Search Results for author: Ting Jiang

Found 18 papers, 11 papers with code

CAN-GRU: a Hierarchical Model for Emotion Recognition in Dialogue

no code implementations CCL 2020 Ting Jiang, Bing Xu, Tiejun Zhao, Sheng Li

In the first layer, in order to extract textual features of utterances, we propose a convolutional self-attention network(CAN).

Emotion Recognition Opinion Mining

Improving Bracket Image Restoration and Enhancement with Flow-guided Alignment and Enhanced Feature Aggregation

no code implementations16 Apr 2024 Wenjie Lin, Zhen Liu, Chengzhi Jiang, Mingyan Han, Ting Jiang, Shuaicheng Liu

In this paper, we address the Bracket Image Restoration and Enhancement (BracketIRE) task using a novel framework, which requires restoring a high-quality high dynamic range (HDR) image from a sequence of noisy, blurred, and low dynamic range (LDR) multi-exposure RAW inputs.

Image Restoration Optical Flow Estimation

Improving Domain Adaptation through Extended-Text Reading Comprehension

1 code implementation14 Jan 2024 Ting Jiang, Shaohan Huang, Shengyue Luo, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang, Deqing Wang, Fuzhen Zhuang

To enhance the domain-specific capabilities of large language models, continued pre-training on a domain-specific corpus is a prevalent method.

Clustering Domain Adaptation +1

FrFT based estimation of linear and nonlinear impairments using Vision Transformer

no code implementations25 Aug 2023 Ting Jiang, Zheng Gao, Yizhao Chen, Zihe Hu, Ming Tang

To comprehensively assess optical fiber communication system conditions, it is essential to implement joint estimation of the following four critical impairments: nonlinear signal-to-noise ratio (SNRNL), optical signal-to-noise ratio (OSNR), chromatic dispersion (CD) and differential group delay (DGD).

Scaling Sentence Embeddings with Large Language Models

1 code implementation31 Jul 2023 Ting Jiang, Shaohan Huang, Zhongzhi Luan, Deqing Wang, Fuzhen Zhuang

We also fine-tune LLMs with current contrastive learning approach, and the 2. 7B OPT model, incorporating our prompt-based method, surpasses the performance of 4. 8B ST5, achieving the new state-of-the-art results on STS tasks.

Contrastive Learning In-Context Learning +4

SAM-IQA: Can Segment Anything Boost Image Quality Assessment?

1 code implementation10 Jul 2023 Xinpeng Li, Ting Jiang, Haoqiang Fan, Shuaicheng Liu

Our experiments confirm the powerful feature extraction capabilities of Segment Anything and highlight the value of combining spatial-domain and frequency-domain features in IQA tasks.

Image Quality Assessment

Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Prediction

no code implementations27 May 2023 Hao Geng, Deqing Wang, Fuzhen Zhuang, Xuehua Ming, Chenguang Du, Ting Jiang, Haolong Guo, Rui Liu

To cope with this problem, we propose a Dynamic heterogeneous Graph and Node Importance network (DGNI) learning framework, which fully leverages the dynamic heterogeneous graph and node importance information to predict future citation trends of newly published papers.

Citation Prediction Network Embedding

Realistic Noise Synthesis with Diffusion Models

no code implementations23 May 2023 Qi Wu, Mingyan Han, Ting Jiang, Haoqiang Fan, Bing Zeng, Shuaicheng Liu

Deep image denoising models often rely on large amount of training data for the high quality performance.

Image Denoising Noise Estimation

DIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution

no code implementations14 Apr 2023 Lei Yu, Xinpeng Li, Youwei Li, Ting Jiang, Qi Wu, Haoqiang Fan, Shuaicheng Liu

To address this issue, we propose a novel multi-stage lightweight network boosting method, which can enable lightweight networks to achieve outstanding performance.

Image Super-Resolution Network Pruning

RHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale Graphs

1 code implementation20 Nov 2022 Ziming Wan, Deqing Wang, Xuehua Ming, Fuzhen Zhuang, Chenguang Du, Ting Jiang, Zhengyang Zhao

To address these problems, we propose a novel Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning (RHCO) for large-scale heterogeneous graph representation learning.

