Search Results for author: Juhua Liu

Found 25 papers, 19 papers with code

ROSE Doesn't Do That: Boosting the Safety of Instruction-Tuned Large Language Models with Reverse Prompt Contrastive Decoding

no code implementations19 Feb 2024 Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, DaCheng Tao

With the development of instruction-tuned large language models (LLMs), improving the safety of LLMs has become more critical.

Revisiting Knowledge Distillation for Autoregressive Language Models

no code implementations19 Feb 2024 Qihuang Zhong, Liang Ding, Li Shen, Juhua Liu, Bo Du, DaCheng Tao

Knowledge distillation (KD) is a common approach to compress a teacher model to reduce its inference cost and memory footprint, by training a smaller student model.

Knowledge Distillation

Hi-SAM: Marrying Segment Anything Model for Hierarchical Text Segmentation

1 code implementation31 Jan 2024 Maoyuan Ye, Jing Zhang, Juhua Liu, Chenyu Liu, BaoCai Yin, Cong Liu, Bo Du, DaCheng Tao

In terms of the AMG mode, Hi-SAM segments text stroke foreground masks initially, then samples foreground points for hierarchical text mask generation and achieves layout analysis in passing.

Segmentation Text Segmentation

GoMatching: A Simple Baseline for Video Text Spotting via Long and Short Term Matching

1 code implementation13 Jan 2024 Haibin He, Maoyuan Ye, Jing Zhang, Juhua Liu, DaCheng Tao

In response to this issue, we propose to efficiently turn an off-the-shelf query-based image text spotter into a specialist on video and present a simple baseline termed GoMatching, which focuses the training efforts on tracking while maintaining strong recognition performance.

Text Detection Text Spotting

Zero-Shot Sharpness-Aware Quantization for Pre-trained Language Models

no code implementations20 Oct 2023 Miaoxi Zhu, Qihuang Zhong, Li Shen, Liang Ding, Juhua Liu, Bo Du, DaCheng Tao

The key algorithm in solving ZSAQ is the SAM-SGA optimization, which aims to improve the quantization accuracy and model generalization via optimizing a minimax problem.

Language Modelling Quantization

PNT-Edge: Towards Robust Edge Detection with Noisy Labels by Learning Pixel-level Noise Transitions

1 code implementation26 Jul 2023 Wenjie Xuan, Shanshan Zhao, Yu Yao, Juhua Liu, Tongliang Liu, Yixin Chen, Bo Du, DaCheng Tao

Exploiting the estimated noise transitions, our model, named PNT-Edge, is able to fit the prediction to clean labels.

Edge Detection

DeepSolo++: Let Transformer Decoder with Explicit Points Solo for Text Spotting

2 code implementations31 May 2023 Maoyuan Ye, Jing Zhang, Shanshan Zhao, Juhua Liu, Tongliang Liu, Bo Du, DaCheng Tao

On the other hand, based on the extensibility of DeepSolo, we launch DeepSolo++ for multilingual text spotting, making a further step to let Transformer decoder with explicit points solo for multilingual text detection, recognition, and script identification all at once.

Scene Text Detection Text Detection +1

Self-Evolution Learning for Discriminative Language Model Pretraining

1 code implementation24 May 2023 Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, DaCheng Tao

Masked language modeling, widely used in discriminative language model (e. g., BERT) pretraining, commonly adopts a random masking strategy.

Language Modelling Masked Language Modeling +1

Revisiting Token Dropping Strategy in Efficient BERT Pretraining

1 code implementation24 May 2023 Qihuang Zhong, Liang Ding, Juhua Liu, Xuebo Liu, Min Zhang, Bo Du, DaCheng Tao

Token dropping is a recently-proposed strategy to speed up the pretraining of masked language models, such as BERT, by skipping the computation of a subset of the input tokens at several middle layers.

Scalable Mask Annotation for Video Text Spotting

1 code implementation2 May 2023 Haibin He, Jing Zhang, Mengyang Xu, Juhua Liu, Bo Du, DaCheng Tao

Video text spotting refers to localizing, recognizing, and tracking textual elements such as captions, logos, license plates, signs, and other forms of text within consecutive video frames.

Text Spotting

Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT

1 code implementation19 Feb 2023 Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, DaCheng Tao

Recently, ChatGPT has attracted great attention, as it can generate fluent and high-quality responses to human inquiries.

Question Answering Sentiment Analysis

Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE

no code implementations18 Feb 2023 Qihuang Zhong, Liang Ding, Keqin Peng, Juhua Liu, Bo Du, Li Shen, Yibing Zhan, DaCheng Tao

This technical report briefly describes our JDExplore d-team's submission Vega v1 on the General Language Understanding Evaluation (GLUE) leaderboard, where GLUE is a collection of nine natural language understanding tasks, including question answering, linguistic acceptability, sentiment analysis, text similarity, paraphrase detection, and natural language inference.

