Author name cluster

Jia He

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11 papers

2 author rows

AIIM Journal 2025 Journal Article

A cell-interacting and multi-correcting method for automatic circulating tumor cells detection

Xuan Zhang
Rensheng Lai
Ling Bai
Jianxin Ji
Ruihao Qin
Lihong Jiang
Bin Meng
Ying Zhang

Sensitive detection of circulating tumor cells (CTCs) from peripheral blood can serve as an effective tool in the early diagnosis and prognosis of cancer. Many methods based on modern object detectors were proposed in recent years for automatic abnormal cells detection in slide images. Although the modes of these methods can also be applied to the CTCs detection, several practical difficulties lead to suboptimal performance of them, such as accurate capture of CTCs in a large number of mixed cells and identification of CTCs and CTC-like cells with similar visual characteristics. Here, we develop a new cell-interacting and multi-correcting detector called CMD, and apply H&E-stained slide images to detect CTCs automatically for the first time. Specifically, the proposed method incorporates two task-oriented novel modules: (1) a self-attention module for aggregating feature interactions between cells and allowing the model to pay more attention to key abnormal cells, (2) a hard sample mining sampler for progressively correcting predictions of cells with ambiguous classification boundaries. Experiments conducted on a multi-center dataset of 1247 annotated slide images confirm the superiority of our method over state-of-the-art cell detection methods. The results of ablation experiment part also prove the effectiveness of two modules. The source codes of this paper are available at https: //github. com/zx333445/CMD.

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AAAI Conference 2025 Conference Paper

HS-FPN: High Frequency and Spatial Perception FPN for Tiny Object Detection

Zican Shi
Jing Hu
Jie Ren
Hengkang Ye
Xuyang Yuan
Yan Ouyang
Jia He
Bo Ji

The introduction of Feature Pyramid Network (FPN) has significantly improved object detection performance. However, substantial challenges remain in detecting tiny objects, as their features occupy only a very small proportion of the feature maps. Although FPN integrates multi-scale features, it does not directly enhance or enrich the features of tiny objects. Furthermore, FPN lacks spatial perception ability. To address these issues, we propose a novel High Frequency and Spatial Perception Feature Pyramid Network (HS-FPN) with two innovative modules. First, we designed a high frequency perception module (HFP) that generates high frequency responses through high pass filters. These high frequency responses are used as mask weights from both spatial and channel perspectives to enrich and highlight the features of tiny objects in the original feature maps. Second, we developed a spatial dependency perception module (SDP) to capture the spatial dependencies that FPN lacks. Our experiments demonstrate that detectors based on HS-FPN exhibit competitive advantages over state-of-the-art models on the AI-TOD dataset for tiny object detection.

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ICLR Conference 2025 Conference Paper

Kronecker Mask and Interpretive Prompts are Language-Action Video Learners

Jingyi Yang
Zitong Yu
Xiuming Ni
Jia He
Hui Li

Contrastive language-image pretraining (CLIP) has significantly advanced image-based vision learning. A pressing topic subsequently arises: how can we effectively adapt CLIP to the video domain? Recent studies have focused on adjusting either the textual or visual branch of CLIP for action recognition. However, we argue that adaptations of both branches are crucial. In this paper, we propose a **C**ontrastive **L**anguage-**A**ction **V**ideo Learn**er** (**CLAVER**), designed to shift CLIP's focus from the alignment of static visual objects and concrete nouns to the alignment of dynamic action behaviors and abstract verbs. Specifically, we introduce a novel Kronecker mask attention for temporal modeling. Our tailored Kronecker mask offers three benefits 1) it expands the temporal receptive field for each token, 2) it serves as an effective spatiotemporal heterogeneity inductive bias, mitigating the issue of spatiotemporal homogenization, and 3) it can be seamlessly plugged into transformer-based models. Regarding the textual branch, we leverage large language models to generate diverse, sentence-level and semantically rich interpretive prompts of actions, which shift the model's focus towards the verb comprehension. Extensive experiments on various benchmarks and learning scenarios demonstrate the superiority and generality of our approach. The code will be available soon.

