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TOME: A Two-stage Approach for Model-based Retrieval 61

TOME: A Two-stage Approach for Model-based Retrieval

引用

61st Annual Meeting of the Association for Computational Linguistics, ACL 2023

作者： Ren, Ruiyang Zhao, Wayne Xin Liu, Jing Wu, Hua Wen, Ji-Rong Wang, Haifeng Gaoling School of Artificial Intelligence Renmin University of China China Baidu Inc. China Beijing Key Laboratory of Big Data Management and Analysis Methods China

ISBN: (纸本)9781959429722

Recently, model-based retrieval has emerged as a new paradigm in text retrieval that discards the index in the traditional retrieval model and instead memorizes the candidate corpora using model parameters. This design employs a sequence-to-sequence paradigm to generate document identifiers, which enables the complete capture of the relevance between queries and documents and simplifies the classic index-retrieval-rerank pipeline. Despite its attractive qualities, there remain several major challenges in model-based retrieval, including the discrepancy between pre-training and fine-tuning, and the discrepancy between training and inference. To deal with the above challenges, we propose a novel two-stage model-based retrieval approach called TOME, which makes two major technical contributions, including the utilization of tokenized URLs as identifiers and the design of a two-stage generation architecture. We also propose a number of training strategies to deal with the training difficulty as the corpus size increases. Extensive experiments and analysis on MS MARCO and Natural Questions demonstrate the effectiveness of our proposed approach, and we investigate the scaling laws of TOME by examining various influencing factors. © 2023 Association for Computational Linguistics.

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Few-Shot Zero-Shot Learning: Knowledge Transfer with Less Supervision 15th

Few-Shot Zero-Shot Learning: Knowledge Transfer with Less Su...

引用

15th Asian Conference on Computer Vision, ACCV 2020

作者： Fei, Nanyi Guan, Jiechao Lu, Zhiwu Gao, Yizhao School of Information Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China Beijing China

ISBN: (纸本)9783030695347

Existing zero-shot learning (ZSL) methods assume that there exist sufficient training samples from seen classes, each annotated with semantic descriptors such as attributes, for knowledge transfer to unseen classes without any training samples. However, this assumption is often invalid because collecting sufficient seen class samples can be difficult and attribute annotation is expensive;it thus severely limits the scalability of ZSL. In this paper, we define a new setting termed Few-Shot Zero-Shot Learning (FSZSL), where only a few annotated images are collected from each seen class (i.e., few-shot). This is clearly more challenging yet more realistic than the conventional ZSL setting. To overcome the resultant image-level attribute sparsity, we propose a novel inductive ZSL model termed sparse attribute propagation (SAP) by propagating attribute annotations to more unannotated images using sparse coding. This is followed by learning bidirectional projections between features and attributes for ZSL. An efficient solver is provided for such knowledge transfer with less supervision, together with rigorous theoretic analysis. With our SAP, we show that a ZSL training dataset can also be augmented by the abundant web images returned by image search engine, to further improve the model performance. Extensive experiments show that the proposed model achieves state-of-the-art results. © 2021, Springer Nature Switzerland AG.

关键词： Image enhancement

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Compression Image dataset Based on Multiple Matrix Product States

Compression Image Dataset Based on Multiple Matrix Product ...

引用

Future of Information and Communication Conference, FICC 2024

作者： Gao, Ze-Feng Liu, Peiyu Zhao, Wayne Xin Xie, Zhi-Yuan Wen, Ji-Rong Lu, Zhong-Yi Gaoling School of Artificial Intelligence Renmin University of China Beijing China Department of Physics Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

ISBN: (纸本)9783031539626

Large-scale datasets have made impressive progress in deep learning. However, storing datasets and training neural network models on large datasets have become increasingly expensive. In this paper, we present an effective dataset compression approach based on the matrix product states (short as MPS) and knowledge distillation. MPS can decompose image samples into a sequential product of tensors to achieve task-agnostic image compression by preserving the low-rank information of the images. Based on this property, we use multiple MPS to represent the image datasets samples. Meanwhile, we also designed a task-related component based on knowledge distillation to enhance the generality of the compressed dataset. Extensive experiments have demonstrated the effectiveness of the proposed approach in image datasets compression, especially obtaining better model performance (2.26% on average) than the best baseline method on the same compression ratio. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Distillation

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引用

10th International Conference on Learning Representations, ICLR 2022

作者： Guan, Jiechao Lu, Zhiwu School of Information Renmin University of China Beijing China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

