Significant progress has been witnessed in learning-based Multi-view Stereo (MVS) under supervised and unsupervised settings. To combine their respective merits in accuracy and completeness, meantime reducing the demand for expensive labeled data, this paper explores the problem of learning-based MVS in a semi-supervised setting that only a tiny part of the MVS data is attached with dense depth ground truth. However, due to huge variation of scenarios and flexible settings in views, it may break the basic assumption in classic semi-supervised learning, that unlabeled data and labeled data share the same label space and data distribution, named as semi-supervised distribution-gap ambiguity in the MVS problem. To handle these issues, we propose a novel semi-supervised distribution-augmented MVS framework, namely SDA-MVS. For the simple case that the basic assumption works in MVS data, consistency regularization encourages the model predictions to be consistent between original sample and randomly augmented sample. For further troublesome case that the basic assumption is conflicted in MVS data, we propose a novel style consistency loss to alleviate the negative effect caused by the distribution gap. The visual style of unlabeled sample is transferred to labeled sample to shrink the gap, and the model prediction of generated sample is further supervised with the label in original labeled sample. The experimental results in semi-supervised settings of multiple MVS datasets show the superior performance of the proposed method. With the same settings in backbone network, our proposed SDA-MVS outperforms its fully-supervised and unsupervised baselines.
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In the Class-Incremental Learning (CIL) task, rehearsal-based approaches have received a lot of attention recently. However, storing old class samples is often infeasible in application scenarios where device memory is insufficient or data privacy is important. Therefore, it is necessary to rethink Non-Exemplar Class-Incremental Learning (NECIL). In this paper, we propose a novel NECIL method named POLO with an adaPtive Old cLass recOnstruction mechanism, in which a density-based prototype reinforcement method (DBR), a topology-correction prototype adaptation method (TPA), and an adaptive prototype augmentation method (APA) are designed to reconstruct pseudo features of old classes in new incremental sessions. Specifically, the DBR focuses on the low-density features to maintain the model's discriminative ability for old classes. Afterward, the TPA is designed to adapt old class prototypes to new feature spaces in the incremental learning process. Finally, the APA is developed to further adapt pseudo feature spaces of old classes to new feature spaces. Experimental evaluations on four benchmark datasets demonstrate the effectiveness of our proposed method over the state-of-the-art NECIL methods.
Video Grounding (VG), has drawn widespread attention over the past few years, and numerous studies have been devoted to improving performance on various VG benchmarks. Nevertheless, the label annotation procedures in VG produce imbalanced query-moment-label distributions in the datasets, which severely deteriorate the learning model's capability of truly understanding the video contents. Existing works on debiased VG either focus on adjusting the learning model or conducting video-level augmentation, failing to handle the temporal bias issue caused by imbalanced query-moment-label distributions. In this paper, we propose a Disentangled Feature Mixup (DFM) framework for debiased VG, which is capable of performing unbiased grounding to tackle the temporal bias issue. Specifically, a feature-mixup augmentation strategy is designed to generate new (text, location) pairs with diverse temporal distributions via jointly augmenting the representation of text queries and the location labels. This strategy encourages making prediction based on more diverse data samples with balanced query-moment-label distributions. Furthermore, we also design a content-location disentanglement module to disentangle the representations of the temporal information and content information in videos, which is able to remove the spurious effect of temporal biases on video representation. Given that our proposed DFM framework conducts feature-level augmentation and disentanglement, it is model-agnostic and can be applied to most baselines simply yet effectively. Extensive experiments show that our proposed DFM framework is able to significantly outperform baseline models in various metrics under both independent identical distribution (i.i.d.) and out-of-distribution (o.o.d.) scenes, especially in scenarios with annotation distribution changes.
