Xin Jin 金鑫

I am an Assistant Professor at the College of Info Science & Technology, Eastern Institute for Advanced Study (EIAS) where I cooperated with professor Wenjun Zeng (IEEE Fellow). Our group is to engage in state of the art research in computer vision and deep learning.

Previously, I was a Visiting Scholar of Learning and Vision (LV) Lab at the National University of Singapore where I was guided by professor Xinchao Wang, professor Jiashi Feng and professor Shuicheng Yan. I received Ph.D. degree from University of Science and Technology of China (USTC), under the supervision of Zhibo Chen. From Jan. 2019 to Jul. 2020, I worked at Intelligent Multimedia Group (IMG) in MSRA under the supervision of Cuiling Lan. From Sep. 2018 to Jan. 2019, I worked at KDDI Research, Inc. in Japan under the supervision of Jianfeng Xu.

If you are highly creative, have top research/coding skill and interested in joining us, please do not hesitate to send me (jinxin@eias.ac.cn) your CV and cc wenjunzeng@eias.ac.cn.

Email: jinxin@eias.ac.cn  /  Google Scholar  /  Github

I'm interested in image/video coding, person re-id, low-level vision and recently working on large model adaptation/generalization, prompt learning with CLIP model, etc.

In this work, by rethinking the inherent relationship between the face of target identity and its variants, we introduce a new pipeline of Generalized Manifold Adversarial Attack (GMAA) to achieve a better attack performance by expanding the attack range.

In this work, we propose a new efficient tuning approach for VLMs named Task Residual Tuning (TaskRes), which performs directly on the text-based classifier and explicitly decouples the prior knowledge of the pre-trained models and new knowledge regarding a target task.

In this work, we are the first to propose a novel training strategy for image restoration from the causality perspective, to improve the generalization ability of DNNs for unknown degradations.

In this work, we resort to data mixing to establish a deliberated domain bridging (DDB) for domain adaptive semantic segmentation. The joint distributions of source and target domains are aligned and interacted with each other in the intermediate space.

In this paper, we attempt to learn a kind of omnipotent feature that is both general (for AI tasks) and compact (for compression) for Image Coding for Machines (ICM). Considering self-supervised learning (SSL) improves feature generalization, we integrate it with the compression task to learn such features.

In this paper, we explore a novel learning scheme, termed as Learning wIth Recoverable Forgetting (LIRF), that explicitly handles the task- or sample-specific knowledge removal and recovery.

In this paper, we make attempt to the large-scale Unsupervised ReID and propose a “small data for big task” paradigm dubbed Meta Clustering Learning (MCL), which our method significantly saves computational cost while achieving a comparable or even better performance compared to prior works.

We design an Unsupervised Pre-training framework for ReID based on the contrastive learning (CL) pipeline, dubbed UP-ReID.

We focus on handling well the Cloth-Changing ReID problem under a more challenging setting, i.e., just from a single image, which enables high-efficiency and latency-free pedestrian identify for real-time surveillance applications.

We address the adversarial-based DA problem from a different perspective and design a simple yet effective adversarial paradigm in the form of a discriminator-free adversarial learning network (DALN), wherein the category classifier is reused as a discriminator.

We propose the Dual Prior Learning (DPL) method for blind image restoration by taking both image and distortion priors into account. DPL goes beyond DIP (deep image prior) by considering an additional step to explicitly learn the blended distortion prior.

We design a novel Style Normalization and Restitution module (SNR) to simultaneously ensure both high generalization and discrimination capability of the networks, and evaluate it on multiple vision tasks of classification, detection, segmentation, etc.

We propose an efficient optimization strategy named Re-enforceable Adversarial Domain Adaptation (RADA) which aims to re-energize the domain discriminator during the training by using dynamic domain labels.

This paper proposes a hybrid framework, Dense Interaction Learning (DenseIL), that takes the principal advantages of both CNN-based and Attention-based architectures to tackle video-based person re-ID difficulties.

The key to solving this more challenging real image super-resolution (RealSR) problem lies in learning feature representations that are both informative and content-aware. We propose an Omni-frequency Region-adaptive Network (OR-Net), here we call features of all low, middle and high frequencies omni-frequency features.

We introduce a global distance-distributions separation (GDS) constraint over the two distributions to encourage the clear separation of positive and negative samples from a global view.

We introduce the concept of Disentangled Feature Learning to achieve the feature-level divide-and-conquer of hybrid distortions for low-level enhancement.

We propose a simple yet effective Style Normalization and Restitution (SNR) module. Specifically, we filter out style variations (eg, illumination, color contrast) by Instance Normalization (IN). However, such a process inevitably removes discriminative information. We propose to distill identity-relevant feature from the removed information and restitute it to the network to ensure high discrimination.

We propose an effective Relation-Aware Global Attention (RGA) module which captures the global structural information for better attention learning.

We build a Semantics Aligning Network (SAN) which consists of a base network as encoder (SA-Enc) for re-ID, and a decoder (SA-Dec) for reconstructing the densely semantics aligned full texture image.

We propose exploiting the multi-shots of the same identity to guide the feature learning of each individual image. Specifically, we design an Uncertainty-aware Multi-shot Teacher-Student (UMTS) Network.

We show that the mean and variance shifts caused by full-spatial FN limit the image inpainting network training and we propose a spatial region-wise normalization named Region Normalization (RN) to overcome the limitation.

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