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JMLR Journal 2025 Journal Article

Orthogonal Bases for Equivariant Graph Learning with Provable k-WL Expressive Power

Jia He
Maggie Cheng

Graph neural network (GNN) models have been widely used for learning graph-structured data. Due to the permutation-invariant requirement of graph learning tasks, a basic element in graph neural networks is the invariant and equivariant linear layers. Previous work (Maron et al., 2019b) provided a maximal collection of invariant and equivariant linear layers and a simple deep neural network model, called k-IGN, for graph data defined on k-tuples of nodes. It is shown that the expressive power of k-IGN is at least as good as the k-Weisfeiler-Leman (WL) algorithm in graph isomorphism tests. However, the dimension of the invariant layer and equivariant layer is the k-th and 2k-th bell numbers, respectively. Such high complexity makes it computationally infeasible for k-IGNs with k >= 3. In this paper, we show that a much smaller dimension for the linear layers is sufficient to achieve the same expressive power. We provide two sets of orthogonal bases for the linear layers, each with only 3(2^k-1)-k basis elements. Based on these linear layers, we develop neural network models GNN-a and GNN-b and show that for the graph data defined on k-tuples of data, GNN-a and GNN-b achieve the expressive power of the k-WL algorithm and the (k+1)-WL algorithm in graph isomorphism tests, respectively. In molecular prediction tasks on benchmark datasets, we demonstrate that low-order neural network models consisting of the proposed linear layers achieve better performance than other neural network models. In particular, order-2 GNN-b and order-3 GNN-a both have 3-WL expressive power, but use a much smaller basis and hence much less computation time than known neural network models. [abs] [ pdf ][ bib ] &copy JMLR 2025. ( edit, beta )

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TCS Journal 2025 Journal Article

SAT-based bounded model checking for propositional projection temporal logic

Zhenhua Duan
Cong Tian
Nan Zhang
Chaofeng Yu
Mengfei Yang
Jia He

This paper presents a bounded model checking (BMC) approach for propositional projection temporal logic (PPTL). To this end, first PPTL is briefly introduced. Then, bounded semantics of PPTL is defined according to its semantics in logic theory. Further, a reduction method from BMC to SAT is given in detail. In addition, an example is presented to illustrate how the approach works. Finally, miniSAT is employed to solve the SAT based BMC problem by means of verifying RMS algorithm in detail. Our experience shows that SAT based BMC approach for PPTL proposed in the paper is useful and feasible.

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ECAI Conference 2024 Conference Paper

Generalized Face Anti-Spoofing via Finer Domain Partition and Disentangling Liveness-Irrelevant Factors

Jingyi Yang
Zitong Yu
Xiuming Ni
Jia He
Hui Li

Face anti-spoofing techniques based on domain generalization have recently been studied widely. Adversarial learning and meta-learning techniques have been adopted to learn domain-invariant representations. However, prior approaches often consider the dataset gap as the primary factor behind domain shifts. This perspective is not fine-grained enough to reflect the intrinsic gap among the data accurately. In our work, we redefine domains based on identities rather than datasets, aiming to disentangle liveness and identity attributes. We emphasize ignoring the adverse effect of identity shift, focusing on learning identity-invariant liveness representations through orthogonalizing liveness and identity features. To cope with style shifts, we propose Style Cross module to expand the stylistic diversity and Channel-wise Style Attention module to weaken the sensitivity to style shifts, aiming to learn robust liveness representations. Furthermore, acknowledging the asymmetry between live and spoof samples, we introduce a novel contrastive loss, Asymmetric Augmented Instance Contrast. Extensive experiments on four public datasets demonstrate that our method achieves state-of-the-art performance under cross-dataset and limited source dataset scenarios. Additionally, our method has good scalability when expanding diversity of identities. Code is available at https: //github. com/yjyddq/DLIF.

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AAAI Conference 2023 Conference Paper

Gradient-Adaptive Pareto Optimization for Constrained Reinforcement Learning

Zixian Zhou
Mengda Huang
Feiyang Pan
Jia He
Xiang Ao
Dandan Tu
Qing He

Constrained Reinforcement Learning (CRL) burgeons broad interest in recent years, which pursues maximizing long-term returns while constraining costs. Although CRL can be cast as a multi-objective optimization problem, it is still facing the key challenge that gradient-based Pareto optimization methods tend to stick to known Pareto-optimal solutions even when they yield poor returns (e.g., the safest self-driving car that never moves) or violate the constraints (e.g., the record-breaking racer that crashes the car). In this paper, we propose Gradient-adaptive Constrained Policy Optimization (GCPO for short), a novel Pareto optimization method for CRL with two adaptive gradient recalibration techniques. First, to find Pareto-optimal solutions with balanced performance over all targets, we propose gradient rebalancing which forces the agent to improve more on under-optimized objectives at every policy iteration. Second, to guarantee that the cost constraints are satisfied, we propose gradient perturbation that can temporarily sacrifice the returns for costs. Experiments on the SafetyGym benchmarks show that our method consistently outperforms previous CRL methods in reward while satisfying the constraints.

