Identifying novel human-object interaction (HOI) classes with scarce data is a challenging and crucial task in computer vision. Existing methods mainly use coarse global visual information to build class prototypes in meta-learning. Despite their promising results, these methods often fail to capture fine-grained interaction semantics and effectively learn from data with low inter-class variance, leading to suboptimal performance in distinguishing similar categories. To overcome these issues, we propose a new model called hierarchical relation network for few-shot HOI recognition (FS-HOI). This model integrates multi-level interaction clues, spanning from coarse to fine-grained, to enhance HOI features. It employs a unified graph network to capture intra- and inter-relationships among human parts with contextual information, augmented by language-guided attention for semantic mining within each interactive sub-graph. In contrast to conventional methods that depend on global class prototype comparisons, our approach advances metric learning by integrating contrastive mechanisms, utilizing rich instance pairs as comparative references to effectively address inter-class variance. Furthermore, a graph relation network leverages prior knowledge of unknown HOIs, embedding task-specific features into contrastive instances. Our method establishes a new state-of-the-art on three few-shot HOI datasets, with substantial performance gains and ablation studies confirming the efficacy of each component.

Generalized Category Discovery (GCD) typically relies on the pre-trained Vision Transformer (ViT) to extract features from a global receptive field, followed by contrastive learning to simultaneously classify unlabeled known classes and unknown classes without priors. Owing to the deficiency in the modeling capacity for inner-patch local information within ViT, current methods primarily focus on discriminative features at global level. This results in a model with more yet scattered attention, where neither excessive nor insufficient focus can grasp subtle differences to classify fine-grained unknown and known categories. To address this issue, we propose the AptGCD to deliver apt attention for GCD. It mimics the human brain how leveraging visual perception to refine local attention and comprehend global context by proposing a Meta Visual Prompt (MVP) and Prompt Transformer (PT). MVP is introduced into GCD for the first time, refining channel-level attention, while adaptively self-learning unique inner-patch features as prompts to achieve local visual modeling for our prompt transformer. Yet, relying solely on detailed features can lead to skewed judgments. Hence, PT harmonizes local and global representations, guiding the model's interpretation of features through broader contexts, thereby capturing more useful details with less attention. Extensive experiments on seven datasets demonstrate that AptGCD outperforms current methods, it achieves an average proportional ‘New’ accuracy improvement of approximately 9.2% over SOTA method on the all four fine-grained datasets, establishing a new standard in the field. The code is available at https://github.com/wendy26zhang/AptGCD.

The Dense Audio-Visual Event Localization (DAVEL) task aims to temporally localize all audio-visual events within untrimmed videos. Existing methods typically operate under a closed-set assumption, which limits their ability to generalize to test videos containing previously unseen event categories-an essential capability for open-world scenarios. To this end, we propose a novel task setting, Open-Vocabulary Dense Audio-Visual Event Localization (OV-DAVEL), along with a one-stage method Open-DAVTR designed to detect events that were not observed during training. Open-DAVTR consists of two core components: a class-agnostic foreground-aware generator and a multi-modal semantic-aware classifier. Specifically, the generator is a Detection Transformer-based module that produces event proposals while adaptively attending to discriminative foreground snippets for downstream classification. The classifier leverages rich temporal representations and context-aware textual semantics to effectively recognize events, regardless of whether they were seen during training. In addition, we establish comprehensive OV-DAVEL benchmarks across various settings. Experimental results show that our model significantly outperforms existing baselines in detecting both seen and unseen events, highlighting its effectiveness in open-vocabulary event localization. Code and data are available at: https://github.com/yujialele/OV-DEAVEL.

With the rapid evolution of deepfake technologies, attackers can arbitrarily alter the intended message of a video by modifying just a few frames. To this extent, simplistic binary judgments of entire videos increasingly seem less convincing and interpretable. Although numerous efforts have been made to develop fine-grained interpretations, these typically depend on elaborate annotations, which are both costly and challenging to obtain in real-world scenarios. To push the related frontier research, we introduce a novel task called Weakly-Supervised Deepfake Localization (WSDL), which aims to identify manipulated frames only with cushy video-level labels. Meanwhile, we propose a new framework named Bi-stream coteaching Deepfake Localization (CoDL), which advances the WSDL task through a progressive mutual refinement strategy across complementary spatial and temporal modalities. The CoDL framework incorporates an inconsistency perception module that discerns subtle forgeries by assessing spatial and temporal incoherence, and a prototype-based enhancement module that mitigates frame noise and amplifies discrepancies to create a robust feature space. Additionally, a progressive coteaching mechanism is implemented to facilitate the exchange of valuable knowledge between modalities, enhancing the detection of subtle frame-level forgery features and thereby improving the model’s generalization capabilities. Extensive experiments are conducted to demonstrate the superiority of our approach, particularly achieving an impressive 8.83% improvement in AUC on highly compressed datasets when learning from weak supervision.

