Zero-Shot Learning (ZSL) aims to recognize images from seen and unseen classes by aligning visual and semantic knowledge (e.g., attribute descriptions). However, the fine-grained attributes in the RGB domain can be easily affected by background noise (e.g., the grey bird tail blending with the ground), making it difficult to effectively distinguish them. Analyzing the features in the frequency domain assists in better distinguishing the attributes since their patterns remain consistent across different images, unlike noise which may be more variable. Nevertheless, existing ZSL methods typically learn visual features directly from the RGB domain, which can impede the recognition of certain attributes. To overcome this limitation, we propose a novel ZSL method named Frequency-based Phase Augmentation (FPA) network, which learns an effective representation of the attributes in the frequency domain. Specifically, we introduce a Hybrid Phase Augmentation (HPA) module to transform visual features into the frequency domain and augment the phase component for better retention of semantic information of the attributes. The use of phase-augmented features enables FPA to capture more semantic knowledge that can be challenging to distinguish in the RGB domain, suppress noise, and highlight significant attributes. Our extensive experiments show that FPA achieves state-of-the-art performance across four standard datasets.
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This paper identifies two kinds of redundancy in the current VideoQA paradigm. Specifically, the current video encoders tend to holistically embed all video clues at different granularities in a hierarchical manner, which inevitably introducesneighboring-frame redundancy that can overwhelm detailed visual clues at the object level. Subsequently, prevailing vision-language fusion designs introduce thecross-modal redundancy by exhaustively fusing all visual elements with question tokens without explicitly differentiating their pairwise vision-language interactions, thus making a pernicious impact on the answering. To this end, we propose a novel transformer-based architecture, that aims to model VideoQA in a redundancy-aware manner. To address the neighboring-frame redundancy, we introduce a video encoder structure that emphasizes the object-level change in neighboring frames, while adopting an out-of-neighboring message-passing scheme that imposes attention only on distant frames. As for the cross-modal redundancy, we equip our fusion module with a novel adaptive sampling, which explicitly differentiates the vision-language interactions by identifying a small subset of visual elements that exclusively support the answer. Upon these advancements, we find this \underlineR edundancy-\underlinea ware trans\underlineformer (RaFormer) can achieve state-of-the-art results on multiple VideoQA benchmarks.
In the zero-shot learning (ZSL), learned representation spaces are often biased toward seen classes, thus limiting the ability to predict previously unseen classes. In this paper, we propose Masked token Mixup and cross-Modal Reconstruction for zero-shot learning, termed as M3R, which can significantly alleviate the bias toward seen classes. The M3R mainly consists of Random Token Mixup (RTM), Unseen Class Detection (UCD), and Hard Cross-modal Reconstruction (HCR). Firstly, mappings without proper adaptations to unseen classes would cause the bias toward seen classes. To address this issue, the RTM is introduced to generate diverse unseen class agents, thereby broadening the representation space to cover unknown classes. It is applied at a randomly selected layer in the Vision Transformer, producing smooth low- and high-level representation space boundaries to cover rich attributes. Secondly, it should be noted that unseen class agents generated by the RTM may be mixed with seen class samples. To overcome this challenge, the UCD is designed to generate greater entropy values for unseen classes, thereby distinguishing seen classes from unseen classes. Thirdly, to further mitigate the bias toward seen classes and explore associations between semantics and visual images, the HCR is proposed, which can reconstruct masked pixels based on few discriminative tokens and attribute embeddings. This approach can enable models to have a deep understanding of image contents and build powerful connections between semantic attributes and visual information. Both qualitative and quantitative results demonstrate the effectiveness and usefulness of our proposed M3R model.
Multi-stage Factorized Spatio-Temporal Representation for RGB-D Action and Gesture Recognition
PDF ↗RGB-D action and gesture recognition remain an interesting topic in human-centered scene understanding, primarily due to the multiple granularities and large variation in human motion. Although many RGB-D based action and gesture recognition approaches have demonstrated remarkable results by utilizing highly integrated spatio-temporal representations across multiple modalities (i.e., RGB and depth data), they still encounter several challenges. Firstly, vanilla 3D convolution makes it hard to capture fine-grained motion differences between local clips under different modalities. Secondly, the intricate nature of highly integrated spatio-temporal modeling can lead to optimization difficulties. Thirdly, duplicate and unnecessary information can add complexity and complicate entangled spatio-temporal modeling. To address the above issues, we propose an innovative heuristic architecture called Multi-stage Factorized Spatio-Temporal (MFST) for RGB-D action and gesture recognition. The proposed MFST model comprises a 3D Central Difference Convolution Stem (CDC-Stem) module and multiple factorized spatio-temporal stages. The CDC-Stem enriches fine-grained temporal perception, and the multiple hierarchical spatio-temporal stages construct dimension-independent higher-order semantic primitives. Specifically, the CDC-Stem module captures bottom-level spatio-temporal features and passes them successively to the following spatio-temporal factored stages to capture the hierarchical spatial and temporal features through the Multi-Scale Convolution and Transformer (MSC-Trans) hybrid block and Weight-shared Multi-Scale Transformer (WMS-Trans) block. The seamless integration of these innovative designs results in a robust spatio-temporal representation that outperforms state-of-the-art approaches on RGB-D action and gesture recognition datasets.
