Federated learning aims to train a better global model without sharing the sensitive training samples (usually images) of local clients. Since the sample distributions in local clients tend to be different from each other (i.e., non-IID), one of the major challenges for federated learning is to alleviate model degradation when aggregating local models. The degradation can be attributed to the weight divergence that quantifies the difference of local models from different training processes. Furthermore, non-IID also results in feature space heterogeneity during local training, making neurons of local models in the same location have different functions and further exacerbating weight divergence. In this paper, we demonstrate that the problem can be solved by sharing information from the non-sensitive modality (e.g., metadata, non-sensitive descriptions, etc.) while keeping the sensitive information of images protected. In particular, we propose Federated Learning with Adversarial Example and Adversarial Identifier (FedAA) that trains adversarial examples based on the shared non-sensitive modality to fine-tune local models before global aggregation. The training of local models is enhanced by client identifiers that discriminate the source of inputs to force different local models to get similar outputs and be more homogeneous during the local training. Experiments show that FedAA significantly outperforms recent non-IID federated learning algorithms while preserving image privac, by sharing information from non-sensitive modalities.
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Multispectral pedestrian detection is of great importance in various around-the-clock applications, i.e., self-driving and video surveillance. Fusing the features from RGB images and thermal infrared (TIR) images to explore the complementary information between different modalities is one of the most effective manners to improve multispectral pedestrian detection performance. However, the misalignment between different modalities in spatial dimension and modality reliability would introduce harmful information during feature fusion, limiting the performance of multispectral pedestrian detection. To address the above issues, we propose an attentive alignment network, consisting of an attentive position alignment (APA) module and an attentive modality alignment (AMA) module. Our APA module emphasizes pedestrian regions while aligning the pedestrian regions between different modalities. Our AMA module utilizes a channel-wise attention mechanism with illumination guidance to eliminate the imbalance between different modalities. The experiments are conducted on two widely used multispectral detection datasets, KASIT and CVC-14. Our approach surpasses the current state-of-the-art performance on both datasets.
Recent RGB-D semantic segmentation networks are usually manually designed. However, due to limited human efforts and time costs, their performance might be inferior for complex scenarios. To address this issue, we propose the first Neural Architecture Search (NAS) method that designs the network automatically. Specifically, the target network consists of an encoder and a decoder. The encoder is designed with two independent branches, where each branch specializes in extracting features from RGB and depth images, respectively. The decoder fuses the features and generates the final segmentation result. Besides, for automatic network design, we design a grid-like network-level search space combined with a hierarchical cell-level search space. By further developing an effective gradient-based search strategy, the network structure with hierarchical cell architectures is discovered. Extensive results on two datasets show that the proposed method outperforms the state-of-the-art approaches, which achieves a mIoU score of 55.1% on the NYU-Depth v2 dataset and 50.3% on the SUN-RGBD dataset.
Surround-view cameras combined with image depth transformation to 3D feature space and fusion with point cloud features are highly regarded. The transformation of 2D features into 3D feature space by means of predefined sampling points and depth distribution happens throughout the scene, and this process generates a large number of redundant features. In addition, multimodal feature fusion unified in 3D space often happens in the previous step of the downstream task, ignoring the interactive fusion between different scales. To this end, we design a new framework, focusing on the design that can give 3D geometric perception information to images and unify them into voxel space to accomplish multi-scale interactive fusion, and we mitigate feature alignment between modal features by geometric relationships between voxel features. The method has two main designs. First, a Segmentation-guided Image View Transformation module is used to accurately transform the pixel region containing the object into a 3D pseudo-point voxel space with the help of a depth distribution. This allows subsequent feature fusion to be performed in a unified voxel feature. Secondly, a Voxel-centric Consistent Fusion module is used to alleviate the errors caused by depth estimation, as well as to achieve better feature fusion between unified modalities. Through extensive experiments on the KITTI and nuScenes datasets, we validate the effectiveness of our camera-LIDAR fusion method. Our proposed approach shows competitive performance on both datasets and outperforms state-of-the-art methods in certain classes of 3D object detection benchmarks. https://github.com/no-Name128/DLFusion [code release]
Audiovisual self-supervised representation learning has made significant strides in various audiovisual tasks. Existing methods mostly focus on single representation modeling between audio and visual modalities, ignoring the complex correspondence between them, resulting in the inability to execute cross-modal understanding in a more natural audiovisual scene. Several biological studies have shown that human learning is influenced by multi-layered synchronization of perception. To this end, inspired by biology, we argue to exploit the naturally existing relationships in audio and visual modalities to learn audiovisual representations under multilayer perceptual integration. Firstly, we introduce an audiovisual multi-representation pretext task that integrates semantic consistency, temporal alignment, and spatial correspondence. Secondly, we propose a self-supervised audiovisual multi-representation learning approach, which simultaneously learns the perceptual relationship between visual and audio modalities at semantic, temporal, and spatial levels. To establish fine-grained correspondence between visual objects and sounds, an audiovisual object detection module is proposed, which detects potential sounding objects by combining unsupervised knowledge at multiple levels. In addition, we propose a modality-wise loss and a task-wise loss to learn a subspace-orthogonal representation space that makes representation relations more discriminative. Finally, experimental results demonstrate that collectively understanding the semantic, temporal, and spatial correspondence between audiovisual modalities enables the model to perform better on downstream tasks such as sound separation, sound spatialization, and audiovisual segmentation.
