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32nd ACM International Conference on Multimedia, MM 2024

作者： Guo, Pinxue Li, Wanyun Huang, Hao Hong, Lingyi Zhou, Xinyu Chen, Zhaoyu Li, Jinglun Jiang, Kaixun Zhang, Wei Zhang, Wenqiang Shanghai Engineering Research Center of Ai & Robotics Academy for Engineering & Technology Fudan University China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Engineering Research Center of Ai & Robotics Ministry of Education Academy for Engineering & Technology China

ISBN: (纸本)9798400706868

Multi-modal Video Object Segmentation (VOS), including RGB-Thermal, RGB-Depth, and RGB-Event, has garnered attention due to its capability to address challenging scenarios where traditional VOS methods struggle, such as extreme illumination, rapid motion, and background distraction. Existing approaches often involve designing specific additional branches and performing full-parameter fine-tuning for fusion in each task. However, this paradigm not only duplicates research efforts and hardware costs but also risks model collapse with the limited multi-modal annotated data. In this paper, we propose a universal framework named X-Prompt for all multi-modal video object segmentation tasks, designated as RGB+X. The X-Prompt framework first pre-trains a video object segmentation foundation model using RGB data, and then utilize the additional modality of the prompt to adapt it to downstream multi-modal tasks with limited data. Within the X-Prompt framework, we introduce the Multi-modal Visual Prompter (MVP), which allows prompting foundation model with the various modalities to segment objects precisely. We further propose the Multi-modal Adaptation Experts (MAEs) to adapt the foundation model with pluggable modality-specific knowledge without compromising the generalization capacity. To evaluate the effectiveness of the X-Prompt framework, we conduct extensive experiments on 3 tasks across 4 benchmarks. The proposed universal X-Prompt framework consistently outperforms the full fine-tuning paradigm and achieves state-of-the-art performance. Code: https://***/PinxueGuo/*** © 2024 ACM.

关键词： Image segmentation

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EAFormer: Scene Text Segmentation with Edge-Aware Transformers

arXiv

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arXiv 2024年

作者： Yu, Haiyang Fu, Teng Li, Bin Xue, Xiangyang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China

Scene text segmentation aims at cropping texts from scene images, which is usually used to help generative models edit or remove texts. The existing text segmentation methods tend to involve various text-related supervisions for better performance. However, most of them ignore the importance of text edges, which are significant for downstream applications. In this paper, we propose Edge-Aware Transformers, termed EAFormer, to segment texts more accurately, especially at the edge of texts. Specifically, we first design a text edge extractor to detect edges and filter out edges of non-text areas. Then, we propose an edge-guided encoder to make the model focus more on text edges. Finally, an MLP-based decoder is employed to predict text masks. We have conducted extensive experiments on commonly-used benchmarks to verify the effectiveness of EAFormer. The experimental results demonstrate that the proposed method can perform better than previous methods, especially on the segmentation of text edges. Considering that the annotations of several benchmarks (e.g., COCO_TS and MLT_S) are not accurate enough to fairly evaluate our methods, we have relabeled these datasets. Through experiments, we observe that our method can achieve a higher performance improvement when more accurate annotations are used for training. The code and datasets are available at https://***/EAFormer/. © 2024, CC BY.

关键词： Image segmentation

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Improving Viewpoint-Independent Object-Centric Representations through Active Viewpoint Selection

arXiv

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arXiv 2024年

作者： Huang, Yinxuan Gao, Chengmin Li, Bin Xue, Xiangyang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China

Given the complexities inherent in visual scenes, such as object occlusion, a comprehensive understanding often requires observation from multiple viewpoints. Existing multi-viewpoint object-centric learning methods typically employ random or sequential viewpoint selection strategies. While applicable across various scenes, these strategies may not always be ideal, as certain scenes could benefit more from specific viewpoints. To address this limitation, we propose a novel active viewpoint selection strategy. This strategy predicts images from unknown viewpoints based on information from observation images for each scene. It then compares the object-centric representations extracted from both viewpoints and selects the unknown viewpoint with the largest disparity, indicating the greatest gain in information, as the next observation viewpoint. Through experiments on various datasets, we demonstrate the effectiveness of our active viewpoint selection strategy, significantly enhancing segmentation and reconstruction performance compared to random viewpoint selection. Moreover, our method can accurately predict images from unknown viewpoints. Copyright © 2024, The Authors. All rights reserved.

关键词： Active learning

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Deep-OCTA: Ensemble Deep Learning Approaches for Diabetic Retinopathy Analysis on OCTA Images 25th

Deep-OCTA: Ensemble Deep Learning Approaches for Diabetic R...