Deep neural networks are vulnerable to adversarial examples crafted by applying human-imperceptible perturbations on clean inputs. Although many attack methods can achieve high success rates in the white-box setting, they also exhibit weak transferability in the black-box setting. Recently, various methods have been proposed to improve adversarial transferability, in which the input transformation is one of the most effective methods. In this work, we notice that existing input transformation-based works mainly adopt the transformed data in the same domain for augmentation. Inspired by domain generalization, we aim to further improve the transferability using the data augmented from different domains. Specifically, a style transfer network can alter the distribution of low-level visual features in an image while preserving semantic content for humans. Hence, we propose a novel attack method named Style Transfer Method (STM) that utilizes a proposed arbitrary style transfer network to transform the images into different domains. To avoid inconsistent semantic information of stylized images for the classification network, we fine-tune the style transfer network and mix up the generated images added by random noise with the original images to maintain semantic consistency and boost input diversity. Extensive experimental results on the ImageNet-compatible dataset show that our proposed method can significantly improve the adversarial transferability on either normally trained models or adversarially trained models than state-of-the-art input transformation-based attacks. Code is available at: https://github.com/Zhijin-Ge/STM.
Most previous approaches for analyzing food images have relied on extensively annotated datasets, resulting in significant human labeling expenses due to the varied and intricate nature of such images. Inspired by the effectiveness of contrastive self-supervised methods in utilizing unlabelled data, weiqing explore leveraging these techniques on unlabelled food images. In contrastive self-supervised methods, two views are randomly generated from an image by data augmentations. However, regarding food images, the two views tend to contain similar informative contents, causing large mutual information, which impedes the efficacy of contrastive self-supervised learning. To address this problem, we propose Feature Suppressed Contrast (FeaSC) to reduce mutual information between views. As the similar contents of the two views are salient or highly responsive in the feature map, the proposed FeaSC uses a response-aware scheme to localize salient features in an unsupervised manner. By suppressing some salient features in one view while leaving another contrast view unchanged, the mutual information between the two views is reduced, thereby enhancing the effectiveness of contrast learning for self-supervised food pre-training. As a plug-and-play module, the proposed method consistently improves BYOL and SimSiam by 1.70% ~ 6.69% classification accuracy on four publicly available food recognition datasets. Superior results have also been achieved on downstream segmentation tasks, demonstrating the effectiveness of the proposed method.
Semi-supervised semantic segmentation (SSS) is an important task that utilizes both labeled and unlabeled data to reduce expenses on labeling training examples. However, the effectiveness of SSS algorithms is limited by the difficulty of fully exploiting the potential of unlabeled data. To address this, we propose a dual-level Siamese structure network (DSSN) for pixel-wise contrastive learning. By aligning positive pairs with a pixel-wise contrastive loss using strong augmented views in both low-level image space and high-level feature space, the proposed DSSN is designed to maximize the utilization of available unlabeled data. Additionally, we introduce a novel class-aware pseudo-label selection strategy for weak-to-strong supervision, which addresses the limitations of most existing methods that do not perform selection or apply a predefined threshold for all classes. Specifically, our strategy selects the top high-confidence prediction of the weak view for each class to generate pseudo labels that supervise the strong augmented views. This strategy is capable of taking into account the class imbalance and improving the performance of long-tailed classes. Our proposed method achieves state-of-the-art results on two datasets, PASCAL VOC 2012 and Cityscapes, outperforming other SSS algorithms by a significant margin. The source code is available at https://github.com/kunzhan/DSSN.
Zero-Shot Learning (ZSL) aims to recognize unseen classes by generalizing the knowledge, i.e., visual and semantic relationships, obtained from seen classes, where image augmentation techniques are commonly applied to improve the generalization ability of a model. However, this approach can also cause adverse effects on ZSL since the conventional augmentation techniques that solely depend on single-label supervision is not able to maintain semantic information and result in the semantic distortion issue consequently. In other words, image argumentation may falsify the semantic (e.g., attribute) information of an image. To take the advantage of image augmentations while mitigating the semantic distortion issue, we propose a novel ZSL approach by Harnessing Adversarial Samples (HAS). HAS advances ZSL through adversarial training which takes into account three crucial aspects: (1) robust generation by enforcing augmentations to be similar to negative classes, while maintaining correct labels, (2) reliable generation by introducing a latent space constraint to avert significant deviations from the original data manifold, and (3) diverse generation by incorporating attribute-based perturbation by adjusting images according to each semantic attribute's localization. Through comprehensive experiments on three prominent zero-shot benchmark datasets, we demonstrate the effectiveness of our adversarial samples approach in both ZSL and Generalized Zero-Shot Learning (GZSL) scenarios. Our source code is available at https://github.com/uqzhichen/HASZSL.