Supposing the n training tasks and the new task are sampled from the same environment, traditional meta learning theory derives an error bound on the expected loss over the new task in terms of the empirical training loss, uniformly over the set of all hypothesis spaces. However, there is still little research on how the relatedness of these tasks can affect the full utilization of all mn training data (with m examples per task). In this paper, we propose to address this problem by defining a new notion of task relatedness according to the existence of the bijective transformation between two tasks. A novel generalization bound of O(1/√mn) for meta learning is thus derived by exploiting the proposed task relatedness. Moreover, when investigating a special branch of meta learning that involves representation learning with deep neural networks, we establish spectrally-normalized bounds for both classification and regression problems. Finally, we demonstrate that the relatedness requirement between two tasks is satisfied when the sample space possesses the completeness and separability properties, validating the rationality and applicability of our proposed task-relatedness measure. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

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Visual Prompt Tuning for Few-Shot Text Classification 29

Visual Prompt Tuning for Few-Shot Text Classification

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29th International Conference on Computational Linguistics, COLING 2022

作者： Wen, Jingyuan Luo, Yutian Fei, Nanyi Yang, Guoxing Lu, Zhiwu Jiang, Hao Jiang, Jie Cao, Zhao Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods China Huawei Poisson Lab Zhejiang Hangzhou China

Deploying large-scale pre-trained models in the prompt-tuning paradigm has demonstrated promising performance in few-shot learning. Particularly, vision-language pre-training models (VL-PTMs) have been intensively explored in various few-shot downstream tasks. However, most existing works only apply VL-PTMs to visual tasks like image classification, with few attempts being made on language tasks like text classification. In few-shot text classification, a feasible paradigm for deploying VL-PTMs is to align the input samples and their category names via the text encoders. However, it leads to the waste of visual information learned by the image encoders of VL-PTMs. To overcome this drawback, we propose a novel method named Visual Prompt Tuning (VPT). To our best knowledge, this method is the first attempt to deploy VL-PTM in few-shot text classification task. The main idea is to generate the image embeddings w.r.t. category names as visual prompt and then add them to the aligning process. Extensive experiments show that our VPT can achieve significant improvements under both zero-shot and few-shot settings. Importantly, our VPT even outperforms the most recent prompt-tuning methods on five public text classification datasets. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Classification (of information)

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Task-Sensitive Discriminative Mutual Attention Network for Few-Shot Learning 26

Task-Sensitive Discriminative Mutual Attention Network for F...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Xu, Baogui Xu, Chengjin Lu, Zhiwu Su, Bing Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods China International Digital Economy Academy China

ISBN: (纸本)9781643684369

Many few-shot image classification methods focus on learning a fixed feature space from sufficient samples of seen classes that can be readily transferred to unseen classes. For different tasks, the feature space is either kept the same or only adjusted by generating attentions to query samples. However, the discriminative channels and spatial parts for comparing different query and support images in different tasks are usually different. In this paper, we propose a task-sensitive discriminative mutual attention (TDMA) network to produce task-and-sample-specific features. For each task, TDMA first generates a discriminative task embedding that encodes the inter-class separability and within-class scatter, and then employs the task embedding to enhance discriminative channels respective to this task. Given a specific query and different support images, TDMA further incorporates the task embedding and long-range dependencies to locate the discriminative parts in the spatial dimension. Experimental results on miniImageNet, tieredImageNet and FC100 datasets show the effectiveness of the proposed model. © 2023 The Authors.

关键词： Embeddings

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WEAKLY-SUPERVISED ATTRIBUTE SEGMENTATION

WEAKLY-SUPERVISED ATTRIBUTE SEGMENTATION

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Liu, Guangzhen Lu, Zhiwu School of Information Renmin University of China Beijing China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

ISBN: (纸本)9781665438643

Semantic segmentation is a fundamental vision problem which aims to divide an image into non-overlapped regions and then assign them with predefined object labels. Despite its latest development (especially deep neural network-based), semantic segmentation is still far from comprehensive understanding of the visual world around us. For example, the obtained object labels are not fine-grained enough for solving more complicated vision tasks such as fine-grained retrieval with multiple attribute-based queries (e.g. the query 'a bird' along with the attribute 'has wing colour: grey'). In this paper, we thus choose to study a more challenging vision problem called attribute segmentation, which aims to localize attributes within the targeted object (e.g. bird) in a given image. Due to the high cost of collecting pixel-level attribute annotations, we focus on weakly-supervised attribute segmentation (WSAS) that utilizes only image-level attribute labels for model training. Note that this new WSAS problem degrades to the conventional weakly-supervised semantic segmentation (WSSS) problem when attribute labels are considered as object labels. To overcome the attribute label noise caused by image-level weak supervision, we propose a novel sparse learning method for solving the WSAS problem. Extensive experiments demonstrate that the proposed WSAS method significantly outperforms the state-of-the-art WSSS and attribute localization methods. © 2021 IEEE

关键词： Semantic Segmentation

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Sequential Recommendation with User Causal Behavior Discovery 39

Sequential Recommendation with User Causal Behavior Discover...