Traditional 3D segmentation methods can only recognize a fixed range of classes that appear in the training set, which limits their application in real-world scenarios due to the lack of generalization ability. Large-scale visual-language pre-trained models, such as CLIP, have shown their generalization ability in the zero-shot 2D vision tasks, but are still unable to be applied to 3D semantic segmentation directly. In this work, we focus on zero-shot point cloud semantic segmentation and propose a simple yet effective baseline to transfer the visual-linguistic knowledge implied in CLIP to point cloud encoder at both feature and output levels. Both feature-level and output-level alignments are conducted between 2D and 3D encoders for effective knowledge transfer. Concretely, a Multi-granularity Cross-modal Feature Alignment (MCFA) module is proposed to align 2D and 3D features from global semantic and local position perspectives for feature-level alignment. For the output level, per-pixel pseudo labels of unseen classes are extracted using the pre-trained CLIP model as supervision for the 3D segmentation model to mimic the behavior of the CLIP image encoder. Extensive experiments are conducted on two popular benchmarks of point cloud segmentation. Our method outperforms significantly previous state-of-the-art methods under zero-shot setting (+29.2% mIoU on SemanticKITTI and 31.8% mIoU on nuScenes), and further achieves promising results in the annotation-free point cloud semantic segmentation setting, showing its great potential for label-efficient learning.
As a challenging task, unsupervised person re-identification (Re-ID) aims to optimize the pedestrian matching model based on the unlabeled image frames from surveillance videos. Recently, the fusion with the spatio-temporal clues of pedestrians have been proven effective to improve the performance of classification. However, most of these methods adopt some hard combination approaches by multiplying the visual scores with the spatio-temporal scores, which are sensitive to the noise caused by imprecise estimation of the spatio-temporal patterns in unlabeled datasets and limit the advantage of the fusion model. In this paper, we propose a Graph based Spatio-Temporal Fusion model for high-performance multi-modal person Re-ID, namely G-Fusion, to mitigate the impact of noise. In particular, we construct a graph of pedestrian images by selecting neighboring nodes based on the visual information and the transition time between cameras. Then we use a randomly initialized two-layer GraphSAGE model to obtain the multi-modal affinity matrix between images, and deploy the distillation learning to optimize the visual model by learning the affinity between the nodes. Finally, a graph-based multi-modal re-ranking method is deployed to make the decision in the testing phase for precise person Re-ID. Comprehensive experiments are conducted on two large-scale Re-ID datasets, and the results show that our method achieves a significant improvement of the performance while combined with SOTA unsupervised person Re-ID methods. Specifically, the mAP scores can reach 92.2%, and 80.4% on the Market-1501, and MSMT17 datasets respectively.
Multimodal learning has been widely studied and applied due to its improvement over previous unimodal tasks and its effectiveness on emerging multimodal challenges. However, it has been reported that modal encoders are under-optimized in multimodal learning in contrast to unimodal learning, especially when some modalities are dominant over others. Existing solutions to this problem suffer from two limitations: i) they merely focus on inter-modal balance, failing to consider the influence of intra-modal data on each modality; ii) their implementations heavily rely on unimodal performances or losses, thus being suboptimal for the tasks requiring modal interactions (e.g., visual question answering). To tackle these limitations, we propose I2MCL, a generic Intra- and Inter-Modal Curriculum Learning framework which simultaneously considers both data difficulty and modality balance for multimodal learning. In the intra-modal curriculum, we adopt a pretrained teacher model to obtain knowledge distillation loss as the difficulty measurer, which determines the data weights within the corresponding modality. In the inter-modal curriculum, we utilize a Pareto optimization strategy to measure and compare the gradients from distillation loss and task loss across modalities, capable of determining whether a modality should learn from the task or its teacher. Empirical experiments on various tasks including multimodal classification, visual question answering and visual entailment demonstrate that our proposed I2MCL is able to tackle the under-optimized modality problem and bring consistent improvement to multimodal learning.
