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Lin Xiong

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NeurIPS Conference 2023 Conference Paper

RGMIL: Guide Your Multiple-Instance Learning Model with Regressor

Zhaolong Du
Shasha Mao
Yimeng Zhang
Shuiping Gou
Licheng Jiao
Lin Xiong

In video analysis, an important challenge is insufficient annotated data due to the rare occurrence of the critical patterns, and we need to provide discriminative frame-level representation with limited annotation in some applications. Multiple Instance Learning (MIL) is suitable for this scenario. However, many MIL models paid attention to analyzing the relationships between instance representations and aggregating them, but neglecting the critical information from the MIL problem itself, which causes difficultly achieving ideal instance-level performance compared with the supervised model. To address this issue, we propose the $\textbf{\textit{Regressor-Guided MIL network} (RGMIL)}$, which effectively produces discriminative instance-level representations in a general multi-classification scenario. In the proposed method, we make full use of the $\textit{regressor}$ through our newly introduced $\textit{aggregator}$, $\textbf{\textit{Regressor-Guided Pooling} (RGP)}$. RGP focuses on simulating the correct inference process of humans while facing similar problems without introducing new parameters, and the MIL problem can be accurately described through the critical information from the $\textit{regressor}$ in our method. In experiments, RGP shows dominance on more than 20 MIL benchmark datasets, with the average bag-level classification accuracy close to 1. We also perform a series of comprehensive experiments on the MMNIST dataset. Experimental results illustrate that our $\textit{aggregator}$ outperforms existing methods under different challenging circumstances. Instance-level predictions are even possible under the guidance of RGP information table in a long sequence. RGMIL also presents comparable instance-level performance with S-O-T-A supervised models in complicated applications. Statistical results demonstrate the assumption that a MIL model can compete with a supervised model at the instance level, as long as a structure that accurately describes the MIL problem is provided. The codes are available on $\url{https: //github. com/LMBDA-design/RGMIL}$.

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EAAI Journal 2021 Journal Article

Local–Global Attentive Adaptation for Object Detection

Dan Zhang
Jingjing Li
Xingpeng Li
Zhekai Du
Lin Xiong
Mao Ye

Adversarial adaptive methods have been proven to be useful for domain transfer in many fields such as image recognition and semantic segmentation, etc However, for object detection, since each image could have different combinations of objects, brutally aligning all the images without considering their transferability may cause the notorious phenomena named ‘negative transfer’. On the other hand, strong matching the local-level features makes sense, as it not only reduces the discrepancy between different domain distributions, but preserves the category-level semantic information. However, it is hard to markedly achieve domain invariance using a simple adversarial adaptive method. In this work, we propose an effective method termed Local–Global Attentive Adaptation for object Detection (LGAAD). Our method can alleviate the negative transfer caused by improper global alignments through leveraging an adaptively and dynamically weighted transferability to highlight the more transferable images. Furthermore, the proposed method also achieves the strong matching between two domains at local-level features to alleviate the cross-domain discrepancy by using the attention mechanism after multiple local discriminators. Additionally, we also consider the domain impacts of instance-wise features and backgrounds in images with large domain divergence, a non-negligible factor for improving the domain adaptive detection model performance. Extensive experiments of various domain shift scenarios show that our method exceeds the state-of-the-art results on several public datasets. Furthermore, qualitative visualization and ablation analyzes can demonstrate the validity of our approach for attending the interested regions and instances on domain adaptation.

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IJCAI Conference 2018 Conference Paper

3D-Aided Deep Pose-Invariant Face Recognition

Jian Zhao
Lin Xiong
Yu Cheng
Yi Cheng
Jianshu Li
Li Zhou
Yan Xu
Jayashree Karlekar

Learning from synthetic faces, though perhaps appealing for high data efficiency, may not bring satisfactory performance due to the distribution discrepancy of the synthetic and real face images. To mitigate this gap, we propose a 3D-Aided Deep Pose-Invariant Face Recognition Model (3D-PIM), which automatically recovers realistic frontal faces from arbitrary poses through a 3D face model in a novel way. Specifically, 3D-PIM incorporates a simulator with the aid of a 3D Morphable Model (3D MM) to obtain shape and appearance prior for accelerating face normalization learning, requiring less training data. It further leverages a global-local Generative Adversarial Network (GAN) with multiple critical improvements as a refiner to enhance the realism of both global structures and local details of the face simulator’s output using unlabelled real data only, while preserving the identity information. Qualitative and quantitative experiments on both controlled and in-the-wild benchmarks clearly demonstrate superiority of the proposed model over state-of-the-arts.

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NeurIPS Conference 2017 Conference Paper

Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis

Jian Zhao
Lin Xiong
Panasonic Karlekar Jayashree
Jianshu Li
Fang Zhao
Zhecan Wang
Panasonic Sugiri Pranata
Panasonic Shengmei Shen

Synthesizing realistic profile faces is promising for more efficiently training deep pose-invariant models for large-scale unconstrained face recognition, by populating samples with extreme poses and avoiding tedious annotations. However, learning from synthetic faces may not achieve the desired performance due to the discrepancy between distributions of the synthetic and real face images. To narrow this gap, we propose a Dual-Agent Generative Adversarial Network (DA-GAN) model, which can improve the realism of a face simulator's output using unlabeled real faces, while preserving the identity information during the realism refinement. The dual agents are specifically designed for distinguishing real v. s. fake and identities simultaneously. In particular, we employ an off-the-shelf 3D face model as a simulator to generate profile face images with varying poses. DA-GAN leverages a fully convolutional network as the generator to generate high-resolution images and an auto-encoder as the discriminator with the dual agents. Besides the novel architecture, we make several key modifications to the standard GAN to preserve pose and texture, preserve identity and stabilize training process: (i) a pose perception loss; (ii) an identity perception loss; (iii) an adversarial loss with a boundary equilibrium regularization term. Experimental results show that DA-GAN not only presents compelling perceptual results but also significantly outperforms state-of-the-arts on the large-scale and challenging NIST IJB-A unconstrained face recognition benchmark. In addition, the proposed DA-GAN is also promising as a new approach for solving generic transfer learning problems more effectively.

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