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25th International Conference on Medical Image Computing and computer-Assisted Intervention , MICCAI 2022

作者： Hou, Junlin Xiao, Fan Xu, Jilan Zhang, Yuejie Zou, Haidong Feng, Rui School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Department of Ophthalmology Shanghai General Hospital School of Medicine Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783031336577

The ultra-wide optical coherence tomography angiography (OCTA) has become an important imaging modality in diabetic retinopathy (DR) diagnosis. However, there are few researches focusing on automatic DR analysis using ultra-wide OCTA. In this paper, we present novel and practical deep-learning solutions based on ultra-wide OCTA for the Diabetic Retinopathy Analysis Challenge (DRAC). In the first task of segmentation of DR lesions, we utilize UNet and UNet++ to segment three lesions with strong data augmentation and model ensemble. In the second task of image quality assessment, we create an ensemble of Inception-V3, SE-ResNeXt, and Vision Transformer models. Pre-training on the large dataset as well as the hybrid MixUp and CutMix strategy are both adopted to boost the generalization ability of our models. In the third task of DR grading, we build a Vision Transformer and find that the model pre-trained on color fundus images serves as a useful substrate for OCTA images. Extensive ablation studies demonstrate the effectiveness of each designed component in our solutions. The proposed methods rank 4th, 3rd, and 5th on the three leaderboards of DRAC, respectively. Our code is publicly available at https://***/FDU-VTS/DRAC. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optical tomography

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AdvQDet: Detecting Query-Based Adversarial Attacks with Adversarial Contrastive Prompt Tuning

arXiv

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arXiv 2024年

作者： Wang, Xin Chen, Kai Ma, Xingjun Chen, Zhineng Chen, Jingjing Jiang, Yu-Gang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China

Deep neural networks (DNNs) are known to be vulnerable to adversarial attacks even under a black-box setting where the adversary can only query the model. Particularly, query-based blackbox adversarial attacks estimate adversarial gradients based on the returned probability vectors of the target model for a sequence of queries. During this process, the queries made to the target model are intermediate adversarial examples crafted at the previous attack step, which share high similarities in the pixel space. Motivated by this observation, stateful detection methods have been proposed to detect and reject query-based attacks. While demonstrating promising results, these methods either have been evaded by more advanced attacks or suffer from low efficiency in terms of the number of shots (queries) required to detect different attacks. Arguably, the key challenge here is to assign high similarity scores for any two intermediate adversarial examples perturbed from the same clean image. To address this challenge, we propose a novel Adversarial Contrastive Prompt Tuning (ACPT) method to robustly fine-tune the CLIP image encoder to extract similar embeddings for any two intermediate adversarial queries. With ACPT, we further introduce a detection framework AdvQDet that can detect 7 state-of-the-art query-based attacks with > 99% detection rate within 5 shots. We also show that ACPT is robust to 3 types of adaptive attacks. Code is available at https://***/xinwong/AdvQDet. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Exercise-induced adaptation of neurons in the vertebrate locomotor system

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Journal of Sport and Health science 2024年第2期13卷 160-171页

作者： Yue Dai Yi Cheng Renkai Ge Ke Chen Liming Yang Key Lab of Adolescent Health Assessment and Exercise Intervention of Ministry of Education College of Physical Education and Health CareEast China Normal UniversityShanghai 200241China Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic EngineeringEast China Normal UniversityShanghai 200241China School of Physical Education and Health Care East China Jiaotong UniversityNanchang 330013China Key Laboratory of High Confidence Software Technologies of Ministry of Education School of Computer SciencePeking UniversityBeijing 100871China

Vertebrate neurons are highly dynamic cells that undergo several alterations in their functioning and physiologies in adaptation to various external *** particular,how these neurons respond to physical exercise has long been an area of active *** of the vertebrate locomotor system’s adaptability suggest multiple mechanisms are involved in the regulation of neuronal activity and properties during *** this brief review,we highlight recent results and insights from the field with a focus on the following mechanisms:(a)alterations in neuronal excitability during acute exercise;(b)alterations in neuronal excitability after chronic exercise;(c)exercise-induced changes in neuronal membrane properties via modulation of ion channel activity;(d)exercise-enhanced dendritic plasticity;and(e)exercise-induced alterations in neuronal gene expression and protein *** hope is to update the community with a cellular and molecular understanding of the recent mechanisms underlying the adaptability of the vertebrate locomotor system in response to both acute and chronic physical exercise.

关键词： Dendritic plasticity Excitability Exercise Ion channel modulation Neuron adaptation

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Learned Fingerprint Embedding for Large-Scale Peptide Mass Spectra Retrieval

Learned Fingerprint Embedding for Large-Scale Peptide Mass S...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Yongshuai Cai, Xiaojun Li, Defeng Sun, Shiwei Chen, Cheng Cui, Xuefeng Shandong University School of Computer Science and Technology Qingdao China Chinese Academy of Sciences Key Lab of Intelligent Information Processing Institute of Computing Technology Beijing China

ISBN: (纸本)9798350337488

Tandem mass spectrometry (MS/MS) is a widely used technique for protein identification, post-translational modifications, immunotherapy, and other applications. As the amount of MS/MS spectra data increases, new computational methods are needed to efficiently search through these databases. This study introduces MS2VEC, a novel fingerprint embedding model designed to facilitate large-scale retrieval of peptide mass spectra. MS2VEC captures the relationships between distant peaks and incorporates position-aware fingerprint features from all peaks. To do this, dilated convolutions are used to capture remote relationships, and a novel position-aware multi-head attention pooling mechanism is used to abstract fingerprint features. The results demonstrate that MS2VEC achieves a top-1 retrieval accuracy of 0.810, outperforming existing methods by 5.1%. Interestingly, the precursor charge is not essential for the retrieval task, as the spectra itself contains enough information to accurately predict the charge. Additionally, the results suggest that weight-balanced fragment ions and water losses are important contributors to fingerprint features. © 2023 IEEE.