Audio-Visual Event (AVE) Localization aims to identify and classify video segments that are both audible and visible, a field that has seen substantial progress in recent years. Existing methods operate under a closed-set assumption and struggle to recognize unknown events in open-world scenarios. To better adapt to real-life applications, we introduce the Open Set Audio-Visual Event Localization task and propose a novel and effective network called OpenAVE based on evidential deep learning. To the best of our knowledge, this is the first effort to address this challenge. Our approach encompasses deep evidential AVE classification and event-relevant prediction, targeting the nuanced demands of open-set environments. The deep evidential AVE classification manages event classification uncertainty by extracting class evidence from segment-specific representations enriched with multi-scale context. To effectively distinguish between unknown events and background segments, event-relevant prediction utilizes positive-unlabeled learning. Futhermore, a learnable Gaussian-prior prediction branch is adopted to enhance the performance of event-relevant prediction. Experimental results demonstrate that OpenAVE significantly outperforms state-of-the-art models on the Audio-Visual Event dataset, confirming the effectiveness of our proposed method.

The existing one-stage multi-object tracking (MOT) algorithms have achieved satisfactory performance benefiting from a large amount of labeled data. However, acquiring plenty of laborious annotated frames is not practical in real applications. To reduce the cost of human annotations, we propose Heterogeneous Diversity driven Active Multi-Object Tracking (HD-AMOT), to infer the most informative frames for any MOT tracker by observing the heterogeneous cues of samples. HD-AMOT defines the diversified informative representation by encoding the geometric and semantic information, and formulates the frame inference strategy as a Markov decision process to learn an optimal sampling policy based on the designed informative representation. Specifically, HD-AMOT consists of a diversified informative representation module as well as an informative frame selection network. The former produces the signal characterizing the diversity and distribution of frames, and the latter receives the signal and conducts multi-frame cooperation to enable batch frame sampling. Extensive experiments conducted on the MOT15, MOT17, MOT20, and Dancetrack datasets demonstrate the efficacy and effectiveness of HD-AMOT. Experiments show that under 50% budget our HD-AMOT can achieve similar or even higher performance as fully-supervised learning.

Few-shot learning (FSL) for human-object interaction aims at classifying samples of new unseen HOI classes with only a few labeled samples available. Although progress has been made in few-shot human-object interaction, most of the existing methods encounter two issues in handling fine-grained interactions: the inability to capture more subtle interactive clues and the inadequacy in learning from data with low inter-class variance. To tackle the first issue, we propose a hierarchical reasoning network to integrate multi-level interactive clues (from coarse to fine-grained) for strengthening HOI representations. The hierarchical relation module mainly captures and aggregates more discriminative relation information among human parts at multiple levels (including the human instance, action region, and body part levels) and objects via a unified graph and exploits a language-guided attentive fusion way to highlight informative features of each interaction level. To address the second issue, we introduce a contrastive learning mechanism to alleviate the inter-class variance. Compared with the previous ProtoNet-based methods, our model generates more discriminative representations for low inter-class variance data, since it makes full use of potential contrastive pairs in each training episode. Extensive experimental results on two standard benchmarks demonstrate that the proposed model performs favorably against state-of-the-art FS-HOI methods.

