1 code implementation • 28 Dec 2023 • Chin-Hsuan Wu, Yen-Chun Chen, Bolivar Solarte, Lu Yuan, Min Sun
Our strategy unfolds in three steps: (1) We invert the diffusion model for camera pose estimation instead of synthesizing novel views.
1 code implementation • 18 Oct 2023 • Cheng-Fu Yang, Yen-Chun Chen, Jianwei Yang, Xiyang Dai, Lu Yuan, Yu-Chiang Frank Wang, Kai-Wei Chang
Additional analysis shows that the contrastive objective and meta-actions are complementary in achieving the best results, and the resulting agent better aligns its states with corresponding instructions, making it more suitable for real-world embodied agents.
1 code implementation • NeurIPS 2023 • Shihao Zhao, Dongdong Chen, Yen-Chun Chen, Jianmin Bao, Shaozhe Hao, Lu Yuan, Kwan-Yee K. Wong
Text-to-Image diffusion models have made tremendous progress over the past two years, enabling the generation of highly realistic images based on open-domain text descriptions.
1 code implementation • 29 Aug 2022 • Wan-Cyuan Fan, Yen-Chun Chen, Dongdong Chen, Yu Cheng, Lu Yuan, Yu-Chiang Frank Wang
Diffusion models (DMs) have shown great potential for high-quality image synthesis.
1 code implementation • 12 Jun 2022 • Haotian Zhang, Pengchuan Zhang, Xiaowei Hu, Yen-Chun Chen, Liunian Harold Li, Xiyang Dai, Lijuan Wang, Lu Yuan, Jenq-Neng Hwang, Jianfeng Gao
We present GLIPv2, a grounded VL understanding model, that serves both localization tasks (e. g., object detection, instance segmentation) and Vision-Language (VL) understanding tasks (e. g., VQA, image captioning).
Ranked #1 on Phrase Grounding on Flickr30k Entities Test (using extra training data)
no code implementations • 22 Apr 2022 • Zhecan Wang, Noel Codella, Yen-Chun Chen, Luowei Zhou, Xiyang Dai, Bin Xiao, Jianwei Yang, Haoxuan You, Kai-Wei Chang, Shih-Fu Chang, Lu Yuan
Experiments demonstrate that MAD leads to consistent gains in the low-shot, domain-shifted, and fully-supervised conditions on VCR, SNLI-VE, and VQA, achieving SOTA performance on VCR compared to other single models pretrained with image-text data.
Ranked #4 on Visual Question Answering (VQA) on VCR (Q-A) test
no code implementations • 15 Jan 2022 • Zhecan Wang, Noel Codella, Yen-Chun Chen, Luowei Zhou, Jianwei Yang, Xiyang Dai, Bin Xiao, Haoxuan You, Shih-Fu Chang, Lu Yuan
Experiments demonstrate that our proposed CLIP-TD leads to exceptional gains in the low-shot (up to 51. 9%) and domain-shifted (up to 71. 3%) conditions of VCR, while simultaneously improving performance under standard fully-supervised conditions (up to 2%), achieving state-of-art performance on VCR compared to other single models that are pretrained with image-text data only.
1 code implementation • 8 Jun 2021 • Linjie Li, Jie Lei, Zhe Gan, Licheng Yu, Yen-Chun Chen, Rohit Pillai, Yu Cheng, Luowei Zhou, Xin Eric Wang, William Yang Wang, Tamara Lee Berg, Mohit Bansal, Jingjing Liu, Lijuan Wang, Zicheng Liu
Most existing video-and-language (VidL) research focuses on a single dataset, or multiple datasets of a single task.
no code implementations • 23 Apr 2021 • Zhe Gan, Yen-Chun Chen, Linjie Li, Tianlong Chen, Yu Cheng, Shuohang Wang, Jingjing Liu, Lijuan Wang, Zicheng Liu
However, we can find "relaxed" winning tickets at 50%-70% sparsity that maintain 99% of the full accuracy.