Autonomous obstacle avoidance is of vital importance for an intelligent agent such as a mobile robot to navigate in its environment. Existing state-of-the-art methods train a spiking neural network (SNN) with deep reinforcement learning (DRL) to achieve energy-efficient and fast inference speed in complex/unknown scenes. These methods typically assume that the environment is static while the obstacles in real-world scenes are often dynamic. The movement of obstacles increases the complexity of the environment and poses a great challenge to the existing methods. In this work, we approach robust dynamic obstacle avoidance twofold. First, we introduce the neuromorphic vision sensor (i.e., event camera) to provide motion cues complementary to the traditional Laser depth data for handling dynamic obstacles. Second, we develop an DRL-based event-enhanced multimodal spiking actor network (EEM-SAN) that extracts information from motion events data via unsupervised representation learning and fuses Laser and event camera data with learnable thresholding. Experiments demonstrate that our EEM-SAN outperforms state-of-the-art obstacle avoidance methods by a significant margin, especially for dynamic obstacle avoidance.
RGB and thermal infrared (TIR) data have different visual properties, which make their fusion essential for effective object tracking in diverse environments and scenes. Existing RGBT tracking methods commonly use attention mechanisms to generate reliability weights for multi-modal feature fusion. However, without explicit supervision, these weights may be unreliably estimated, especially in complex scenarios. To address this problem, we propose a novel Quality-Aware RGBT Tracker (QAT) for robust RGBT tracking. QAT learns reliable weights for each modality in a supervised manner and performs weighted residual guidance to extract and leverage useful features from both modalities. We address the issue of the lack of labels for reliability learning by designing an efficient three-branch network that generates reliable pseudo labels, and a simple binary classification scheme that estimates high-accuracy reliability weights, mitigating the effect of noisy pseudo labels. To propagate useful features between modalities while reducing the influence of noisy modal features on the migrated information, we design a weighted residual guidance module based on the estimated weights and residual connections. We evaluate our proposed QAT on five benchmark datasets, including GTOT, RGBT210, RGBT234, LasHeR, and VTUAV, and demonstrate its excellent performance compared to state-of-the-art methods. Experimental results show that QAT outperforms existing RGBT tracking methods in various challenging scenarios, demonstrating its efficacy in improving the reliability and accuracy of RGBT tracking.
Curriculum-Listener: Consistency- and Complementarity-Aware Audio-Enhanced Temporal Sentence Grounding
PDF ↗Temporal Sentence Grounding aims to retrieve a video moment given a natural language query. Most existing literature merely focuses on visual information in videos without considering the naturally accompanied audio which may contain rich semantics. The few works considering audio simply regard it as an additional modality, overlooking that: i) it's non-trivial to explore consistency and complementarity between audio and visual; ii) such exploration requires handling different levels of information densities and noises in the two modalities. To tackle these challenges, we propose Adaptive Dual-branch Promoted Network (ADPN) to exploit such consistency and complementarity: i) we introduce a dual-branch pipeline capable of jointly training visual-only and audio-visual branches to simultaneously eliminate inter-modal interference; ii) we design Text-Guided Clues Miner (TGCM) to discover crucial locating clues via considering both consistency and complementarity during audio-visual interaction guided by text semantics; iii) we propose a novel curriculum-based denoising optimization strategy, where we adaptively evaluate sample difficulty as a measure of noise intensity in a self-aware fashion. Extensive experiments show the state-of-the-art performance of our method.
