bo Yang, Jiaxian Guo, Yusuke Iwasawa, Yutaka Matsuo
Recent studies have increasingly demonstrated that large language models (LLMs) possess significant theory of mind (ToM) capabilities, showing the potential for simulating the tracking of mental states in generative agents.
no code implementations • 19 Dec 2024 • Xiabin Zhou, Wenbin Wang, Minyan Zeng, Jiaxian Guo, Xuebo Liu, Li Shen, Min Zhang, Liang Ding
Efficient KV cache management in LLMs is crucial for long-context tasks like RAG and summarization.
no code implementations • 8 Feb 2024 • Kento Kawaharazuka, Tatsuya Matsushima, Andrew Gambardella, Jiaxian Guo, Chris Paxton, Andy Zeng
This paper provides an overview of the practical application of foundation models in real-world robotics, with a primary emphasis on the replacement of specific components within existing robot systems.
1 code implementation • 29 Sep 2023 • Jiaxian Guo, Bo Yang, Paul Yoo, Bill Yuchen Lin, Yusuke Iwasawa, Yutaka Matsuo
Unlike perfect information games, where all elements are known to every player, imperfect information games emulate the real-world complexities of decision-making under uncertain or incomplete information.
no code implementations • 16 Sep 2023 • So Kuroki, Jiaxian Guo, Tatsuya Matsushima, Takuya Okubo, Masato Kobayashi, Yuya Ikeda, Ryosuke Takanami, Paul Yoo, Yutaka Matsuo, Yusuke Iwasawa
Due to the inherent uncertainty in their deformability during motion, previous methods in deformable object manipulation, such as rope and cloth, often required hundreds of real-world demonstrations to train a manipulation policy for each object, which hinders their applications in our ever-changing world.
no code implementations • 14 Jun 2023 • So Kuroki, Jiaxian Guo, Tatsuya Matsushima, Takuya Okubo, Masato Kobayashi, Yuya Ikeda, Ryosuke Takanami, Paul Yoo, Yutaka Matsuo, Yusuke Iwasawa
To achieve this, we augment the policy by conditioning it on deformable rope parameters and training it with a diverse range of simulated deformable ropes so that the policy can adjust actions based on different rope parameters.
no code implementations • 13 Jun 2023 • Xin Zhang, Jiaxian Guo, Paul Yoo, Yutaka Matsuo, Yusuke Iwasawa
To guarantee the visual coherence of the generated or edited image, we introduce an inpainting and harmonizing module to guide the pre-trained diffusion model to seamlessly blend the inserted subject into the scene naturally.
no code implementations • 6 Jun 2023 • Paul Yoo, Jiaxian Guo, Yutaka Matsuo, Shixiang Shane Gu
Leveraging the strong image priors in the pre-trained diffusion models, DreamSparse is capable of synthesizing high-quality novel views for both object and scene-level images and generalising to open-set images.
1 code implementation • 16 Jan 2023 • Xingzhou Lou, Jiaxian Guo, Junge Zhang, Jun Wang, Kaiqi Huang, Yali Du
We conduct experiments on the Overcooked environment, and evaluate the zero-shot human-AI coordination performance of our method with both behavior-cloned human proxies and real humans.
no code implementations • CVPR 2023 • Jiaxian Guo, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Boyang Li, DaCheng Tao, Steven Hoi
To address this issue, we propose Img2Prompt, a plug-and-play module that provides the prompts that can bridge the aforementioned modality and task disconnections, so that LLMs can perform zero-shot VQA tasks without end-to-end training.
3 code implementations • 21 Dec 2022 • Jiaxian Guo, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Boyang Li, DaCheng Tao, Steven C. H. Hoi
To address this issue, we propose \emph{Img2Prompt}, a plug-and-play module that provides the prompts that can bridge the aforementioned modality and task disconnections, so that LLMs can perform zero-shot VQA tasks without end-to-end training.
no code implementations • 24 Jul 2022 • Zhen Wang, Liu Liu, Yajing Kong, Jiaxian Guo, DaCheng Tao
Based on the learnable focuses, we design a focal contrastive loss to rebalance contrastive learning between new and past classes and consolidate previously learned representations.
1 code implementation • CVPR 2022 • Jiaxian Guo, Jiachen Li, Huan Fu, Mingming Gong, Kun Zhang, DaCheng Tao
Unsupervised image-to-image (I2I) translation aims to learn a domain mapping function that can preserve the semantics of the input images without paired data.
no code implementations • 1 Jan 2021 • Jiaxian Guo, Jiachen Li, Mingming Gong, Huan Fu, Kun Zhang, DaCheng Tao
Unsupervised image-to-image (I2I) translation, which aims to learn a domain mapping function without paired data, is very challenging because the function is highly under-constrained.
no code implementations • WS 2020 • Minghan Wang, Hao Yang, Yao Deng, Ying Qin, Lizhi Lei, Daimeng Wei, Hengchao Shang, Ning Xie, Xiaochun Li, Jiaxian Guo
The paper presents details of our system in the IWSLT Video Speech Translation evaluation.
no code implementations • 6 Dec 2018 • Yuhui Xu, Shuai Zhang, Yingyong Qi, Jiaxian Guo, Weiyao Lin, Hongkai Xiong
Network quantization is an effective method for the deployment of neural networks on memory and energy constrained mobile devices.
1 code implementation • 6 Feb 2018 • Yaoming Zhu, Sidi Lu, Lei Zheng, Jiaxian Guo, Wei-Nan Zhang, Jun Wang, Yong Yu
We introduce Texygen, a benchmarking platform to support research on open-domain text generation models.
6 code implementations • 24 Sep 2017 • Jiaxian Guo, Sidi Lu, Han Cai, Wei-Nan Zhang, Yong Yu, Jun Wang
Automatically generating coherent and semantically meaningful text has many applications in machine translation, dialogue systems, image captioning, etc.
Ranked #1 on
Text Generation
on COCO Captions