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.
PAIF: Perception-Aware Infrared-Visible Image Fusion for Attack-Tolerant Semantic Segmentation
PDF ↗Infrared and visible image fusion is a powerful technique that combines complementary information from different modalities for downstream semantic perception tasks. Existing learning-based methods show remarkable performance, but are suffering from the inherent vulnerability of adversarial attacks, causing a significant decrease in accuracy. In this work, a perception-aware fusion framework is proposed to promote segmentation robustness in adversarial scenes. We first conduct systematic analyses about the components of image fusion, investigating the correlation with segmentation robustness under adversarial perturbations. Based on these analyses, we propose a harmonized architecture search with a decomposition-based structure to balance standard accuracy and robustness. We also propose an adaptive learning strategy to improve the parameter robustness of image fusion, which can learn effective feature extraction under diverse adversarial perturbations. Thus, the goals of image fusion (i.e., extracting complementary features from source modalities and defending attack) can be realized from the perspectives of architectural and learning strategies. Extensive experimental results demonstrate that our scheme substantially enhances the robustness, with gains of 15.3% mIOU of segmentation in the adversarial scene, compared with advanced competitors. The source codes are available at https://github.com/LiuZhu-CV/PAIF.
Most of the existing bi-modal (RGB-D or RGB-T) salient object detection methods attempt to integrate multimodality information through various fusion strategies. However, existing methods lack a clear definition of salient regions before feature fusion, which results in poor model robustness. To tackle this problem, we propose a novel prototype, the saliency prototype, which captures common characteristic information among salient objects. A prototype contains inherent characteristics information of multiple salient objects, which can be used for feature enhancement of various salient objects. By utilizing the saliency prototype, we provide a clearer definition of salient regions and enable the model to focus on these regions before feature fusion, avoiding the influence of complex backgrounds during the feature fusion stage. Additionally, we utilize the saliency prototypes to address the quality issue of auxiliary modality. Firstly, we apply the saliency prototypes obtained by the primary modality to perform semantic enhancement of the auxiliary modality. Secondly, we dynamically allocate weights for the auxiliary modality during the feature fusion stage in proportion to its quality. Thus, we develop a new bi-modal salient detection architecture Saliency Prototype Network (SPNet), which can be used for both RGB-D and RGB-T SOD. Extensive experimental results on RGB-D and RGB-T SOD datasets demonstrate the effectiveness of the proposed approach against the state-of-the-art. Our code is available at https://github.com/ZZ2490/SPNet.
Visual storytelling aims to generate a narrative based on a sequence of images, necessitating both vision-language alignment and coherent story generation. Most existing solutions predominantly depend on paired image-text training data, which can be costly to collect and challenging to scale. To address this, we formulate visual storytelling as a visual-conditioned story generation problem and propose a text-only training method that separates the learning of cross-modality alignment and story generation. Our approach specifically leverages the cross-modality pre-trained CLIP model to integrate visual control into a story generator, trained exclusively on text data. Moreover, we devise a training-free visual condition planner that accounts for the temporal structure of the input image sequence while balancing global and local visual content. The distinctive advantage of requiring only text data for training enables our method to learn from external text story data, enhancing the generalization capability of visual storytelling. We conduct extensive experiments on the VIST benchmark, showcasing the effectiveness of our approach in both in-domain and cross-domain settings. Further evaluations on expression diversity and human assessment underscore the superiority of our method in terms of informativeness and robustness.