Multi-view learning aims to leverage data acquired from multiple sources to achieve better performance compared to using a single view. However, the performance of multi-view learning can be negatively impacted by noisy or corrupted views in certain real-world situations. As a result, it is crucial to assess the confidence of predictions and obtain reliable learning outcomes. In this paper, we introduce CALM, an enhanced encoding and confidence evaluation framework for trustworthy multi-view classification. Our method comprises enhanced multi-view encoding, multi-view confidence-aware fusion, and multi-view classification regularization, enabling the simultaneous evaluation of prediction confidence and the yielding trustworthy classifications. Enhanced multi-view encoding takes advantage of cross-view consistency and class diversity to improve the efficacy of the learned latent representation, facilitating more reliable classification results. Multi-view confidence-aware fusion utilizes a confidence-aware estimator to evaluate the confidence scores of classification outcomes. The final multi-view classification results are then derived through confidence-aware fusion. To achieve reliable and accurate confidence scores, multivariate Gaussian distributions are employed to model the prediction distribution. The advantage of CALM lies in its ability to evaluate the quality of each view, reducing the influence of low-quality views on the multi-view fusion process and ultimately leading to improved classification performance and confidence evaluation. Comprehensive experimental results demonstrate that our method outperforms other trusted multi-view learning methods in terms of effectiveness, reliability, and robustness.
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.
Multi-view clustering is an important machine learning task for multi-media data. Recently, graph filter based multi-view clustering achieves promising performance and attracts much attention. However, the conventional graph filter based methods only use a pre-defined graph filter for each view and the used graph filters ignore the rich information among all views. Different from the conventional methods, in this paper, we aim to tackle a new problem, i.e., instead of using the pre-defined graph filters, how to construct an appropriate consensus graph filter by considering the information in all views. To achieve this, we propose a novel multi-view clustering method with graph filter learning. In our method, we learn an appropriate consensus graph filter from all views of data with multiple graph learning rather than directly pre-defining it. Then, we provide an iterative algorithm to obtain the consensus graph filter and analyze why it can lead to better clustering results. The extensive experiments on benchmark data sets demonstrate the effectiveness and superiority of the proposed method. The codes of this article are released in http://Doctor-Nobody.github.io/codes/MCLGF.zip.
In the presence of abrupt motion, the pushbroom scanning mechanism of a rolling shutter (RS) camera tends to bring undesirable distortion, which is recently shown to be beneficial for high-speed frame interpolation. Although promising results have been reported by using multiple consecutive RS frames, to interpolate intermediate distortion-free frames from a single RS image is still an open question, due to the existence of multiple motions that can account for the recorded distortion. Another limitation of RS cameras in complex dynamic scenarios lies in the dynamic range, since traditional ways of multiple exposure for high dynamic range (HDR) imaging will fail due to alignment issues. To deal with these two challenges simultaneously, we propose to use an event camera for assistance, which has much faster temporal response and wider dynamic range. Since there does not exist learning data for this brand new imaging setup, we first build a quad-axis imaging system to capture a realistic dataset called REG-HDR, with pairs of fully aligned RS image and its associated events, as well as their corresponding high-speed HDR GS images. We also propose a flow-based network for frame interpolation, compounded with an attention-based fusion network for dynamic range expansion. Experimental results have verified the effectiveness of our proposed algorithm and the superiority of using realistic data for this challenging dural-purpose enhancement task.
Ensemble clustering has shown its promising ability in fusing multiple base clusterings into a probably better and more robust clustering result. Typically, the co-association matrix based ensemble clustering methods attempt to integrate multiple connective matrices from base clusterings by weighted fusion to acquire a common graph representation. However, few of them are aware of the potential noise or corruption from the common representation by direct integration of different connective matrices with distinct cluster structures, and further consider the mutual information propagation between the input observations. In this paper, we propose a Graph Tensor Learning based Ensemble Clustering (GTLEC) method to refine multiple connective matrices by the substantial rank recovery and graph tensor learning. Within this framework, each input connective matrix is dexterously refined to approximate a graph structure by obeying the theoretical rank constraint with an adaptive weight coefficient. Further, we stack multiple refined connective matrices into a three-order tensor to extract their higher-order similarities via graph tensor learning, where the mutual information propagation across different graph matrices will also be promoted. Extensive experiments on several challenging datasets have confirmed the superiority of GTLEC compared with the state-of-the-art.
In this article, we propose a novel and effective incomplete multi-view clustering (IMVC) framework, referred to as incomplete multi-view clustering with regularized hierarchical graph (IMVC_RHG). Different from the existing graph learning-based IMVC methods, IMVC_RHG introduces a novel heterogeneous-graph learning and embedding strategy, which adopts the high-order structures between four tuples for each view, rather than a simple paired-sample intrinsic structure. Besides this, with the aid of the learned heterogeneous graphs, a between-view preserving strategy is designed to recover the incomplete graph for each view. Finally, a consensus representation for each sample is gained with a co-regularization term for final clustering. As a result of integrating these three learning strategies, IMVC_RHG can be flexibly applied to different types of IMVC tasks. Comparing with the other state-of-the-art methods, the proposed IMVC_RHG can achieve the best performances on real-world incomplete multi-view databases.