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Wang, Zhenlei Chen, Xu Zhou, Rui Dai, Quanyu Dong, Zhenhua Wen, Ji-Rong Renmin University of China Gaoling School of Artificial Intelligence Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Noah's Ark Lab Huawei Beijing China

ISBN: (纸本)9798350322279

The key of sequential recommendation lies in the accurate item correlation modeling. Previous models infer such information based on item co-occurrences, which may fail to capture the real causal relations, and impact the recommendation performance and explainability. In this paper, we equip sequential recommendation with a novel causal discovery module to capture causalities among user behaviors. Our general idea is firstly assuming a causal graph underlying item correlations, and then we learn the causal graph jointly with the sequential recommender model by fitting the real user behavior data. More specifically, in order to satisfy the causality requirement, the causal graph is regularized by a differentiable directed acyclic constraint. Considering that the number of items in recommender systems can be very large, we represent different items with a unified set of latent clusters, and the causal graph is defined on the cluster level, which enhances the model scalability and robustness. In addition, we provide theoretical analysis on the identifiability of the learned causal graph. To the best of our knowledge, this paper makes a first step towards combining sequential recommendation with causal discovery. For evaluating the recommendation performance, we implement our framework with different neural sequential architectures, and compare them with many state-of-the-art methods based on real-world datasets. Empirical studies manifest that our model can on average improve the performance by about 6.1% and 11.3% on F1 and NDCG, respectively. To evaluate the model explainability, we build a new dataset with human labeled explanations for both quantitative and qualitative analysis. © 2023 IEEE.

关键词： Recommender systems

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COMPLEX ACTION SEGMENTATION IN COMPRESSED VIDEOS

COMPLEX ACTION SEGMENTATION IN COMPRESSED VIDEOS

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Han, Hongfeng Yang, Guoxing Huo, Yuqi Lu, Zhiwu Wen, Ji-Rong School of Information Renmin University of China Beijing China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

ISBN: (纸本)9781665438643

Complex action segmentation aims to detect what actions and when they happen in fine-grained level from long videos. Despite the fact that videos are often stored in a compressed format (e.g., MPEG-4), most existing approaches are proposed to directly model raw RGB videos: when only compressed videos are accessible, they have to first decode these videos, which is very time-consuming. In this paper, by explicitly leveraging the 'compressed' characteristic of compressed videos, we are the first to address the challenging task of complex action segmentation in compressed videos. To extract meaningful representations for complex action segmentation, we introduce the GOP-Level Compressed features (Golec), which can be obtained directly from compressed videos without video decompression. Importantly, by taking GOPs as the atomic units of actions, our Golec representation is intrinsically suitable for fine-grained action segmentation. Moreover, to remedy the coarser motion vectors (compared with optical flows which are computed from raw frames) used in our Golec representation for capturing the temporal context, we propose a new Bi-path knowledge distillation strategy. Extensive experiments show the effectiveness of our Golec representation and the Bi-path strategy. Importantly, our proposed model for complex action detection not only runs 5.2 times faster but also achieves significantly better results than the state-of-the-art alternatives using raw videos. © 2021 IEEE Computer Society. All rights reserved.

关键词： Distillation

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Self-organized Hawkes Processes 1st

Self-organized Hawkes Processes

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1st CAAI International Conference on Artificial Intelligence, CICAI 2021

作者： Yuan, Shen Xu, Hongteng University of Electronic Science and Technology of China Chengdu China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

ISBN: (纸本)9783030930486

In this paper, we propose a novel self-organized Hawkes process (SOHP) to model complex event sequences based on extremely few observations. Motivated by the fact that the complicated global relations among events are often composed of simple local relations, we model the event sequences by a set of heterogeneous local Hawkes processes rather than a single Hawkes process. In the training phase, we learn the Hawkes processes with a self-organization mechanism, selecting training sequences adaptively for each Hawkes process by a bandit algorithm. The reward used in the algorithm is originally defined based on an optimal transport distance. Additionally, we leverage the superposition property of the Hawkes process to enhance the robustness of our algorithm to the data sparsity problem. We apply our SOHP method to sequential recommendation problems in the continuous-time domain and achieve encouraging performance in various datasets. The code is available at https://***/UESTC-DaShenZi/MHP. © 2021, Springer Nature Switzerland AG.

关键词： Continuous time systems

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