110 citations · 867 across the 51 of their papers we have counts for
51 papers
Qwen Technical Report
Jinze Bai, Shuai Bai, Yunfei Chu +45
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to hu…
Transferring General Multimodal Pretrained Models to Text Recognition
Junyang Lin, Xuancheng Ren, Yichang Zhang +4
This paper proposes a new method, OFA-OCR, to transfer multimodal pretrained models to text recognition. Specifically, we recast text recognition as image captioning and directly t…
OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Models
Jinze Bai, Rui Men, Hao Yang +15
Generalist models, which are capable of performing diverse multi-modal tasks in a task-agnostic way within a single model, have been explored recently. Being, hopefully, an alterna…
DiMBERT: Learning Vision-Language Grounded Representations with Disentangled Multimodal-Attention
Fenglin Liu, Xian Wu, Shen Ge +4
Vision-and-language (V-L) tasks require the system to understand both vision content and natural language, thus learning fine-grained joint representations of vision and language (…
From Mimicking to Integrating: Knowledge Integration for Pre-Trained Language Models
Lei Li, Yankai Lin, Xuancheng Ren +4
Investigating better ways to reuse the released pre-trained language models (PLMs) can significantly reduce the computational cost and the potential environmental side-effects. Thi…
Prophet Attention: Predicting Attention with Future Attention for Image Captioning
Fenglin Liu, Xuancheng Ren, Xian Wu +3
Recently, attention based models have been used extensively in many sequence-to-sequence learning systems. Especially for image captioning, the attention based models are expected…
Delving into the Openness of CLIP
Shuhuai Ren, Lei Li, Xuancheng Ren +2
Contrastive Language-Image Pre-training (CLIP) formulates image classification as an image-to-text matching task, i.e., matching images to the corresponding natural language descri…
Hierarchical Inductive Transfer for Continual Dialogue Learning
Shaoxiong Feng, Xuancheng Ren, Kan Li +1
Pre-trained models have achieved excellent performance on the dialogue task. However, for the continual increase of online chit-chat scenarios, directly fine-tuning these models fo…
Model Uncertainty-Aware Knowledge Amalgamation for Pre-Trained Language Models
Lei Li, Yankai Lin, Xuancheng Ren +4
As many fine-tuned pre-trained language models~(PLMs) with promising performance are generously released, investigating better ways to reuse these models is vital as it can greatly…
Topology-Imbalance Learning for Semi-Supervised Node Classification
Deli Chen, Yankai Lin, Guangxiang Zhao +4
The class imbalance problem, as an important issue in learning node representations, has drawn increasing attention from the community. Although the imbalance considered by existin…
Well-classified Examples are Underestimated in Classification with Deep Neural Networks
Guangxiang Zhao, Wenkai Yang, Xuancheng Ren +3
The conventional wisdom behind learning deep classification models is to focus on bad-classified examples and ignore well-classified examples that are far from the decision boundar…
Adversarial Parameter Defense by Multi-Step Risk Minimization
Zhiyuan Zhang, Ruixuan Luo, Xuancheng Ren +3
Previous studies demonstrate DNNs' vulnerability to adversarial examples and adversarial training can establish a defense to adversarial examples. In addition, recent studies show…
O2NA: An Object-Oriented Non-Autoregressive Approach for Controllable Video Captioning
Fenglin Liu, Xuancheng Ren, Xian Wu +4
Video captioning combines video understanding and language generation. Different from image captioning that describes a static image with details of almost every object, video capt…
Rethinking Skip Connection with Layer Normalization in Transformers and ResNets
Fenglin Liu, Xuancheng Ren, Zhiyuan Zhang +2
Skip connection, is a widely-used technique to improve the performance and the convergence of deep neural networks, which is believed to relieve the difficulty in optimization due…
Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models
Wenkai Yang, Lei Li, Zhiyuan Zhang +3
Recent studies have revealed a security threat to natural language processing (NLP) models, called the Backdoor Attack. Victim models can maintain competitive performance on clean…
Multi-View Feature Representation for Dialogue Generation with Bidirectional Distillation
Shaoxiong Feng, Xuancheng Ren, Kan Li +1
Neural dialogue models suffer from low-quality responses when interacted in practice, demonstrating difficulty in generalization beyond training data. Recently, knowledge distillat…
Rethinking the Promotion Brought by Contrastive Learning to Semi-Supervised Node Classification
Deli Chen, Yankai Lin, Lei Li +4
Graph Contrastive Learning (GCL) has proven highly effective in promoting the performance of Semi-Supervised Node Classification (SSNC). However, existing GCL methods are generally…
CAPT: Contrastive Pre-Training for Learning Denoised Sequence Representations
Fuli Luo, Pengcheng Yang, Shicheng Li +2
Pre-trained self-supervised models such as BERT have achieved striking success in learning sequence representations, especially for natural language processing. These models typica…
Regularizing Dialogue Generation by Imitating Implicit Scenarios
Shaoxiong Feng, Xuancheng Ren, Hongshen Chen +3
Human dialogues are scenario-based and appropriate responses generally relate to the latent context knowledge entailed by the specific scenario. To enable responses that are more m…
Collaborative Group Learning
Shaoxiong Feng, Hongshen Chen, Xuancheng Ren +3
Collaborative learning has successfully applied knowledge transfer to guide a pool of small student networks towards robust local minima. However, previous approaches typically str…
Exploring the Vulnerability of Deep Neural Networks: A Study of Parameter Corruption
Xu Sun, Zhiyuan Zhang, Xuancheng Ren +2
We argue that the vulnerability of model parameters is of crucial value to the study of model robustness and generalization but little research has been devoted to understanding th…
Rethinking and Improving Natural Language Generation with Layer-Wise Multi-View Decoding
Fenglin Liu, Xuancheng Ren, Guangxiang Zhao +4
In sequence-to-sequence learning, e.g., natural language generation, the decoder relies on the attention mechanism to efficiently extract information from the encoder. While it is…
Exploring and Distilling Cross-Modal Information for Image Captioning
Fenglin Liu, Xuancheng Ren, Yuanxin Liu +2
Recently, attention-based encoder-decoder models have been used extensively in image captioning. Yet there is still great difficulty for the current methods to achieve deep image u…
Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection
Guangxiang Zhao, Junyang Lin, Zhiyuan Zhang +3
Self-attention based Transformer has demonstrated the state-of-the-art performances in a number of natural language processing tasks. Self-attention is able to model long-term depe…