The current social media is flooded with hyper realistic face-synthetic videos due to the explosion of DeepFake technology that has brought a serious impact on human society security, which calls for further exploring on deepfake video detection methods. Existing methods attempt to isolated capture spatial artifacts or extract the homogeneous temporal inconsistency to detect deepfake video, but little attention has been paid to the exploitation of dynamic spatial-temporal inconsistency. To mitigate this issue, in this paper, we propose a novel Multi-Rate Excitation Network (MRE-Net) to effectively excite dynamic spatial-temporal inconsistency from the perspective of multiple rates for deepfake video detection. The proposed MRE-Net is composed of two components: Bipartite Group Sampling (BGS) and multiple rate branches. The BGS draws the entire video into multiple bipartite groups with different rates to cover various face motion dynamic evolution. We further design multiple rate branches to capture both short-term and long-term spatial-temporal inconsistency from corresponding bipartite groups of BGS. Concretely, for the early stages of the multi-rate branches, Momentary Inconsistency Excitation (MIE) module is developed to encode the spatial artifacts and intra-group short-term temporal inconsistency. Meanwhile, for the last stages of the multi-rate branches, Longstanding Inconsistency Excitation (LIE) module is constructed to perceive inter-group long-term temporal dynamics. Extensive experiments and visualizations conducted on four popular datasets demonstrate the effectiveness of the proposed method against state-of-the-art deepfake detection methods.

As face forgery techniques have become more mature, the proliferation of deepfakes may threaten the security of human society. Although existing deepfake detection methods achieve good performance for in-dataset evaluation, it remains to be improved in the generalization ability, where the representation of the imperceptible artifacts plays a significant role. In this paper, we propose an Interactive Two-Stream Network (ITSNet) to explore the discriminant inconsistency representation from the perspective of cross-modality. In particular, the patch-wise Decomposable Discrete Cosine Transform (DDCT) is adopted to extract fine-grained high-frequency clues, and information from different modalities communicates with each other via a designed interaction module. To perceive the temporal inconsistency, we first develop a Short-term Embedding Module (SEM) to refine subtle local inconsistency representation between adjacent frames, and then a Long-term Embedding Module (LEM) is designed to further refine the erratic temporal inconsistency representation from the long-range perspective. Extensive experimental results conducted on three public datasets show that ITSNet outperforms the state-of-the-art methods both in terms of in-dataset and cross-dataset evaluations.

The supervised one-shot multi-object tracking (MOT) algorithms have achieved satisfactory performance benefiting from a large amount of labeled data. However, in real applications, acquiring plenty of laborious manual annotations is not practical. It is necessary to adapt the one-shot MOT model trained on a labeled domain to an unlabeled domain, yet such domain adaptation is a challenging problem. The main reason is that it has to detect and associate multiple moving objects distributed in various spatial locations, but there are obvious discrepancies in style, object identity, quantity, and scale among different domains. Motivated by this, we propose a novel inference-domain network evolution to enhance the generalization ability of the one-shot MOT model. Specifically, we design a spatial topology-based one-shot network (STONet) to perform the one-shot MOT task, where a self-supervision mechanism is employed to stimulate the feature extractor to learn the spatial contexts without any annotated information. Furthermore, a temporal identity aggregation (TIA) module is proposed to assist STONet to weaken the adverse effects of noisy labels in the network evolution. This designed TIA aggregates historical embeddings with the same identity to learn cleaner and more reliable pseudo labels. In the inference domain, the proposed STONet with TIA performs pseudo label collection and parameter update progressively to realize the network evolution from the labeled source domain to an unlabeled inference domain. Extensive experiments and ablation studies conducted on MOT15, MOT17, and MOT20, demonstrate the effectiveness of our proposed model.

The popular tracking-by-detection paradigm of multi-object tracking (MOT) takes detections of each frame as the input and associates detections from one frame to another. Existing association methods based on the relative motion have attracted attention, because they restrain the effect of noisy detections and improve the performance of MOT. However, these methods depend only on the immediately previous frame, which may easily lead to inaccurate matches and even large accumulated errors. Furthermore, multiple objects involved in occlusions are not fully exploited in these existing methods, which leads to the aggravation of inaccurate matches. Motivated by these issues, we design the pivot to represent each object and propose a novel pivot association network (PANet) for the MOT task. Specifically, pivots are learned from spatial semantic and historical contextual clues, which alleviates the dependency on the immediately previous frame. Our online tracker PANet employs pivots and a lightweight associator to localize tracklets of objects, which can inhibit noise detections and improve the accuracy of tracklet prediction by learning the correlation responses between pivots and spatial search areas. Extensive experiments conducted on two-dimensional MOT15, MOT16, MOT17, and MOT20 demonstrate the effectiveness of the proposed method against numerous state-of-the-art MOT trackers.