2 code implementations • NAACL 2021 • Siqi Sun, Yen-Chun Chen, Linjie Li, Shuohang Wang, Yuwei Fang, Jingjing Liu
Multimodal pre-training has propelled great advancement in vision-and-language research.
no code implementations • Findings (ACL) 2021 • Shuohang Wang, Luowei Zhou, Zhe Gan, Yen-Chun Chen, Yuwei Fang, Siqi Sun, Yu Cheng, Jingjing Liu
Transformer has become ubiquitous in the deep learning field.
no code implementations • 13 Sep 2020 • Shuohang Wang, Luowei Zhou, Zhe Gan, Yen-Chun Chen, Yuwei Fang, Siqi Sun, Yu Cheng, Jingjing Liu
Transformer has become ubiquitous in the deep learning field.
Ranked #1 on Open-Domain Question Answering on SearchQA
1 code implementation • ACL 2020 • Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan
We present a large, tunable neural conversational response generation model, DIALOGPT (dialogue generative pre-trained transformer).
2 code implementations • NeurIPS 2020 • Zhe Gan, Yen-Chun Chen, Linjie Li, Chen Zhu, Yu Cheng, Jingjing Liu
We present VILLA, the first known effort on large-scale adversarial training for vision-and-language (V+L) representation learning.
Ranked #7 on Visual Entailment on SNLI-VE val (using extra training data)
no code implementations • ECCV 2020 • Jize Cao, Zhe Gan, Yu Cheng, Licheng Yu, Yen-Chun Chen, Jingjing Liu
To reveal the secrets behind the scene of these powerful models, we present VALUE (Vision-And-Language Understanding Evaluation), a set of meticulously designed probing tasks (e. g., Visual Coreference Resolution, Visual Relation Detection, Linguistic Probing Tasks) generalizable to standard pre-trained V+L models, aiming to decipher the inner workings of multimodal pre-training (e. g., the implicit knowledge garnered in individual attention heads, the inherent cross-modal alignment learned through contextualized multimodal embeddings).
3 code implementations • EMNLP 2020 • Linjie Li, Yen-Chun Chen, Yu Cheng, Zhe Gan, Licheng Yu, Jingjing Liu
We present HERO, a novel framework for large-scale video+language omni-representation learning.
Ranked #1 on Video Retrieval on TVR
1 code implementation • ACL 2020 • Yen-Chun Chen, Zhe Gan, Yu Cheng, Jingzhou Liu, Jingjing Liu
Experiments show that the proposed approach significantly outperforms strong Transformer baselines on multiple language generation tasks such as machine translation and text summarization.
6 code implementations • 1 Nov 2019 • Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan
We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer).
no code implementations • 25 Sep 2019 • Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, Jingjing Liu
Joint image-text embedding is the bedrock for most Vision-and-Language (V+L) tasks, where multimodality inputs are jointly processed for visual and textual understanding.
7 code implementations • ECCV 2020 • Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, Jingjing Liu
Different from previous work that applies joint random masking to both modalities, we use conditional masking on pre-training tasks (i. e., masked language/region modeling is conditioned on full observation of image/text).
Ranked #3 on Visual Question Answering (VQA) on VCR (Q-A) test
1 code implementation • ACL 2019 • Yichen Jiang, Nitish Joshi, Yen-Chun Chen, Mohit Bansal
Multi-hop reading comprehension requires the model to explore and connect relevant information from multiple sentences/documents in order to answer the question about the context.
3 code implementations • ACL 2018 • Yen-Chun Chen, Mohit Bansal
Inspired by how humans summarize long documents, we propose an accurate and fast summarization model that first selects salient sentences and then rewrites them abstractively (i. e., compresses and paraphrases) to generate a concise overall summary.
Ranked #7 on Text Summarization on CNN / Daily Mail (Anonymized)