DTF-Net: Category-Level Pose Estimation and Shape Reconstruction via Deformable Template Field
PDF ↗Estimating 6D poses and reconstructing 3D shapes of objects in open-world scenes from RGB-depth image pairs is challenging. Many existing methods rely on learning geometric features that correspond to specific templates while disregarding shape variations and pose differences among objects in the same category. As a result, these methods underperform when handling unseen object instances in complex environments. In contrast, other approaches aim to achieve category-level estimation and reconstruction by leveraging normalized geometric structure priors, but the static prior-based reconstruction struggles with substantial intra-class variations. To solve these problems, we propose the DTF-Net, a novel framework for pose estimation and shape reconstruction based on implicit neural fields of object categories. In DTF-Net, we design a deformable template field to represent the general category-wise shape latent features and intra-category geometric deformation features. The field establishes continuous shape correspondences, deforming the category template into arbitrary observed instances to accomplish shape reconstruction. We introduce a pose regression module that shares the deformation features and template codes from the fields to estimate the accurate 6D pose of each object in the scene. We integrate a multi-modal representation extraction module to extract object features and semantic masks, enabling end-to-end inference. Moreover, during training, we implement a shape-invariant training strategy and a viewpoint sampling method to further enhance the model's capability to extract object pose features. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superiority of DTF-Net in both synthetic and real scenes. Furthermore, we show that DTF-Net effectively supports grasping tasks with a real robot arm.
Large-scale Vision-Language Pre-training (VLP) model, e.g., CLIP, has demonstrated its natural advantage in generating textual descriptions for images. These textual descriptions afford us greater semantic monitoring insights while not requiring any domain knowledge. In this paper, we propose a new prompt learning paradigm for unsupervised visible-infrared person re-identification (USL-VI-ReID) by taking full advantage of the visual-text representation ability from CLIP. In our framework, we establish a learnable cluster-aware prompt for person images and obtain textual descriptions allowing for subsequent unsupervised training. This description complements the rigid pseudo-labels and provides an important semantic supervised signal. On that basis, we propose a new memory-swapping contrastive learning, where we first find the correlated cross-modal prototypes by the Hungarian matching method and then swap the prototype pairs in the memory. Thus typical contrastive learning without any change could easily associate the cross-modal information. Extensive experiments on the benchmark datasets demonstrate the effectiveness of our method. For example, on SYSU-MM01 we arrive at 54.0% in terms of Rank-1 accuracy, over 9% improvement against state-of-the-art approaches. Code is available at https://github.com/CzAngus/CCLNet.
Action recognition research has gained significant attention with two dominant unimodal approaches: skeleton-based and RGB video-based. While the former is known for its robustness in complex backgrounds, the latter provides rich environmental information useful for context-based analysis. However, the fusion of these two modalities remains an open challenge. In this paper, we propose a Spatial Transformer & Selective Temporal encoder (ST&ST) for skeleton-based action recognition by constructing two modules: Reranking-Enhanced Dynamic Mask Transformer (RE-DMT) and Selective Kernel Temporal Convolution (SK-TC). The RE-DMT captures global spatial features, while the dynamic mask strategy and reranking strategy reduce redundancy. The SK-TC captures both long-term and short-term temporal features and enables adaptive fusion. Furthermore, in two phases, we propose a Homogeneous-Heterogeneous Multimodal Network (HHMNet) for multi-modal action recognition. In the first phase, contrastive learning is employed to achieve implicit semantic fusion within the four homogeneous skeletal modalities (joint, bone, etc.). In the second phase, the fusion of heterogeneous modalities (skeleton & RGB video) is carried out at three levels: model, feature, and decision. At the model level, the powerful skeleton-based model from the previous phase provides explicit attention guidance to the RGB video-based model. At the feature level, multi-part contrastive learning enables semantic distillation between heterogeneous modalities. At the decision level, ensemble learning combines outputs for final action recognition. We evaluate our proposed ST&ST guided HHMNet on NTU RGB+D 60 & 120 and NW-UCLA datasets and demonstrate that it achieves state-of-the-art performance in both skeleton-based and multi-modal action recognition tasks.
Graph Neural Networks (GNNs) have garnered considerable interest due to their exceptional performance in a wide range of graph machine learning tasks. Nevertheless, the majority of GNN-based approaches have been examined using well-annotated benchmark datasets, leading to suboptimal performance in real-world graph learning scenarios. To bridge this gap, the present paper investigates the problem of graph transfer learning in the presence of label noise, which transfers knowledge from a noisy source graph to an unlabeled target graph. We introduce a novel technique termed Balance Alignment and Information-aware Examination (ALEX) to address this challenge. ALEX first employs singular value decomposition to generate different views with crucial structural semantics, which help provide robust node representations using graph contrastive learning. To mitigate both label shift and domain shift, we estimate a prior distribution to build subgraphs with balanced label distributions. Building on this foundation, an adversarial domain discriminator is incorporated for the implicit domain alignment of complex multi-modal distributions. Furthermore, we project node representations into a different space, optimizing the mutual information between the projected features and labels. Subsequently, the inconsistency of similarity structures is evaluated to identify noisy samples with potential overfitting. Comprehensive experiments on various benchmark datasets substantiate the outstanding superiority of the proposed ALEX in different settings.
