Author name cluster

Jinglin Xu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

11 papers

2 author rows

AAAI Conference 2026 Conference Paper

MCMoE: Completing Missing Modalities with Mixture of Experts for Incomplete Multimodal Action Quality Assessment

Huangbiao Xu
Huanqi Wu
Xiao Ke
Junyi Wu
Rui Xu
Jinglin Xu

Multimodal Action Quality Assessment (AQA) has recently emerged as a promising paradigm. By leveraging complementary information across shared contextual cues, it enhances the discriminative evaluation of subtle intra-class variations in highly similar action sequences. However, partial modalities are frequently unavailable at the inference stage in reality. The absence of any modality often renders existing multimodal models inoperable. Furthermore, it triggers catastrophic performance degradation due to interruptions in cross-modal interactions. To address this issue, we propose a novel Missing Completion Framework with Mixture of Experts (MCMoE) that unifies unimodal and joint representation learning in single-stage training. Specifically, we propose an adaptive gated modality generator that dynamically fuses available information to reconstruct missing modalities. We then design modality experts to learn unimodal knowledge and dynamically mix the knowledge of all experts to extract cross-modal joint representations. With a mixture of experts, missing modalities are further refined and complemented. Finally, in the training phase, we mine the complete multimodal features and unimodal expert knowledge to guide modality generation and generation-based joint representation extraction. Extensive experiments demonstrate that our MCMoE achieves state-of-the-art results in both complete and incomplete multimodal learning on three public AQA benchmarks.

PDF Details DOI

AAAI Conference 2026 Conference Paper

TSPO: Temporal Sampling Policy Optimization for Long-form Video Language Understanding

Canhui Tang
Zifan Han
Hongbo Sun
Sanping Zhou
Xuchong Zhang
Xin Wei
Ye Yuan
Huayu Zhang

Multimodal Large Language Models (MLLMs) have demonstrated significant progress in vision-language tasks, yet they still face challenges when processing long-duration video inputs. The limitation arises from MLLMs' context limit and training costs, necessitating sparse frame sampling before feeding videos into MLLMs. However, building a trainable sampling method remains challenging due to the unsupervised and non-differentiable nature of sparse frame sampling in Video-MLLMs. To address these problems, we propose Temporal Sampling Policy Optimization (**TSPO**), advancing MLLMs' long-form video-language understanding via reinforcement learning. Specifically, we first propose a trainable event-aware temporal agent, which captures event-query correlation for performing probabilistic keyframe selection. Then, we propose the TSPO reinforcement learning paradigm, which models keyframe selection and language generation as a joint decision-making process, enabling end-to-end group relative optimization for the temporal sampling policy. Furthermore, we propose a dual-style long video training data construction pipeline, balancing comprehensive temporal understanding and key segment localization. Finally, we incorporate rule-based answering accuracy and temporal locating reward mechanisms to optimize the temporal sampling policy. Comprehensive experiments show that our TSPO achieves state-of-the-art performance across multiple long video understanding benchmarks, and shows transferable ability across different cutting-edge Video-MLLMs.

PDF Details DOI

ICLR Conference 2025 Conference Paper

Analyzing and Boosting the Power of Fine-Grained Visual Recognition for Multi-modal Large Language Models

Hulingxiao He
Geng Li 0003
Zijun Geng
Jinglin Xu
Yuxin Peng 0001

Multi-modal large language models (MLLMs) have shown remarkable abilities in various visual understanding tasks. However, MLLMs still struggle with fine-grained visual recognition (FGVR), which aims to identify subordinate-level categories from images. This can negatively impact more advanced capabilities of MLLMs, such as object-centric visual question answering and reasoning. In our study, we revisit three quintessential capabilities of MLLMs for FGVR, including object information extraction, category knowledge reserve, object-category alignment, and position of the root cause as a misalignment problem. To address this issue, we present Finedefics, an MLLM that enhances the model's FGVR capability by incorporating informative attribute descriptions of objects into the training phase. We employ contrastive learning on object-attribute pairs and attribute-category pairs simultaneously and use examples from similar but incorrect categories as hard negatives, naturally bringing representations of visual objects and category names closer. Extensive evaluations across multiple popular FGVR datasets demonstrate that Finedefics outperforms existing MLLMs of comparable parameter sizes, showcasing its remarkable efficacy. The code is available at [https://github.com/PKU-ICST-MIPL/Finedefics_ICLR2025](https://github.com/PKU-ICST-MIPL/Finedefics_ICLR2025).