Contrastive Learning Graph Representation Learning +1

Pruning Pre-trained Language Models Without Fine-Tuning

1 code implementation12 Oct 2022 Ting Jiang, Deqing Wang, Fuzhen Zhuang, Ruobing Xie, Feng Xia

These methods, such as movement pruning, use first-order information to prune PLMs while fine-tuning the remaining weights.

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

no code implementations25 May 2022 Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw, Aleš Leonardis, Radu Timofte, Zexin Zhang, Cen Liu, Yunbo Peng, Yue Lin, Gaocheng Yu, Jin Zhang, Zhe Ma, Hongbin Wang, Xiangyu Chen, Xintao Wang, Haiwei Wu, Lin Liu, Chao Dong, Jiantao Zhou, Qingsen Yan, Song Zhang, Weiye Chen, Yuhang Liu, Zhen Zhang, Yanning Zhang, Javen Qinfeng Shi, Dong Gong, Dan Zhu, Mengdi Sun, Guannan Chen, Yang Hu, Haowei Li, Baozhu Zou, Zhen Liu, Wenjie Lin, Ting Jiang, Chengzhi Jiang, Xinpeng Li, Mingyan Han, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Juan Marín-Vega, Michael Sloth, Peter Schneider-Kamp, Richard Röttger, Chunyang Li, Long Bao, Gang He, Ziyao Xu, Li Xu, Gen Zhan, Ming Sun, Xing Wen, Junlin Li, Shuang Feng, Fei Lei, Rui Liu, Junxiang Ruan, Tianhong Dai, Wei Li, Zhan Lu, Hengyan Liu, Peian Huang, Guangyu Ren, Yonglin Luo, Chang Liu, Qiang Tu, Fangya Li, Ruipeng Gang, Chenghua Li, Jinjing Li, Sai Ma, Chenming Liu, Yizhen Cao, Steven Tel, Barthelemy Heyrman, Dominique Ginhac, Chul Lee, Gahyeon Kim, Seonghyun Park, An Gia Vien, Truong Thanh Nhat Mai, Howoon Yoon, Tu Vo, Alexander Holston, Sheir Zaheer, Chan Y. Park

The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).

Image Restoration Vocal Bursts Intensity Prediction

Exploiting Global and Local Hierarchies for Hierarchical Text Classification

1 code implementation5 May 2022 Ting Jiang, Deqing Wang, Leilei Sun, Zhongzhi Chen, Fuzhen Zhuang, Qinghong Yang

Existing methods encode label hierarchy in a global view, where label hierarchy is treated as the static hierarchical structure containing all labels.

Multi Label Text Classification Multi-Label Text Classification +1

Improving Non-autoregressive Generation with Mixup Training

1 code implementation21 Oct 2021 Ting Jiang, Shaohan Huang, Zihan Zhang, Deqing Wang, Fuzhen Zhuang, Furu Wei, Haizhen Huang, Liangjie Zhang, Qi Zhang

While pre-trained language models have achieved great success on various natural language understanding tasks, how to effectively leverage them into non-autoregressive generation tasks remains a challenge.

Natural Language Understanding Paraphrase Generation +2

ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging

8 code implementations22 May 2021 Zhen Liu, Wenjie Lin, Xinpeng Li, Qing Rao, Ting Jiang, Mingyan Han, Haoqiang Fan, Jian Sun, Shuaicheng Liu

In this paper, we present an attention-guided deformable convolutional network for hand-held multi-frame high dynamic range (HDR) imaging, namely ADNet.

Face Alignment Vocal Bursts Intensity Prediction

LightXML: Transformer with Dynamic Negative Sampling for High-Performance Extreme Multi-label Text Classification

1 code implementation9 Jan 2021 Ting Jiang, Deqing Wang, Leilei Sun, Huayi Yang, Zhengyang Zhao, Fuzhen Zhuang

In LightXML, we use generative cooperative networks to recall and rank labels, in which label recalling part generates negative and positive labels, and label ranking part distinguishes positive labels from these labels.

General Classification Multi Label Text Classification +2

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