关键词： deep learning embedding information retrieval peptide mass spectra tandem mass spectrometry

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An Exploration of Prompt-Based Zero-Shot Relation Extraction Method 21

An Exploration of Prompt-Based Zero-Shot Relation Extraction...

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21st China National Conference on Computational Linguistics, CCL 2022

作者： Zhao, Jun Hu, Yuan Xu, Nuo Gui, Tao Zhang, Qi Chen, Yunwen Gao, Xiang School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Co. Ltd. Shanghai China

ISBN: (纸本)9783031183140

Zero-shot relation extraction is an important method for dealing with the newly emerging relations in the real world which lacks labeled data. However, the mainstream two-tower zero-shot methods usually rely on large-scale and in-domain labeled data of predefined relations. In this work, we view zero-shot relation extraction as a semantic matching task optimized by prompt-tuning, which still maintains superior generalization performance when the labeled data of predefined relations are extremely scarce. To maximize the efficiency of data exploitation, instead of directly fine-tuning, we introduce a prompt-tuning technique to elicit the existing relational knowledge in pre-trained language model (PLMs). In addition, very few relation descriptions are exposed to the model during training, which we argue is the performance bottleneck of two-tower methods. To break through the bottleneck, we model the semantic interaction between relational instances and their descriptions directly during encoding. Experiment results on two academic datasets show that (1) our method outperforms the previous state-of-the-art method by a large margin with different samples of predefined relations;(2) this advantage will be further amplified in the low-resource scenario. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Large dataset

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Abstains from Prediction: Towards Robust Relation Extraction in Real World 21

Abstains from Prediction: Towards Robust Relation Extraction...

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21st China National Conference on Computational Linguistics, CCL 2022

作者： Zhao, Jun Zhang, Yongxin Xu, Nuo Gui, Tao Zhang, Qi Chen, Yunwen Gao, Xiang School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Co. Ltd. Shanghai China

ISBN: (纸本)9783031183140

Supervised learning is a classic paradigm of relation extraction (RE). However, a well-performing model can still confidently make arbitrarily wrong predictions when exposed to samples of unseen relations. In this work, we propose a relation extraction method with rejection option to improve robustness to unseen relations. To enable the classifier to reject unseen relations, we introduce contrastive learning techniques and carefully design a set of class-preserving transformations to improve the discriminability between known and unseen relations. Based on the learned representation, inputs of unseen relations are assigned a low confidence score and rejected. Off-the-shelf open relation extraction (OpenRE) methods can be adopted to discover the potential relations in these rejected inputs. In addition, we find that the rejection can be further improved via readily available distantly supervised data. Experiments on two public datasets prove the effectiveness of our method capturing discriminative representations for unseen relation rejection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Extraction

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CIRCLE: scRNA-seq Data Clustering by Cluster-aware Iterative Contrastive Learning

CIRCLE: scRNA-seq Data Clustering by Cluster-aware Iterative...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jinxian Wang Weikang Jiang Jihong Guan Shuigeng Zhou Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University Shanghai China Department of Computer Science and Technolog Tongji University Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Single-cell RNA sequencing (scRNA-seq) enables researchers to analyze gene expression at single-cell level. One important task in scRNA-seq data analysis is unsupervised clustering, which helps identify distinct cell types, laying down the foundation for other downstream analysis tasks. In this paper, we propose a novel method called Cluster-aware IteRative Contrastive LEarning (CIRCLE in short) for scRNA-seq data clustering, which uses an iterative representation learning and clustering framework to progressively learn the clustering structure of scRNA-seq data with a cluster-aware contrastive loss. CIRCLE consists of a Transformer encoder, a clustering head, a projection head and a contrastive loss. First, CIRCLE extracts the feature vectors of the original and augmented data by the Transformer-encoder. Then, it computes the clustering centroids by K-means and employs the student’s t-distribution to assign pseudo-labels to all cells in the clustering head. The projection-head uses a Multi-Layer Perceptron (MLP) to obtain projections of the augmented data. At last, both pseudo-labels and projections are used in the contrastive loss to guide the model training. Such a process goes iteratively so that the clustering result gets better and better. Extensive experiments on 25 real-world scRNA-seq datasets show that CIRCLE outperforms the state-of-the-art (SOTA) methods in most cases. Concretely, CIRCLE surpasses the existing methods by from 14% to 280%, and from 5% to 133% on average in terms of performance metrics ARI and NMI respectively. Source code is available at https://***/Alunethy/CIRCLE.

关键词： Representation learning Training Accuracy Data visualization Training data Contrastive learning Transformers Feature extraction Vectors Iterative methods

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