Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Li, Xiujun"'
The rapid progress in Multimodal Large Language Models (MLLMs) has significantly advanced their ability to process and understand complex visual and textual information. However, the integration of multiple images and extensive textual contexts remai
Externí odkaz:
http://arxiv.org/abs/2405.14213
Recent multimodal large language models (MLLMs) have shown promising instruction following capabilities on vision-language tasks. In this work, we introduce VISUAL MODALITY INSTRUCTION (VIM), and investigate how well multimodal models can understand
Externí odkaz:
http://arxiv.org/abs/2311.17647
Existing automatic evaluation on text-to-image synthesis can only provide an image-text matching score, without considering the object-level compositionality, which results in poor correlation with human judgments. In this work, we propose LLMScore,
Externí odkaz:
http://arxiv.org/abs/2305.11116
We present an analysis on the heavy flavor hadron decay electrons with charm and beauty contributions decomposed via a data driven method in p+p and Pb+Pb collisions at $\sqrt{s_{\mathrm{NN}}}$ = 5.02 TeV at LHC. The transverse momentum $p_{\mathrm{T
Externí odkaz:
http://arxiv.org/abs/2110.08769
Autor:
Zhang, Pengchuan, Li, Xiujun, Hu, Xiaowei, Yang, Jianwei, Zhang, Lei, Wang, Lijuan, Choi, Yejin, Gao, Jianfeng
Publikováno v:
CVPR 2021
This paper presents a detailed study of improving visual representations for vision language (VL) tasks and develops an improved object detection model to provide object-centric representations of images. Compared to the most widely used \emph{bottom
Externí odkaz:
http://arxiv.org/abs/2101.00529
Self-supervised pre-training (SSP) employs random image transformations to generate training data for visual representation learning. In this paper, we first present a modeling framework that unifies existing SSP methods as learning to predict pseudo
Externí odkaz:
http://arxiv.org/abs/2012.13493
Autor:
Wang, Jianfeng, Hu, Xiaowei, Zhang, Pengchuan, Li, Xiujun, Wang, Lijuan, Zhang, Lei, Gao, Jianfeng, Liu, Zicheng
Recent vision-language (VL) studies have shown remarkable progress by learning generic representations from massive image-text pairs with transformer models and then fine-tuning on downstream VL tasks. While existing research has been focused on achi
Externí odkaz:
http://arxiv.org/abs/2012.06946
Autor:
Li, Xiujun, Yin, Xi, Li, Chunyuan, Zhang, Pengchuan, Hu, Xiaowei, Zhang, Lei, Wang, Lijuan, Hu, Houdong, Dong, Li, Wei, Furu, Choi, Yejin, Gao, Jianfeng
Large-scale pre-training methods of learning cross-modal representations on image-text pairs are becoming popular for vision-language tasks. While existing methods simply concatenate image region features and text features as input to the model to be
Externí odkaz:
http://arxiv.org/abs/2004.06165
When trained effectively, the Variational Autoencoder (VAE) can be both a powerful generative model and an effective representation learning framework for natural language. In this paper, we propose the first large-scale language VAE model, Optimus.
Externí odkaz:
http://arxiv.org/abs/2004.04092
Autor:
Xia, Qiaolin, Li, Xiujun, Li, Chunyuan, Bisk, Yonatan, Sui, Zhifang, Gao, Jianfeng, Choi, Yejin, Smith, Noah A.
Learning to navigate in a visual environment following natural language instructions is a challenging task because natural language instructions are highly variable, ambiguous, and under-specified. In this paper, we present a novel training paradigm,
Externí odkaz:
http://arxiv.org/abs/2003.00857