Details

ICLR Conference 2025 Conference Paper

MAI: A Multi-turn Aggregation-Iteration Model for Composed Image Retrieval

Yanzhe Chen
Zhiwen Yang
Jinglin Xu
Yuxin Peng 0001

Multi-Turn Composed Image Retrieval (MTCIR) addresses a real-world scenario where users iteratively refine retrieval results by providing additional information until a target meeting all their requirements is found. Existing methods primarily achieve MTCIR through a "multiple single-turn" paradigm, wherein methods incorrectly converge on shortcuts that only utilize the most recent turn's image, ignoring attributes from historical turns. Consequently, retrieval failures occur when modification requests involve historical information. We argue that explicitly incorporating historical information into the modified text is crucial to addressing this issue. To this end, we build a new retrospective-based MTCIR dataset, **FashionMT**, wherein modification demands are highly associated with historical turns. We also propose a Multi-turn Aggregation-Iteration (**MAI**) model, emphasizing efficient aggregation of multimodal semantics and optimization of information propagation in multi-turn retrieval. Specifically, we propose a new Two-stage Semantic Aggregation (TSA) paradigm coupled with a Cyclic Combination Loss (CCL), achieving improved semantic consistency and modality alignment by progressively interacting the reference image with its caption and the modified text. In addition, we design a Multi-turn Iterative Optimization (MIO) mechanism that dynamically selects representative tokens and reduces redundancy during multi-turn iterations. Extensive experiments demonstrate that the proposed MAI model achieves substantial improvements over state-of-the-art methods.

Details

IJCAI Conference 2024 Conference Paper

FineFMPL: Fine-grained Feature Mining Prompt Learning for Few-Shot Class Incremental Learning

Hongbo Sun
Jiahuan Zhou
Xiangteng He
Jinglin Xu
Yuxin Peng

Few-Shot Class Incremental Learning (FSCIL) aims to continually learn new classes with few training samples without forgetting already learned old classes. Existing FSCIL methods generally fix the backbone network in incremental sessions to achieve a balance between suppressing forgetting old classes and learning new classes. However, the fixed backbone network causes insufficient learning of new classes from a few samples. Benefiting from the powerful visual and textual understanding ability of Vision-Language (VL) pre-training models, we propose a Fine-grained Feature Mining Prompt Learning (FineFMPL) approach to adapt the VL model to FSCIL, which comprehensively learns and memorizes fine-grained discriminative information of emerging classes. Concretely, the visual probe prompt is firstly proposed to guide the image encoder of VL model to extract global-level coarse-grained features and object-level fine-grained features, and visual prototypes are preserved based on image patch significance, which contains the discriminative characteristics exclusive to the class. Secondly, the textual context prompt is constructed by cross-modal mapping of visual prototypes, feeding into the text encoder of VL model to memorize the class information as textual prototypes. Finally, integrating visual and textual prototypes based on fine-grained feature mining into the model improves the recognition performance of all classes in FSCIL. Extensive experiments on three benchmark datasets demonstrate that our FineFMPL achieves new state-of-the-art. The code is available at https: //github. com/PKU-ICST-MIPL/FineFMPL_IJCAI2024.

PDF Details DOI

AAAI Conference 2020 Conference Paper

Deep Embedded Complementary and Interactive Information for Multi-View Classification

Jinglin Xu
Wenbin Li
Xinwang Liu
Dingwen Zhang
Ji Liu
Junwei Han

Multi-view classiﬁcation optimally integrates various features from different views to improve classiﬁcation tasks. Though most of the existing works demonstrate promising performance in various computer vision applications, we observe that they can be further improved by sufﬁciently utilizing complementary view-speciﬁc information, deep interactive information between different views, and the strategy of fusing various views. In this work, we propose a novel multi-view learning framework that seamlessly embeds various view-speciﬁc information and deep interactive information and introduces a novel multi-view fusion strategy to make a joint decision during the optimization for classiﬁcation. Speciﬁcally, we utilize different deep neural networks to learn multiple view-speciﬁc representations, and model deep interactive information through a shared interactive network using the cross-correlations between attributes of these representations. After that, we adaptively integrate multiple neural networks by ﬂexibly tuning the power exponent of weight, which not only avoids the trivial solution of weight but also provides a new approach to fuse outputs from different deterministic neural networks. Extensive experiments on several public datasets demonstrate the rationality and effectiveness of our method.

