Search Results for author: Liping Jing

Found 32 papers, 15 papers with code

BSDP: Brain-inspired Streaming Dual-level Perturbations for Online Open World Object Detection

no code implementations5 Mar 2024 Yu Chen, Liyan Ma, Liping Jing, Jian Yu

Humans can easily distinguish the known and unknown categories and can recognize the unknown object by learning it once instead of repeating it many times without forgetting the learned object.

Incremental Learning object-detection +1

Retrieval Augmented Cross-Modal Tag Recommendation in Software Q&A Sites

no code implementations6 Feb 2024 Sijin Lu, Pengyu Xu, Bing Liu, Hongjian Sun, Liping Jing, Jian Yu

For the retrieval-augmented representations, we employ a cross-modal context-aware attention to leverage the main modality description for targeted feature extraction across the submodalities title and code.

feature selection Retrieval +1

Large Language Models as Zero-Shot Keyphrase Extractors: A Preliminary Empirical Study

1 code implementation23 Dec 2023 Mingyang Song, Xuelian Geng, Songfang Yao, Shilong Lu, Yi Feng, Liping Jing

Zero-shot keyphrase extraction aims to build a keyphrase extractor without training by human-annotated data, which is challenging due to the limited human intervention involved.

Keyphrase Extraction Language Modelling +1

Sequential Tag Recommendation

no code implementations9 Oct 2023 Bing Liu, Pengyu Xu, Sijin Lu, Shijing Wang, Hongjian Sun, Liping Jing

With the development of Internet technology and the expansion of social networks, online platforms have become an important way for people to obtain information.

Recommendation Systems Retrieval +1

Divert More Attention to Vision-Language Object Tracking

1 code implementation19 Jul 2023 Mingzhe Guo, Zhipeng Zhang, Liping Jing, Haibin Ling, Heng Fan

To thoroughly evidence the effectiveness of our method, we integrate the proposed framework on three tracking methods with different designs, i. e., the CNN-based SiamCAR, the Transformer-based OSTrack, and the hybrid structure TransT.

Attribute Object +1

Recognizable Information Bottleneck

1 code implementation28 Apr 2023 Yilin Lyu, Xin Liu, Mingyang Song, Xinyue Wang, Yaxin Peng, Tieyong Zeng, Liping Jing

The recent PAC-Bayes IB uses information complexity instead of information compression to establish a connection with the mutual information generalization bound.

Siamese transformer with hierarchical concept embedding for fine-grained image recognition

no code implementations Science China Information Sciences 2023 Yilin Lyu, Liping Jing, Jiaqi Wang, Mingzhe Guo, Xinyue Wang & Jian Yu

In particular, one subnetwork is for coarse-scale patches to learn the discriminative regions with the aid of the innate multi-head self-attention mechanism of the transformer.

Fine-Grained Image Recognition

Pairwise Instance Relation Augmentation for Long-tailed Multi-label Text Classification

no code implementations19 Nov 2022 Lin Xiao, Pengyu Xu, Liping Jing, Xiangliang Zhang

In response, we propose a Pairwise Instance Relation Augmentation Network (PIRAN) to augment tailed-label documents for balancing tail labels and head labels.

Multi Label Text Classification Multi-Label Text Classification +2

Learning Intrinsic and Extrinsic Intentions for Cold-start Recommendation with Neural Stochastic Processes

no code implementations MM '22: Proceedings of the 30th ACM International Conference on Multimedia 2022 Huafeng Liu, Liping Jing, Dahai Yu, Mingjie Zhou, Michael Ng

In this paper, we propose an intention neural process model (INP) for user cold-start recommendation (i. e., user with very few historical interactions), a novel extension of the neural stochastic process family using a general meta learning strategy with intrinsic and extrinsic intention learning for robust user preference learning.

Decision Making Meta-Learning

Strong Transferable Adversarial Attacks via Ensembled Asymptotically Normal Distribution Learning

1 code implementation24 Sep 2022 Zhengwei Fang, Rui Wang, Tao Huang, Liping Jing

Strong adversarial examples are crucial for evaluating and enhancing the robustness of deep neural networks.

Adversarial Attack

Divert More Attention to Vision-Language Tracking

1 code implementation3 Jul 2022 Mingzhe Guo, Zhipeng Zhang, Heng Fan, Liping Jing

By revealing the potential of VL representation, we expect the community to divert more attention to VL tracking and hope to open more possibilities for future tracking beyond Transformer.

Object Tracking

Deep Amortized Relational Model with Group-Wise Hierarchical Generative Process

no code implementations AAAI 2022 Huafeng Liu, Tong Zhou, Jiaqi Wang, Liping Jing

In this paper, we propose Deep amortized Relational Model (DaRM) with group-wise hierarchical generative process for community discovery and link prediction on relational data (e. g., graph, network).

Community Detection Link Prediction

Hyperbolic Relevance Matching for Neural Keyphrase Extraction

1 code implementation NAACL 2022 Mingyang Song, Yi Feng, Liping Jing

Meanwhile, considering the hierarchical structure hidden in the document, HyperMatch embeds both phrases and documents in the same hyperbolic space via a hyperbolic phrase encoder and a hyperbolic document encoder.