Augmentation techniques and sampling strategies are crucial in contrastive learning, but in most existing works, augmentation techniques require careful design, and their sampling strategies can only capture a small amount of intrinsic supervision information. Additionally, the existing methods require complex designs to obtain two different representations of the data. To overcome these limitations, we propose a novel framework called the Self-Contrastive Graph Diffusion Network (SCGDN). Our framework consists of two main components: the Attentional Module (AttM) and the Diffusion Module (DiFM). AttM aggregates higher-order structure and feature information to get an excellent embedding, while DiFM balances the state of each node in the graph through Laplacian diffusion learning and allows the cooperative evolution of adjacency and feature information in the graph. Unlike existing methodologies, SCGDN is an augmentation-free approach that avoids "sampling bias" and semantic drift, without the need for pre-training. We conduct a high-quality sampling of samples based on structure and feature information. If two nodes are neighbors, they are considered positive samples of each other. If two disconnected nodes are also unrelated on kNN graph, they are considered negative samples for each other. The contrastive objective reasonably uses our proposed sampling strategies, and the redundancy reduction term minimizes redundant information in the embedding and can well retain more discriminative information. In this novel framework, the graph self-contrastive learning paradigm gives expression to a powerful force. The results manifest that SCGDN can consistently generate out performance over both the contrastive methods and the classical methods. The source code is available at https://github.com/kunzhan/SCGDN.
Graph classification is a crucial task in many real-world multimedia applications, where graphs can represent various multimedia data types such as images, videos, and social networks. Previous efforts have applied graph neural networks (GNNs) in balanced situations where the class distribution is balanced. However, real-world data typically exhibit long-tailed class distributions, resulting in a bias towards the head classes when using GNNs and limited generalization ability over the tail classes. Recent approaches mainly focus on re-balancing different classes during model training, which fails to explicitly introduce new knowledge and sacrifices the performance of the head classes. To address these drawbacks, we propose a novel framework called Retrieval Augmented Hybrid Network (RAHNet) to jointly learn a robust feature extractor and an unbiased classifier in a decoupled manner. In the feature extractor training stage, we develop a graph retrieval module to search for relevant graphs that directly enrich the intra-class diversity for the tail classes. Moreover, we innovatively optimize a category-centered supervised contrastive loss to obtain discriminative representations, which is more suitable for long-tailed scenarios. In the classifier fine-tuning stage, we balance the classifier weights with two weight regularization techniques, i.e., Max-norm and weight decay. Experiments on various popular benchmarks verify the superiority of the proposed method against state-of-the-art approaches.
The increasing popularity of multi-modal knowledge graphs (MMKGs) has led to a need for efficient entity alignment techniques that can exploit multi-modal information to integrate knowledge from different sources. GNN-based multi-modal entity alignment (MMEA) methods have achieved significant progress in entity alignment(EA) areas. However, these methods only rely on Graph Neural Networks (GNNs) to encode structural information, while ignoring visual and semantic modalities, which may lead to incomplete representation, thus how to integrate the visual and semantic information into GNN-based EA methods remains unexplored. In light of our insight that incorporating the message-passing mechanism of Graph Neural Networks to integrate multi-modal information is essential for fully exploiting the graph representation capability of GNN, we propose a novel Cross-modal Graph attention network for Entity Alignment (XGEA) that enables visual knowledge to interact with other views of the entity, including structural and literal information. We leverage the information from one modality as complementary relation information to compute the attention of another modality in the graph attention layers, enabling the learning of entity embedding by integrating multiple modalities. Moreover, the quantity of labeled data plays a crucial role in model performance, yet obtaining sufficient training data is expensive. To mitigate this issue, we use visual and semantic information to generate pseudo-pairs and propose a soft pseudo-labeling method for entity alignment to assign weights to the augmented training data to balance its quantity and quality. Extensive experiments show that our XGEA achieves superior performance consistently over the state-of-the-art MMEA baselines.
