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Chengjiang Long

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8 papers

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JBHI Journal 2025 Journal Article

Robust Multi-Contrast MRI Medical Image Translation via Knowledge Distillation and Adversarial Attack

Xujie Zhao
Feng Liang
Chengjiang Long
Zhiyong Yuan
Jianhui Zhao

Medical image translation is of great value but is very difficult due to the requirement with style change of noise pattern and anatomy invariance of image content. Various deep learning methods like the mainstream GAN, Transformer and Diffusion models have been developed to learn the multi-modal mapping to obtain the translated images, but the results from the generator are still far from being perfect for medical images. In this paper, we propose a robust multi-contrast translation framework for MRI medical images with knowledge distillation and adversarial attack, which can be integrated with any generator. The additional refinement network consists of teacher and student modules with similar structures but different inputs. Unlike the existing knowledge distillation works, our teacher module is designed as a registration network with more inputs to better learn the noise distribution well and further refine the translated results in the training stage. The knowledge is then well distilled to the student module to ensure that better translation results are generated. We also introduce an adversarial attack module before the generator. Such a black-box attacker can generate meaningful perturbations and adversarial examples throughout the training process. Our model has been tested on two public MRI medical image datasets considering different types and levels of perturbations, and each designed module is verified by the ablation study. The extensive experiments and comparison with SOTA methods have strongly demonstrated our model’s superiority of refinement and robustness.

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AAAI Conference 2024 Conference Paper

CoreRec: A Counterfactual Correlation Inference for Next Set Recommendation

Kexin Li
Chengjiang Long
Shengyu Zhang
Xudong Tang
Zhichao Zhai
Kun Kuang
Jun Xiao

Next set recommendation aims to predict the items that are likely to be bought in the next purchase. Central to this endeavor is the task of capturing intra-set and cross-set correlations among items. However, the modeling of cross-set correlations poses challenges due to specific issues. Primarily, these correlations are often implicit, and the prevailing approach of establishing an indiscriminate link across the entire set of objects neglects factors like purchase frequency and correlations between purchased items. Such hastily formed connections across sets introduce substantial noise. Additionally, the preeminence of high-frequency items in numerous sets could potentially overshadow and distort correlation modeling with respect to low-frequency items. Thus, we devoted to mitigating misleading inter-set correlations. With a fresh perspective rooted in causality, we delve into the question of whether correlations between a particular item and items from other sets should be relied upon for item representation learning and set prediction. Technically, we introduce the Counterfactual Correlation Inference framework for next set recommendation, denoted as CoreRec. This framework establishes a counterfactual scenario in which the recommendation model impedes cross-set correlations to generate intervened predictions. By contrasting these intervened predictions with the original ones, we gauge the causal impact of inter-set neighbors on set prediction—essentially assessing whether they contribute to spurious correlations. During testing, we introduce a post-trained switch module that selects between set-aware item representations derived from either the original or the counterfactual scenarios. To validate our approach, we extensively experiment using three real-world datasets, affirming both the effectiveness of CoreRec and the cogency of our analytical approach.

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NeurIPS Conference 2024 Conference Paper

Incorporating Test-Time Optimization into Training with Dual Networks for Human Mesh Recovery

Yongwei Nie
Mingxian Fan
Chengjiang Long
Qing Zhang
Jian Zhu
Xuemiao Xu

Human Mesh Recovery (HMR) is the task of estimating a parameterized 3D human mesh from an image. There is a kind of methods first training a regression model for this problem, then further optimizing the pretrained regression model for any specific sample individually at test time. However, the pretrained model may not provide an ideal optimization starting point for the test-time optimization. Inspired by meta-learning, we incorporate the test-time optimization into training, performing a step of test-time optimization for each sample in the training batch before really conducting the training optimization over all the training samples. In this way, we obtain a meta-model, the meta-parameter of which is friendly to the test-time optimization. At test time, after several test-time optimization steps starting from the meta-parameter, we obtain much higher HMR accuracy than the test-time optimization starting from the simply pretrained regression model. Furthermore, we find test-time HMR objectives are different from training-time objectives, which reduces the effectiveness of the learning of the meta-model. To solve this problem, we propose a dual-network architecture that unifies the training-time and test-time objectives. Our method, armed with meta-learning and the dual networks, outperforms state-of-the-art regression-based and optimization-based HMR approaches, as validated by the extensive experiments. The codes are available at https: //github. com/fmx789/Meta-HMR.

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AAAI Conference 2022 Conference Paper

Complementary Attention Gated Network for Pedestrian Trajectory Prediction

Jinghai Duan
Le Wang
Chengjiang Long
Sanping Zhou
Fang Zheng
Liushuai Shi
Gang Hua

Pedestrian trajectory prediction is crucial in many practical applications due to the diversity of pedestrian movements, such as social interactions and individual motion behaviors. With similar observable trajectories and social environments, different pedestrians may make completely different future decisions. However, most existing methods only focus on the frequent modal of the trajectory and thus are difficult to generalize to the peculiar scenario, which leads to the decline of the multimodal fitting ability when facing similar scenarios. In this paper, we propose a complementary attention gated network (CAGN) for pedestrian trajectory prediction, in which a dual-path architecture including normal and inverse attention is proposed to capture both frequent and peculiar modals in spatial and temporal patterns, respectively. Specifically, a complementary block is proposed to guide normal and inverse attention, which are then be summed with learnable weights to get attention features by a gated network. Finally, multiple trajectory distributions are estimated based on the fused spatio-temporal attention features due to the multimodality of future trajectory. Experimental results on benchmark datasets, i. e. , the ETH, and the UCY, demonstrate that our method outperforms state-of-the-art methods by 13. 8% in Average Displacement Error (ADE) and 10. 4% in Final Displacement Error (FDE). Code will be available at https: //github. com/jinghaiD/CAGN