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IJCAI Conference 2022 Conference Paper

Learn Continuously, Act Discretely: Hybrid Action-Space Reinforcement Learning For Optimal Execution

Feiyang Pan
Tongzhe Zhang
Ling Luo
Jia He
Shuoling Liu

Optimal execution is a sequential decision-making problem for cost-saving in algorithmic trading. Studies have found that reinforcement learning (RL) can help decide the order-splitting sizes. However, a problem remains unsolved: how to place limit orders at appropriate limit prices? The key challenge lies in the ``continuous-discrete duality'' of the action space. On the one hand, the continuous action space using percentage changes in prices is preferred for generalization. On the other hand, the trader eventually needs to choose limit prices discretely due to the existence of the tick size, which requires specialization for every single stock with different characteristics (e. g. , the liquidity and the price range). So we need continuous control for generalization and discrete control for specialization. To this end, we propose a hybrid RL method to combine the advantages of both of them. We first use a continuous control agent to scope an action subset, then deploy a fine-grained agent to choose a specific limit price. Extensive experiments show that our method has higher sample efficiency and better training stability than existing RL algorithms and significantly outperforms previous learning-based methods for order execution.

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NeurIPS Conference 2020 Conference Paper

Trust the Model When It Is Confident: Masked Model-based Actor-Critic

Feiyang Pan
Jia He
Dandan Tu
Qing He

It is a popular belief that model-based Reinforcement Learning (RL) is more sample efficient than model-free RL, but in practice, it is not always true due to overweighed model errors. In complex and noisy settings, model-based RL tends to have trouble using the model if it does not know when to trust the model. In this work, we find that better model usage can make a huge difference. We show theoretically that if the use of model-generated data is restricted to state-action pairs where the model error is small, the performance gap between model and real rollouts can be reduced. It motivates us to use model rollouts only when the model is confident about its predictions. We propose Masked Model-based Actor-Critic (M2AC), a novel policy optimization algorithm that maximizes a model-based lower-bound of the true value function. M2AC implements a masking mechanism based on the model's uncertainty estimation to decide whether the model should be used or not. Consequently, the new algorithm tends to give robust policy improvements. Experiments on continuous control benchmarks demonstrate that M2AC has strong performance even when using long model rollouts in very noisy environments, and significantly outperforms previous state-of-the-art methods.

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IJCAI Conference 2017 Conference Paper

Nonlinear Maximum Margin Multi-View Learning with Adaptive Kernel

Jia He
Changying Du
Changde Du
Fuzhen Zhuang
Qing He
Guoping Long

Existing multi-view learning methods based on kernel function either require the user to select and tune a single predefined kernel or have to compute and store many Gram matrices to perform multiple kernel learning. Apart from the huge consumption of manpower, computation and memory resources, most of these models seek point estimation of their parameters, and are prone to overfitting to small training data. This paper presents an adaptive kernel nonlinear max-margin multi-view learning model under the Bayesian framework. Specifically, we regularize the posterior of an efficient multi-view latent variable model by explicitly mapping the latent representations extracted from multiple data views to a random Fourier feature space where max-margin classification constraints are imposed. Assuming these random features are drawn from Dirichlet process Gaussian mixtures, we can adaptively learn shift-invariant kernels from data according to Bochners theorem. For inference, we employ the data augmentation idea for hinge loss, and design an efficient gradient-based MCMC sampler in the augmented space. Having no need to compute the Gram matrix, our algorithm scales linearly with the size of training set. Extensive experiments on real-world datasets demonstrate that our method has superior performance.

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IJCAI Conference 2016 Conference Paper

Online Bayesian Max-Margin Subspace Multi-View Learning

Jia He
Changying Du
Fuzhen Zhuang
Xin Yin
Qing He
Guoping Long

Last decades have witnessed a number of studies devoted to multi-view learning algorithms, however, few efforts have been made to handle online multi-view learning scenarios. In this paper, we propose an online Bayesian multi-view learning algorithm to learn predictive subspace with max-margin principle. Specifically, we first define the latent margin loss for classification in the subspace, and then cast the learning problem into a variational Bayesian framework by exploiting the pseudo-likelihood and data augmentation idea. With the variational approximate posterior inferred from the past samples, we can naturally combine historical knowledge with new arrival data, in a Bayesian Passive-Aggressive style. Experiments on various classification tasks show that our model have superior performance.

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