Although recent advances have prompted the prosperity in graph contrastive learning, the researches on universal principles for model design and desirable properties of latent representations are still inadequate. From a statistical perspective, this paper proposes two principles for guidance and constructs a general self-supervised framework for negative-free graph contrastive learning. Reformulating data augmentation as a mixture process, the first one, termed consistency principle, lays stress on exploring and mapping cross-view common information to consistent and essence-revealing representations. For the purpose of instantiation, four statistical indicators are employed to estimate and maximize the correlation between representations from various views, whose accordant variation trend during training implies the extraction of common content. With awareness of the insufficiency of a solo consistency principle, suffering from degenerated and coupled solutions, a decorrelation principle is put forward to encourage diverse and informative representations. Accordingly, two specific strategies, performing in representation space and eigen spectral space, respectively, are propounded to decouple various representation channels. Under two principles, various combinations of concrete implementations derive a family of methods. The comparison experiments with current state-of-the-arts demonstrate the effectiveness and sufficiency of two principles for high-quality graph representations. Furthermore, visual studies reveal how certain principles affect learned representations.
Federated Learning aims to learn a global model on the server side that generalizes to all clients in a privacy-preserving manner, by leveraging the local models from different clients. Existing solutions focus on either regularizing the objective functions among clients or improving the aggregation mechanism for the improved model generalization capability. However, their performance is typically limited by the dataset biases, such as the heterogeneous data distributions and the missing classes. To address this issue, this paper presents a cross-silo prototypical calibration method (FedCSPC), which takes additional prototype information from the clients to learn a unified feature space on the server side. Specifically, FedCSPC first employs the Data Prototypical Modeling (DPM) module to learn data patterns via clustering to aid calibration. Subsequently, the cross-silo prototypical calibration (CSPC) module develops an augmented contrastive learning method to improve the robustness of the calibration, which can effectively project cross-source features into a consistent space while maintaining clear decision boundaries. Moreover, the CSPC module's ease of implementation and plug-and-play characteristics make it even more remarkable. Experiments were conducted on four datasets in terms of performance comparison, ablation study, in-depth analysis and case study, and the results verified that FedCSPC is capable of learning the consistent features across different data sources of the same class under the guidance of calibrated model, which leads to better performance than the state-of-the-art methods. The source codes have been released at https://github.com/qizhuang-qz/FedCSPC.
Audio supports visual story-telling in movies through the use of different sounds. These sounds are often tied to different visual elements, including foreground entities, the interactions between them as well as background context. Visual captions provide a condensed view of an image, providing a natural language description of entities and the relationships between them. In this work, we utilize visual captions to semantically ground audio representations in a self-supervised setup. We leverage state-of-the-art vision-language models to augment movie datasets with visual captions at scale to the order of 9.6M captions to learn audio representations from over 2500 hours of movie data. We evaluate the utility of the learned representations and show state-of-the art performance on two movie understanding tasks, genre and speaking-style classification, outperforming video based methods and audio baselines. Finally, we show that the learned model can be transferred in a zero-shot manner through application in both movie understanding tasks and general action recognition.