Object detection from remote sensing images (RSIs) is a basic topic in the area of aviation and satellite image processing, which has great effects on geological disaster detection, agricultural planning, and land utilization. However, it is always faced with several severe difficulties. For instance, the scale of the target spans over a very wide range, and the difference between the target size and the image size is huge, as some targets only account for a dozen pixels compared with the RSI of the megapixel level. In this work, an innovative object detection network (GLNet) is proposed for remote sensing imagery. Our approach integrates global context clues extracted by the multiscale perception (MSP) module and local spatial contextual correlations encoded by Clip long short-term memory (Clip-LSTM). These rich semantic features are further exploited to design a self-adapted anchor (SA) module to alleviate the scale variations in RSIs. Extensive experiments are executed on several public easily accessed benchmarks, including DOTA, NWPU VHR-10, and DIOR. Experimental results have demonstrated that our GLNet outperforms numerous latest methods.

As one of the fundamental visual tasks in the unmanned driving area, lane detection attracts increasing attention. In practical applications, lane points are very difficult to localize because they usually appear to be sparse and incomplete due to the influence of illumination and environment. Conventional lane detection methods rely on coarse features and carefully designed postprocessing to detect the lane lines. However, these methods are usually slow, and the stability and generalization ability are unsatisfactory. In this paper, we propose a novel lane detection network – Fast-HBNet(Fast-Hybrid Branch Network), which exploits both global semantic information and spatial contexts. To enlarge receptive fields and encode more detailed information, the compound transformation is employed and the proposed hybrid branch network extracts four diverse feature maps with different receptive fields and spatial contexts. Besides, we design a Hierarchical Feature Learning (HFL) module to learn lane features from the scale, channel, and spatial levels to enhance the generalization ability of our detector. These features are further selectively coalesced to generate unified lane feature maps with large receptive fields and rich detailed information. In other words, our network can encode the global semantic information from the high-resolution feature maps and the fine-grained details in the low-resolution feature maps. Experimental results conducted on the TuSimple (2017) and CULane [Panet al.(2018)] datasets demonstrate that the proposed Fast-HBNet outperforms numerous state-of-the-art lane detectors in both speed and accuracy. Particularly, Fast-HBNet achieves an accuracy of 96.88% on the TuSimple dataset at a speed of 76 FPS.

Few-shot classification aims to recognize new classes by learning reliable models from very few available samples. It could be very challenging when there is no intersection between the alreadyknown classes (base set) and the novel set (new classes). To alleviate this problem, we propose to evolve the network (for the base set) via label propagation and self-supervision to shrink the distribution difference between the base set and the novel set. Our network evolution approach transfers the latent distribution from the already-known classes to the unknown (novel) classes by: (a) label propagation of the novel/new classes (novel set); and (b) design of dual-task to exploit a discriminative representation to effectively diminish the overfitting on the base set and enhance the generalization ability on the novel set. We conduct comprehensive experiments to examine our network evolution approach against numerous state-of-the-art ones, especially in a higher way setup and cross-dataset scenarios. Notably, our approach outperforms the second best state-of-the-art method by a large margin of 3.25% for one-shot evaluation over miniImageNet.

Unsupervised domain adaptation (UDA) for person re-identification (re-ID) is a challenging task due to large variations in human classes, illuminations, camera views, and so on. Currently, existing UDA methods focus on two-domain adaptation and are generally trained on one labeled source set and adapted on the other unlabeled target set. In this article, we put forward a new issue on person re-ID, namely, unsupervised multi-target domain adaptation (UMDA). It involves one labeled source set and multiple unlabeled target sets, which is more reasonable for practical real-world applications. Enabling UMDA has to learn the consistency for multiple domains, which is significantly different from the UDA problem. To ensure distribution consistency and learn the discriminative embedding, we further propose the Camera Identity-guided Distribution Consistency method that performs an alignment operation for multiple domains. The camera identities are encoded into the image semantic information to facilitate the adaptation of features. According to our knowledge, this is the first attempt on the unsupervised multi-target domain adaptation learning. Extensive experiments are executed on Market-1501, DukeMTMC-reID, MSMT17, PersonX, and CUHK03, and our method has achieved very competitive re-ID accuracy in multi-target domains against numerous state-of-the-art methods.