Most existing multi-view learning methods assume that the dimensions of different views are similar. In real-world applications, it is often the case that the dimension of a view may be extremely small compared with these of other views, resulting in an unbalanced multi-view learning problem. Previous methods for this problem have at least one of the following drawbacks: (1) despising the information of low dimensional views; (2) constructing balanced view-specific inter-instance similarity graphs or employing decision-level fusion, which cannot well learn multi-level inter-view correlations and is limited to category-related tasks such as clustering. To eliminate all these drawbacks, we present an Unbalanced Multi-view Deep Learning (UMDL) method. Considering a low dimensional view usually contains multiple patterns, we construct an overcomplete dictionary with its atoms exceeding the dimension of the original data. We transfer the original data into a combination of atoms and obtain a higher dimensional representation. We propose a sparse multi-view fusion paradigm to explicitly capture the complementarity of multi-view data in a flexible manner. Moreover, we construct positive and negative examples via balanced similarity graphs and employ contrastive learning to train UMDL in a self-supervised manner. Experiments conducted on a toy example and 7 balanced/unbalanced datasets show that UMDL outperforms baseline methods and can be well applied to downstream classification and segmentation tasks. The code is released at https://github.com/xdmvteam/UMDL.
Most existing cross-modal retrieval methods employ two-stream encoders with different architectures for images and texts, e.g., CNN for images and RNN/Transformer for texts. Such discrepancy in architectures may induce different semantic distribution spaces and limit the interactions between images and texts, and further result in inferior alignment between images and texts. To fill this research gap, inspired by recent advances of Transformers in vision tasks, we propose to unify the encoder architectures with Transformers for both modalities. Specifically, we design a cross-modal retrieval framework purely based on two-stream Transformers, dubbed Hierarchical Alignment Transformers (HAT), which consists of an image Transformer, a text Transformer, and a hierarchical alignment module. With such identical architectures, the encoders could produce representations with more similar characteristics for images and texts, and make the interactions and alignments between them much easier. Besides, to leverage the rich semantics, we devise a hierarchical alignment scheme to explore multi-level correspondences of different layers between images and texts. To evaluate the effectiveness of the proposed HAT, we conduct extensive experiments on two benchmark datasets, MSCOCO and Flickr30K. Experimental results demonstrate that HAT outperforms SOTA baselines by a large margin. Specifically, on two key tasks, i.e., image-to-text and text-to-image retrieval, HAT achieves 7.6% and 16.7% relative score improvement of Recall@1 on MSCOCO, and 4.4% and 11.6% on Flickr30k respectively. The code is available at https://github.com/LuminosityX/HAT.
The success of existing multi-view clustering (MVC) relies on the assumption that all views are complete. However, samples are usually partially available due to data corruption or sensor malfunction, which raises the research of incomplete multi-view clustering (IMVC). Although several anchor-based IMVC methods have been proposed to process the large-scale incomplete data, they still suffer from the following drawbacks: i) Most existing approaches neglect the inter-view discrepancy and enforce cross-view representation to be consistent, which would corrupt the representation capability of the model; ii) Due to the samples disparity between different views, the learned anchor might be misaligned, which we referred as the Anchor-Unaligned Problem for Incomplete data (AUP-ID). Such the AUP-ID would cause inaccurate graph fusion and degrades clustering performance. To tackle these issues, we propose a novel incomplete anchor graph learning framework termed Scalable Incomplete Multi-View Clustering with Structure Alignment (SIMVC-SA). Specially, we construct the view-specific anchor graph to capture the complementary information from different views. In order to solve the AUP-ID, we propose a novel structure alignment module to refine the cross-view anchor correspondence. Meanwhile, the anchor graph construction and alignment are jointly optimized in our unified framework to enhance clustering quality. Through anchor graph construction instead of full graphs, the time and space complexity of the proposed SIMVC-SA is proven to be linearly correlated with the number of samples. Extensive experiments on seven incomplete benchmark datasets demonstrate the effectiveness and efficiency of our proposed method. Our code is publicly available at https://github.com/wy1019/SIMVC-SA.