Single domain generalization (SDG) is a realistic yet challenging domain generalization scenario that aims to generalize a model trained on a single domain to multiple unseen domains. Typical SDG methods are essentially supervised data augmentation strategies, which tend to enhance the novelty rather than the diversity of augmented samples. Insufficient diversity may jeopardize the model generalization ability. In this paper, we propose a novel adversarial method, termed Unsupervised Diversity Probe (UDP), to synthesize novel and diverse samples in fully unsupervised settings. More specifically, to ensure that samples are novel, we study SDG from an information-theoretic perspective that minimizes the uncertainty coefficients between synthesized and source samples. Considering that the variation in a single source domain is limited, we introduce a regularization imposed on the auxiliary module that synthesizes variable samples, incorporated with uncertainty coefficients in an adversarial manner to complement the diversity. Subsequently, an available region is utilized to guarantee the samples' safety. For the network architecture, we design a simple probe module that can synthesize samples in several different aspects. UDP is an unsupervised and easy-to-implement method that solves SDG using only synthetic (source) samples, thus reducing the dependence on task models. Extensive experiments on three benchmark datasets show that UDP achieves remarkable results and outperforms existing supervised and unsupervised methods by a large margin in single domain generalization.
Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data
PDF ↗Federated learning (FL) is a distributed machine learning paradigm that needs collaboration between a server and a series of clients with decentralized data. To make FL effective in real-world applications, existing work devotes to improving the modeling of decentralized non-IID data. In non-IID settings, there are intra-client inconsistency that comes from the imbalanced data modeling, and inter-client inconsistency among heterogeneous client distributions, which not only hinders sufficient representation of the minority data, but also brings discrepant model deviations. However, previous work overlooks to tackle the above two coupling inconsistencies together. In this work, we propose FedRANE, which consists of two main modules, i.e., local relational augmentation (LRA) and global Nash equilibrium (GNE), to resolve intra-and inter-client inconsistency simultaneously. Specifically, in each client, LRA mines the similarity relations among different data samples and enhances the minority sample representations with their neighbors using attentive message passing. In server, GNE reaches an agreement among inconsistent and discrepant model deviations from clients to server, which encourages the global model to update in the direction of global optimum without breaking down the clients' optimization toward their local optimums. We conduct extensive experiments on four benchmark datasets to show the superiority of FedRANE in enhancing the performance of FL with non-IID data.
Recently, generative Text-based visual question answering (TextVQA) methods, which are often based on language models, have exhibited impressive results and drawn increasing attention. However, due to the inconsistencies in both input forms and optimization objectives, the power of pretrained language models is not fully explored, resulting in the need for large amounts of training data. In this work, we rethink the characteristics of the TextVQA task and find that scene text is indeed a special kind of language embedded in images. To this end, we propose a text-centered generative framework FITB (stands for Filling In The Blank), in which multimodal information is mainly represented in textual form and rationale-augmented prompting is involved. Specifically, an infilling-based prompt strategy is utilized to formulate TextVQA as a novel problem of filling in the blank with proper scene text according to the language context. Furthermore, aiming to prevent the model from language bias overfitting, we design a rough answer grounding module to provide visual rationales for promoting multimodal reasoning. Extensive experiments verify the superiority of FITB in both fully-supervised and zero-shot/few-shot settings. Notably, even with a saving of about 64M data, FITB surpasses the state-of-the-art method by 3.00% and 1.99% on TextVQA and ST-VQA datasets, respectively.
The rich tapestry of human history is often painstakingly pieced together from ancient manuscripts, serving as resilient time capsules of cultural heritage, societal norms, religious tenets, and quotidian life. Unfortunately, the ravages of time, careless preservation, and varied forms of degradation frequently leave us with fragmented relics. The traditional process of reconstructing these fragments is an arduous task, demanding exhaustive manual intervention and a global collaboration among archaeologists. This paper presents a transformative approach to this challenge, harnessing multi-media techniques to restore the connectable fragments of the invaluable Dunhuang scrolls. We curate a unique multimodal dataset of the fragmented Dunhuang manuscripts and architect an innovative three-tiered pipeline to reconstruct these historical scrolls. Our initial stage uses a text-based localization strategy, filtering fragment pairs through text comparison. We then employ a novel self-supervised contour-based pairwise matching framework to overcome the hurdle of limited labeled pairing samples. This process is powered by data augmentation techniques and a Siamese network which determines the most compatible matches. The final stage in our pipeline globally reconstructs the selected fragment pairs with hierarchical clustering, bringing us closer to the original grandeur of the Dunhuang scrolls. Our empirical evaluations reveal that this pipeline exhibits a remarkable success rate in fragment assembly. By addressing this cross-disciplinary challenge, our dataset and pipeline not only contribute to the field of multi-media artificial intelligence but also hold profound implications for sociocultural studies and future explorations into the understanding of ancient cultural history.