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AAAI Conference 2022 Conference Paper

CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation

Yu Qiao
Jincheng Zhu
Chengjiang Long
Zeyao Zhang
Yuxin Wang
Zhenjun Du
Xin Yang

Acquiring the most representative examples via active learning (AL) can benefit many data-dependent computer vision tasks by minimizing efforts of image-level or pixel-wise annotations. In this paper, we propose a novel Collaborative Panoptic-Regional Active Learning framework (CPRAL) to address the semantic segmentation task. For a small batch of images initially sampled with pixel-wise annotations, we employ panoptic information to initially select unlabeled samples. Considering the class imbalance in the segmentation dataset, we import a Regional Gaussian Attention module (RGA) to achieve semantics-biased selection. The subset is highlighted by vote entropy and then attended by Gaussian kernels to maximize the biased regions. We also propose a Contextual Labels Extension (CLE) to boost regional annotations with contextual attention guidance. With the collaboration of semantics-agnostic panoptic matching and regionbiased selection and extension, our CPRAL can strike a balance between labeling efforts and performance and compromise the semantics distribution. We perform extensive experiments on Cityscapes and BDD10K datasets and show that CPRAL outperforms the cutting-edge methods with impressive results and less labeling proportion.

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AAAI Conference 2022 Conference Paper

Social Interpretable Tree for Pedestrian Trajectory Prediction

Liushuai Shi
Le Wang
Chengjiang Long
Sanping Zhou
Fang Zheng
Nanning Zheng
Gang Hua

Understanding the multiple socially-acceptable future behaviors is an essential task for many vision applications. In this paper, we propose a tree-based method, termed as Social Interpretable Tree (SIT), to address this multi-modal prediction task, where a hand-crafted tree is built depending on the prior information of observed trajectory to model multiple future trajectories. Specifically, a path in the tree from the root to leaf represents an individual possible future trajectory. SIT employs a coarse-to-fine optimization strategy, in which the tree is first built by high-order velocity to balance the complexity and coverage of the tree and then optimized greedily to encourage multimodality. Finally, a teacher-forcing refining operation is used to predict the final fine trajectory. Compared with prior methods which leverage implicit latent variables to represent possible future trajectories, the path in the tree can explicitly explain the rough moving behaviors (e. g. , go straight and then turn right), and thus provides better interpretability. Despite the hand-crafted tree, the experimental results on ETH-UCY and Stanford Drone datasets demonstrate that our method is capable of matching or exceeding the performance of state-of-the-art methods. Interestingly, the experiments show that the raw built tree without training outperforms many prior deep neural network based approaches. Meanwhile, our method presents sufficient flexibility in longterm prediction and different best-of-K predictions.

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AAAI Conference 2021 Conference Paper

A Hybrid Attention Mechanism for Weakly-Supervised Temporal Action Localization

Ashraful Islam
Chengjiang Long
Richard Radke

Weakly supervised temporal action localization is a challenging vision task due to the absence of ground-truth temporal locations of actions in the training videos. With only videolevel supervision during training, most existing methods rely on a Multiple Instance Learning (MIL) framework to predict the start and end frame of each action category in a video. However, the existing MIL-based approach has a major limitation of only capturing the most discriminative frames of an action, ignoring the full extent of an activity. Moreover, these methods cannot model background activity effectively, which plays an important role in localizing foreground activities. In this paper, we present a novel framework named HAM-Net with a hybrid attention mechanism which includes temporal soft, semi-soft and hard attentions to address these issues. Our temporal soft attention module, guided by an auxiliary background class in the classification module, models the background activity by introducing an “action-ness” score for each video snippet. Moreover, our temporal semi-soft and hard attention modules, calculating two attention scores for each video snippet, help to focus on the less discriminative frames of an action to capture the full action boundary. Our proposed approach outperforms recent state-of-the-art methods by at least 2. 2% mAP at IoU threshold 0. 5 on the THU- MOS14 dataset, and by at least 1. 3% mAP at IoU threshold 0. 75 on the ActivityNet1. 2 dataset.

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AAAI Conference 2020 Conference Paper

RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal

Ling Zhang
Chengjiang Long
Xiaolong Zhang
Chunxia Xiao

Residual images and illumination estimation have been proved very helpful in image enhancement. In this paper, we propose a general and novel framework RIS-GAN which explores residual and illumination with Generative Adversarial Networks for shadow removal. Combined with the coarse shadow-removal image, the estimated negative residual images and inverse illumination maps can be used to generate indirect shadow-removal images to reﬁne the coarse shadow-removal result to the ﬁne shadow-free image in a coarse-to-ﬁne fashion. Three discriminators are designed to distinguish whether the predicted negative residual images, shadow-removal images, and the inverse illumination maps are real or fake jointly compared with the corresponding ground-truth information. To our best knowledge, we are the ﬁrst one to explore residual and illumination for shadow removal. We evaluate our proposed method on two benchmark datasets, i. e. , SRD and ISTD, and the extensive experiments demonstrate that our proposed method achieves the superior performance to state-of-the-arts, although we have no particular shadow-aware components designed in our generators.

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