PDF Details

IJCAI Conference 2020 Conference Paper

Joint Multi-view 2D Convolutional Neural Networks for 3D Object Classification

Jinglin Xu
Xiangsen Zhang
Wenbin Li
Xinwang Liu
Junwei Han

Three-dimensional (3D) object classification is widely involved in various computer vision applications, e. g. , autonomous driving, simultaneous localization and mapping, which has attracted lots of attention in the committee. However, solving 3D object classification by directly employing the 3D convolutional neural networks (CNNs) generally suffers from high computational cost. Besides, existing view-based methods cannot better explore the content relationships between views. To this end, this work proposes a novel multi-view framework by jointly using multiple 2D-CNNs to capture discriminative information with relationships as well as a new multi-view loss fusion strategy, in an end-to-end manner. Specifically, we utilize multiple 2D views of a 3D object as input and integrate the intra-view and inter-view information of each view through the view-specific 2D-CNN and a series of modules (outer product, view pair pooling, 1D convolution, and fully connected transformation). Furthermore, we design a novel view ensemble mechanism that selects several discriminative and informative views to jointly infer the category of a 3D object. Extensive experiments demonstrate that the proposed method is able to outperform current state-of-the-art methods on 3D object classification. More importantly, this work provides a new way to improve 3D object classification from the perspective of fully utilizing well-established 2D-CNNs.

PDF Details DOI

AAAI Conference 2019 Conference Paper

Distribution Consistency Based Covariance Metric Networks for Few-Shot Learning

Wenbin Li
Jinglin Xu
Jing Huo
Lei Wang
Yang Gao
Jiebo Luo

Few-shot learning aims to recognize new concepts from very few examples. However, most of the existing few-shot learning methods mainly concentrate on the first-order statistic of concept representation or a fixed metric on the relation between a sample and a concept. In this work, we propose a novel end-to-end deep architecture, named Covariance Metric Networks (CovaMNet). The CovaMNet is designed to exploit both the covariance representation and covariance metric based on the distribution consistency for the few-shot classification tasks. Specifically, we construct an embedded local covariance representation to extract the second-order statistic information of each concept and describe the underlying distribution of this concept. Upon the covariance representation, we further define a new deep covariance metric to measure the consistency of distributions between query samples and new concepts. Furthermore, we employ the episodic training mechanism to train the entire network in an end-to-end manner from scratch. Extensive experiments in two tasks, generic few-shot image classification and fine-grained fewshot image classification, demonstrate the superiority of the proposed CovaMNet. The source code can be available from https: //github. com/WenbinLee/CovaMNet. git.

PDF Details

IJCAI Conference 2017 Conference Paper

Feature Selection via Scaling Factor Integrated Multi-Class Support Vector Machines

Jinglin Xu
Feiping Nie
Junwei Han

In data mining, we often encounter high dimensional and noisy features, which may not only increase the load of computational resources but also result in the problem of model overfitting. Feature selection is often adopted to address this issue. In this paper, we propose a novel feature selection method based on multi-class SVM, which introduces the scaling factor with a flexible parameter to renewedly adjust the distribution of feature weights and select the most discriminative features. Concretely, the proposed method designs a scaling factor with p/2 power to control the distribution of weights adaptively and search optimal sparsity of weighting matrix. In addition, to solve the proposed model, we provide an alternative and iterative optimization method. It not only makes solutions of weighting matrix and scaling factor independently, but also provides a better way to address the problem of solving L2, 0-norm. Comprehensive experiments are conducted on six datasets to demonstrate that this work can obtain better performance compared with a number of existing state-of-the-art multi-class feature selection methods.

PDF Details

IJCAI Conference 2017 Conference Paper

Multi-view Feature Learning with Discriminative Regularization

Jinglin Xu
Junwei Han
Feiping Nie

More and more multi-view data which can capture rich information from heterogeneous features are widely used in real world applications. How to integrate different types of features, and how to learn low dimensional and discriminative information from high dimensional data are two main challenges. To address these challenges, this paper proposes a novel multi-view feature learning framework, which is regularized by discriminative information and obtains a feature learning model that contains multiple discriminative feature weighting matrices for different views, and then yields multiple low dimensional features used for subsequent multi-view clustering. To optimize the formulated objective function, we transform the proposed framework into a trace optimization problem which obtains the global solution in a closed form. Experimental evaluations on four widely used datasets and comparisons with a number of state-of-the-art multi-view clustering algorithms demonstrate the superiority of the proposed work.

PDF Details

IJCAI Conference 2016 Conference Paper

Robust and Sparse Fuzzy K-Means Clustering

Jinglin Xu
Junwei Han
Kai Xiong
Feiping Nie

The partition-based clustering algorithms, like K-Means and fuzzy K-Means, are most widely and successfully used in data mining in the past decades. In this paper, we present a robust and sparse fuzzy K-Means clustering algorithm, an extension to the standard fuzzy K-Means algorithm by incorporating a robust function, rather than the square data fitting term, to handle outliers. More importantly, combined with the concept of sparseness, the new algorithm further introduces a penalty term to make the object-clusters membership of each sample have suitable sparseness. Experimental results on benchmark datasets demonstrate that the proposed algorithm not only can ensure the robustness of such soft clustering algorithm in real world applications, but also can avoid the performance degradation by considering the membership sparsity.

PDF Details