Contrastive Learning Denoising +12

Diff-Font: Diffusion Model for Robust One-Shot Font Generation

1 code implementation12 Dec 2022 Haibin He, Xinyuan Chen, Chaoyue Wang, Juhua Liu, Bo Du, DaCheng Tao, Yu Qiao

Specifically, a large stroke-wise dataset is constructed, and a stroke-wise diffusion model is proposed to preserve the structure and the completion of each generated character.

Font Generation

DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting

2 code implementations CVPR 2023 Maoyuan Ye, Jing Zhang, Shanshan Zhao, Juhua Liu, Tongliang Liu, Bo Du, DaCheng Tao

In this paper, we present DeepSolo, a simple DETR-like baseline that lets a single Decoder with Explicit Points Solo for text detection and recognition simultaneously.

 Ranked #1 on Text Spotting on Total-Text (using extra training data)

Scene Text Detection Text Detection +2

Improving Sharpness-Aware Minimization with Fisher Mask for Better Generalization on Language Models

1 code implementation11 Oct 2022 Qihuang Zhong, Liang Ding, Li Shen, Peng Mi, Juhua Liu, Bo Du, DaCheng Tao

Fine-tuning large pretrained language models on a limited training corpus usually suffers from poor generalization.

PANDA: Prompt Transfer Meets Knowledge Distillation for Efficient Model Adaptation

no code implementations22 Aug 2022 Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, DaCheng Tao

In response to these problems, we propose a new metric to accurately predict the prompt transferability (regarding (i)), and a novel PoT approach (namely PANDA) that leverages the knowledge distillation technique to transfer the "knowledge" from the source prompt to the target prompt in a subtle manner and alleviate the catastrophic forgetting effectively (regarding (ii)).

Knowledge Distillation Transfer Learning

DPText-DETR: Towards Better Scene Text Detection with Dynamic Points in Transformer

3 code implementations10 Jul 2022 Maoyuan Ye, Jing Zhang, Shanshan Zhao, Juhua Liu, Bo Du, DaCheng Tao

However, these methods built upon detection transformer framework might achieve sub-optimal training efficiency and performance due to coarse positional query modeling. In addition, the point label form exploited in previous works implies the reading order of humans, which impedes the detection robustness from our observation.

Inductive Bias Scene Text Detection +1

E2S2: Encoding-Enhanced Sequence-to-Sequence Pretraining for Language Understanding and Generation

1 code implementation30 May 2022 Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, DaCheng Tao

To verify our hypothesis, we first empirically study the functionalities of the encoder and decoder in seq2seq pretrained language models, and find that the encoder takes an important but under-exploitation role than the decoder regarding the downstream performance and neuron activation.

Denoising Language Modelling +2

An End-to-end Supervised Domain Adaptation Framework for Cross-Domain Change Detection

1 code implementation1 Apr 2022 Jia Liu, Wenjie Xuan, Yuhang Gan, Juhua Liu, Bo Du

In this paper, we propose an end-to-end Supervised Domain Adaptation framework for cross-domain Change Detection, namely SDACD, to effectively alleviate the domain shift between bi-temporal images for better change predictions.

Change Detection Change detection for remote sensing images +1

Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-based Sentiment Analysis

1 code implementation13 Jan 2022 Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, Hua Jin, DaCheng Tao

To this end, we propose a knowledge graph augmented network KGAN, which aims to effectively incorporate external knowledge with explicitly syntactic and contextual information.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2

Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition

1 code implementation AAAI 2022 2021 Yue He, Chen Chen, Jing Zhang, Juhua Liu, Fengxiang He, Chaoyue Wang, Bo Du

Technically, given the character segmentation maps predicted by a VR model, we construct a subgraph for each instance, where nodes represent the pixels in it and edges are added between nodes based on their spatial similarity.

Ranked #9 on Scene Text Recognition on ICDAR2015 (using extra training data)

Language Modelling Scene Text Recognition

Unified Instance and Knowledge Alignment Pretraining for Aspect-based Sentiment Analysis

1 code implementation26 Oct 2021 Juhua Liu, Qihuang Zhong, Liang Ding, Hua Jin, Bo Du, DaCheng Tao

In practice, we formulate the model pretrained on the sampled instances into a knowledge guidance model and a learner model, respectively.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2

I3CL:Intra- and Inter-Instance Collaborative Learning for Arbitrary-shaped Scene Text Detection

1 code implementation3 Aug 2021 Bo Du, Jian Ye, Jing Zhang, Juhua Liu, DaCheng Tao

Existing methods for arbitrary-shaped text detection in natural scenes face two critical issues, i. e., 1) fracture detections at the gaps in a text instance; and 2) inaccurate detections of arbitrary-shaped text instances with diverse background context.

Scene Text Detection Text Detection

TextFuseNet: Scene Text Detection with Richer Fused Features

6 code implementations17 May 2020 Jian Ye, Zhe Chen, Juhua Liu, Bo Du

More specifically, we propose to perceive texts from three levels of feature representations, i. e., character-, word- and global-level, and then introduce a novel text representation fusion technique to help achieve robust arbitrary text detection.

Scene Text Detection Text Detection

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