Knowledge Distillation (KD) for object detection aims to train a compact detector by transferring knowledge from a teacher model. Since the teacher model perceives data in a way different from humans, existing KD methods only distill knowledge that is consistent with labels annotated by human expert while neglecting knowledge that is not consistent with human perception, which results in insufficient distillation and sub-optimal performance. In this paper, we propose inconsistent knowledge distillation (IKD), which aims to distill knowledge inherent in the teacher model's counter-intuitive perceptions. We start by considering the teacher model's counter-intuitive perceptions of frequency and non-robust features. Unlike previous works that exploit fine-grained features or introduce additional regularizations, we extract inconsistent knowledge by providing diverse input using data augmentation. Specifically, we propose a sample-specific data augmentation to transfer the teacher model's ability in capturing distinct frequency components and suggest an adversarial feature augmentation to extract the teacher model's perceptions of non-robust features in the data. Extensive experiments demonstrate the effectiveness of our method which outperforms state-of-the-art KD baselines on one-stage, two-stage and anchor-free object detectors (at most +1.0 mAP). Our codes will be made available at https://github.com/JWLiang007/IKD.git.
Vision-and-language multi-modal pretraining and fine-tuning have shown great success in visual question answering (VQA). Compared to general domain VQA, the performance of biomedical VQA suffers from limited data. In this paper, we propose a retrieval-augmented pretrain-and-finetune paradigm named RAMM for biomedical VQA to overcome the data limitation issue. Specifically, we collect a new biomedical dataset named PMCPM which offers patient-based image-text pairs containing diverse patient situations from PubMed. Then, we pretrain the biomedical multi-modal model to learn visual and textual representation for image-text pairs and align these representations with image-text contrastive objective (ITC). Finally, we propose a retrieval-augmented method to better use the limited data. We propose to retrieve similar image-text pairs based on ITC from pretraining datasets and introduce a novel retrieval-attention module to fuse the representation of the image and the question with the retrieved images and texts. Experiments demonstrate that our retrieval-augmented pretrain-and-finetune paradigm obtains state-of-the-art performance on Med-VQA2019, Med-VQA2021, VQARAD, and SLAKE datasets. Further analysis shows that the proposed RAMM and PMCPM can enhance biomedical VQA performance compared with previous resources and methods. The pre-trained models and codes are published at https://github.com/GanjinZero/RAMM.
Graph Neural Networks (GNNs) have been the standard toolkit for processing non-euclidean spatial data since their powerful capability in graph representation learning. Unfortunately, their training strategy for network parameters is inefficient since it is directly inherited from classic Neural Networks (NNs), ignoring the characteristic of GNNs. To alleviate this issue, experimental analyses are performed to investigate the knowledge captured in classifier parameters during network training. We conclude that the parameter features, i.e., the column vectors of the classifier parameter matrix, are cluster representations with high discriminability. And after a theoretical analysis, we conclude that the discriminability of these features is obtained from the feature propagation from nodes to parameters. Furthermore, an experiment verifies that compared with cluster centroids, the parameter features are more potential for augmenting the feature propagation between nodes. Accordingly, a novel GNN-specific training framework is proposed by simultaneously updating node representations and classifier parameters via a unified feature propagation scheme. Moreover, two augmentation schemes are implemented for the framework, named Full Propagation Augmentation (FPA) and Simplified Full Propagation Augmentation (SFPA). Specifically, FPA augmentates the feature propagation of each node with the updated classifier parameters. SFPA only augments nodes with the classifier parameters corresponding to their clusters. Theoretically, FPA is equivalent to optimizing a novel graph learning objective, which demonstrates the universality of the proposed framework to existing GNNs. Extensive experiments demonstrate the superior performance and the universality of the proposed framework.