Anchor-based multi-view graph clustering (AMVGC) has received abundant attention owing to its high efficiency and the capability to capture complementary structural information across multiple views. Intuitively, a high-quality anchor graph plays an essential role in the success of AMVGC. However, the existing AMVGC methods only consider single-structure information, i.e., local or global structure, which provides insufficient information for the learning task. To be specific, the over-scattered global structure leads to learned anchors failing to depict the cluster partition well. In contrast, the local structure with an improper similarity measure results in potentially inaccurate anchor assignment, ultimately leading to sub-optimal clustering performance. To tackle the issue, we propose a novel anchor-based multi-view graph clustering framework termed Efficient Multi-View Graph Clustering with Local and Global Structure Preservation (EMVGC-LG). Specifically, a unified framework with a theoretical guarantee is designed to capture local and global information. Besides, EMVGC-LG jointly optimizes anchor construction and graph learning to enhance the clustering quality. In addition, EMVGC-LG inherits the linear complexity of existing AMVGC methods respecting the sample number, which is time-economical and scales well with the data size. Extensive experiments demonstrate the effectiveness and efficiency of our proposed method.
Graph Neural Networks (GNN) are an effective technique for node classification, but their performance is easily affected by the quality of the primitive graph and the limited receptive field of message-passing. In this paper, we propose a new self-attention method, namely Chain of Propagation Prompting (CPP), to address the above issues as well as reduce dependence on label information when employing self-attention for node classification. To do this, we apply the self-attention framework to reduce the impact of a low-quality graph and to obtain a maximal receptive field for the message-passing. We also design a simple pattern of message-passing as the prompt to make self-attention capture complex patterns and reduce the dependence on label information. Comprehensive experimental results on real graph datasets demonstrate that CPP outperforms all relevant comparison methods.
Pansharpening is to fuse a high-resolution panchromatic (PAN) image with a low-resolution multispectral (LRMS) image to obtain an enhanced LRMS image with high spectral and spatial resolution. The current Transformer-based pansharpening methods neglect the interaction between the extracted long- and short-range features, resulting in spectral and spatial distortion in the fusion results. To address this issue, a novel cross Transformer and convolutional neural network (CNN) for pansharpening (CTCP) is proposed to achieve better fusion results by designing a cross mechanism, which can enhance the interaction between long- and short-range features. First, a dual branch feature extraction module (DBFEM) is constructed to extract the features from the LRMS and PAN images, respectively, reducing the aliasing of the two image features. In the DBFEM, to improve the feature representation ability of the network, a cross long-short-range feature module (CLSFM) is designed by combining the feature learning capabilities of Transformer and CNN via the cross mechanism, which achieves the integration of long-short-range features. Then, to improve the ability of spectral feature representation, a spectral feature enhancement fusion module (SFEFM) based on a frequency channel attention is constructed to realize feature fusion. Finally, the shallow features from the PAN image are reused to provide detail features, which are integrated with the fused features to obtain the final pansharpened results. To the best of our knowledge, this is the first attempt to introduce the cross mechanism between Transformer and CNN in pansharpening field. Numerous experiments show that our CTCP outperforms some state-of-the-art (SOTA) approaches both subjectively and objectively. The source code will be released at https://github.com/zhsu99/CTCP.
Multi-view subspace clustering (MVSC), which leverages comprehensive information from multiple views to effectively reveal the intrinsic relationships among instances, has garnered significant research interest. However, previous MVSC research focuses on exploring the cross-view consistent information only in the instance representation hierarchy or affinity relationship hierarchy, which prevents a joint investigation of the multi-view consistency in multiple hierarchies. To this end, we propose a Triple-gRanularity contrastive learning framework for deep mUlti-view Subspace clusTering (TRUST), which benefits from the comprehensive discovery of valuable information from three hierarchies, including the instance, specific-affinity relationship, and consensus-affinity relationship. Specifically, we first use multiple view-specific autoencoders to extract noise-robust instance representations, which are then respectively input into the MLP model and self-representation model to obtain high-level instance representations and view-specific affinity matrices. Then, the instance and specific-affinity relationship contrastive regularization terms are separately imposed on the high-level instance representations and view specific-affinity matrices, ensuring the cross-view consistency can be found from the instance representations to the view-specific affinity matrices. Furthermore, multiple view-specific affinity matrices are fused into a consensus one associated with the consensus-affinity relationship contrastive constraint, which embeds the local structural relationship of high-level instance representations into the consensus affinity matrix. Extensive experiments on various datasets demonstrate that our method is more effective when compared with other state-of-art methods.