Cross-Lingual Transfer of Large Language Model by Visually-Derived Supervision Toward Low-Resource Languages
PDF ↗Recent progress on vision and language research has shown that visual supervision improves the performance of large language models (LLMs) in various natural language processing (NLP) tasks. In particular, the Vokenization approach [65] initiated a new way of incorporating visual information into LLM training, demonstrating the potential of visual supervision for NLP tasks in a monolingual (i.e., English) setting. Given the effectiveness of visual information in human communication among people who speak different languages, we tackle an ambitious question in this paper; can we expect that visual supervision contributes to cross-lingual transfer learning from a high-resource language to low-resource languages in NLP tasks? To study this hypothesis, we build a cross-lingual Vokenization model and train a cross-lingual LLM on three languages, English, Urdu, and Swahili, in which the last two are considered low-resource languages. The experimental results demonstrate that our visually-supervised cross-lingual transfer learning method significantly improves the LLM performance in multiple cross-lingual NLP tasks such as XNLI, NER, and TyDiQA tasks for low-resource languages. We also qualitatively and quantitatively demonstrate that the benefit of our approach increases as the linguistic distance between low-and high-resource languages grows larger.
Collaborative perception offers a promising solution to overcome challenges such as occlusion and long-range data processing. However, limited sensor accuracy leads to noisy poses that misalign observations among vehicles. To address this problem, we propose the FeaCo, which achieves robust Feature-level Consensus among collaborating agents in noisy pose conditions without additional training. We design an efficient Pose-error Rectification Module (PRM) to align derived feature maps from different vehicles, reducing the adverse effect of noisy pose and bandwidth requirements. We also provide an effective multi-scale Cross-level Attention Module (CAM) to enhance information aggregation and interaction between various scales. Our FeaCo outperforms all other localization rectification methods, as validated on both the collaborative perception simulation dataset OPV2V and real-world dataset V2V4Real, reducing heading error and enhancing localization accuracy across various error levels. Our code is available at: https://github.com/jmgu0212/FeaCo.git.
Subspace clustering, which recoveries the subspace representation in the form of an affinity graph, has drawn tons of attention due to its effectiveness in various clustering tasks. However, existing subspace clustering methods are usually fed with raw data, which may lead to a suboptimal result since it is difficult to directly and accurately depict the inherent relation between data points. In this paper, we propose a novel subspace clustering method by holistically utilizing the pairwise similarity and graph geometric structure. Our model first constructs an initial subspace representation by means of self-expression, which is able to depict the global structure of data. Then, we use an effective metric to recover an intrinsic matrix with pairwise similarity based on the obtained representation, which further preserves the local structure. Besides, we propose to facilitate the downstream subspace learning task by searching for a smooth representation of the original data, which is obtained by applying a low-pass filter to retain the graph geometric features. By leveraging the subtasks of learning the smooth representation, performing the subspace learning, and recovering the intrinsic similarity matrix in a unified learning framework, each subtask can be alternately boosted. Experiments on several benchmark data sets have been conducted to verify the proposed method.
Multi-view clustering (MVC) integrates information from different views to improve clustering performance compared to single-view clustering. However, the raw multi-view data in the feature space often contains irrelevant information to the clustering task, which is difficult to separate using existing methods. This irrelevant information is processed equally with clustering information, negatively impacting the final clustering performance. In this paper, we propose a new framework for multi-view clustering information reinforcement network (MVCIR-net) to alleviate these problems. Our method gives practical clustering meaning to the clustering distribution layer by contrastive learning. Then, the trusted neighbor instances distribution of the normalized graph is debias aggregated to form the clustering information propensity distribution, and the clustering information distribution is made to fit this distribution. In addition, the coupling degree of the clustering information distribution in different views on the same sample should be enhanced. Through the aforementioned strategies, the raw data is fuzzy mapped into clustering information, and the network's ability to recognize clustering information is strengthened. Finally, the fuzzy mapping data is input into the network and reconstructed to evaluate the quality of the extracted clustering information. Extensive experiments on public multi-view datasets show that MVCIR-net achieves superior clustering effectiveness and the ability to identify clustering information.