Information Retrieval Keyphrase Extraction +2

Learning Target-aware Representation for Visual Tracking via Informative Interactions

no code implementations7 Jan 2022 Mingzhe Guo, Zhipeng Zhang, Heng Fan, Liping Jing, Yilin Lyu, Bing Li, Weiming Hu

The proposed GIM module and InBN mechanism are general and applicable to different backbone types including CNN and Transformer for improvements, as evidenced by our extensive experiments on multiple benchmarks.

Representation Learning Visual Tracking

Topic-Aware Encoding for Extractive Summarization

no code implementations17 Dec 2021 Mingyang Song, Liping Jing

Specifically, a neural topic model is added in the neural-based sentence-level representation learning to adequately consider the central topic information for capturing the critical content in the original document.

Document Summarization Extractive Summarization +2

Reinforcing Semantic-Symmetry for Document Summarization

no code implementations14 Dec 2021 Mingyang Song, Liping Jing

Among them, the extractor identifies the salient sentences from the input document, and the abstractor generates a summary from the salient sentences.

Document Summarization reinforcement-learning +3

Reinforced Abstractive Summarization with Adaptive Length Controlling

no code implementations14 Dec 2021 Mingyang Song, Yi Feng, Liping Jing

Document summarization, as a fundamental task in natural language generation, aims to generate a short and coherent summary for a given document.

Abstractive Text Summarization Document Summarization +3

Importance Estimation from Multiple Perspectives for Keyphrase Extraction

no code implementations EMNLP 2021 Mingyang Song, Liping Jing, Lin Xiao

Keyphrase extraction is a fundamental task in Natural Language Processing, which usually contains two main parts: candidate keyphrase extraction and keyphrase importance estimation.

Chunking Keyphrase Extraction +1

StereoRel: Relational Triple Extraction from a Stereoscopic Perspective

no code implementations ACL 2021 Xuetao Tian, Liping Jing, Lu He, Feng Liu

Relational triple extraction is critical to understanding massive text corpora and constructing large-scale knowledge graph, which has attracted increasing research interest.

Cluster-Wise Hierarchical Generative Model for Deep Amortized Clustering

no code implementations CVPR 2021 Huafeng Liu, Jiaqi Wang, Liping Jing

In this paper, we propose Cluster-wise Hierarchical Generative Model for deep amortized clustering (CHiGac).


Probing BERT in Hyperbolic Spaces

1 code implementation ICLR 2021 Boli Chen, Yao Fu, Guangwei Xu, Pengjun Xie, Chuanqi Tan, Mosha Chen, Liping Jing

We introduce a Poincare probe, a structural probe projecting these embeddings into a Poincare subspace with explicitly defined hierarchies.

Word Embeddings

Does Head Label Help for Long-Tailed Multi-Label Text Classification

1 code implementation24 Jan 2021 Lin Xiao, Xiangliang Zhang, Liping Jing, Chi Huang, Mingyang Song

To address the challenge of insufficient training data on tail label classification, we propose a Head-to-Tail Network (HTTN) to transfer the meta-knowledge from the data-rich head labels to data-poor tail labels.

General Classification Multi Label Text Classification +2

Interpretable Image Recognition by Constructing Transparent Embedding Space

2 code implementations ICCV 2021 Jiaqi Wang, Huafeng Liu, Xinyue Wang, Liping Jing

This plug-in embedding space is spanned by transparent basis concepts which are constructed on the Grassmann manifold.

Hyperbolic Capsule Networks for Multi-Label Classification

no code implementations ACL 2020 Boli Chen, Xin Huang, Lin Xiao, Liping Jing

Second, Hyperbolic Dynamic Routing (HDR) is introduced to aggregate hyperbolic capsules in a label-aware manner, so that the label-level discriminative information can be preserved along the depth of neural networks.

Classification General Classification +1

Cross-Task Transfer for Geotagged Audiovisual Aerial Scene Recognition

1 code implementation ECCV 2020 Di Hu, Xuhong LI, Lichao Mou, Pu Jin, Dong Chen, Liping Jing, Xiaoxiang Zhu, Dejing Dou

With the help of this dataset, we evaluate three proposed approaches for transferring the sound event knowledge to the aerial scene recognition task in a multimodal learning framework, and show the benefit of exploiting the audio information for the aerial scene recognition.

Scene Recognition

Hyperbolic Interaction Model For Hierarchical Multi-Label Classification

1 code implementation26 May 2019 Boli Chen, Xin Huang, Lin Xiao, Zixin Cai, Liping Jing

The main reason is that the tree-likeness of the hyperbolic space matches the complexity of symbolic data with hierarchical structures.

Classification General Classification +1

The Automatic Identification of Butterfly Species

no code implementations18 Mar 2018 Juanying Xie, Qi Hou, Yinghuan Shi, Lv Peng, Liping Jing, Fuzhen Zhuang, Junping Zhang, Xiaoyang Tang, Shengquan Xu

We delete those species with only one living environment image from data set, then partition the rest images from living environment into two subsets, one used as test subset, the other as training subset respectively combined with all standard pattern butterfly images or the standard pattern butterfly images with the same species of the images from living environment.

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