Object detection on remote sensing images plays an important role in image analysis and understanding. The generation of anchors is a significant factor due to the diverse variations in object size and clustered small objects in remote sensing images. In this paper, we focus on this issue, and propose an anchor generator with continuously variable scales leading by semantic information and build a Scalable Object Detection Network (SODNet) to detect remote sensing targets. The proposed network exploits semantic information to guide the generation of anchors and improves the target scale variation to adapt to different remote sensing images. We execute extensive experiments on widely employed NWPU VHR-10 dataset and DOTA dataset against numerous state-of-the-art methods, and the experimental results demonstrate the performance of the proposed method, especially on small targets.

There are two key components that can be leveraged for visual tracking: (a) object appearances; and (b) object motions. Many existing techniques have recently employed deep learning to enhance visual tracking due to its superior representation power and strong learning ability, where most of them employed object appearances but few of them exploited object motions. In this work, a deep spatial and temporal network (DSTN) is developed for visual tracking by explicitly exploiting both the object representations from each frame and their dynamics along multiple frames in a video, such that it can seamlessly integrate the object appearances with their motions to produce compact object appearances and capture their temporal variations effectively. Our DSTN method, which is deployed into a tracking pipeline in a coarse-to-fine form, can perceive the subtle differences on spatial and temporal variations of the target (object being tracked), and thus it benefits from both off-line training and online fine-tuning. We have also conducted our experiments over four largest tracking benchmarks, including OTB-2013, OTB-2015, VOT2015, and VOT2017, and our experimental results have demonstrated that our DSTN method can achieve competitive performance as compared with the state-of-the-art techniques. The source code, trained models, and all the experimental results of this work will be made public available to facilitate further studies on this problem.

To configure successful privacy settings for social image sharing, two issues are inseparable: 1) content sensitiveness of the images being shared; and 2) trustworthiness of the users being granted to see the images. This paper aims to consider these two inseparable issues simultaneously to recommend fine-grained privacy settings for social image sharing. For achieving more compact representation of image content sensitiveness (privacy), two approaches are developed: 1) a deep network is adapted to extract 1024-D discriminative deep features; and 2) a deep multiple instance learning algorithm is adopted to identify 280 privacy-sensitive object classes and events. Second, users on the social network are clustered into a set of representative social groups to generate a discriminative dictionary for user trustworthiness characterization. Finally, both the image content sensitiveness and the user trustworthiness are integrated to train a tree classifier to recommend fine-grained privacy settings for social image sharing. Our experimental studies have demonstrated both the efficiency and the effectiveness of our proposed algorithms.

In this paper, a layer-wise mixture model (LMM) is developed to support hierarchical visual recognition, where a Bayesian approach is used to automatically adapt the visual hierarchy to the progressive improvements of the deep network along the time. Our LMM algorithm can provide an end-to-end approach for jointly learning: (a) the deep network for achieving more discriminative deep representations for object classes and their inter-class visual similarities; (b) the tree classifier for recognizing large numbers of object classes hierarchically; and (c) the visual hierarchy adaptation for achieving more accurate assignment and organization of large numbers of object classes. By learning the tree classifier, the deep network and the visual hierarchy adaptation jointly in an end-to-end manner, our LMM algorithm can achieve higher accuracy rates on hierarchical visual recognition. Our experiments are carried on ImageNet1K and ImageNet10K image sets, which have demonstrated that our LMM algorithm can achieve very competitive results on the accuracy rates as compared with the baseline methods.

To achieve automatic recommendation of privacy settings for image sharing, a new tool called iPrivacy (image privacy) is developed for releasing the burden from users on setting the privacy preferences when they share their images for special moments. Specifically, this paper consists of the following contributions: 1) massive social images and their privacy settings are leveraged to learn the object-privacy relatedness effectively and identify a set of privacy-sensitive object classes automatically; 2) a deep multi-task learning algorithm is developed to jointly learn more representative deep convolutional neural networks and more discriminative tree classifier, so that we can achieve fast and accurate detection of large numbers of privacy-sensitive object classes; 3) automatic recommendation of privacy settings for image sharing can be achieved by detecting the underlying privacy-sensitive objects from the images being shared, recognizing their classes, and identifying their privacy settings according to the object-privacy relatedness; and 4) one simple solution for image privacy protection is provided by blurring the privacy-sensitive objects automatically. We have conducted extensive experimental studies on real-world images and the results have demonstrated both the efficiency and effectiveness of our proposed approach.

In this paper, an automatic image-text alignment algorithm is developed for achieving more accurate indexing and retrieval of large-scale web images. First, large-scale web pages are crawled, where the informative images and their most relevant auxiliary text blocks are extracted. Second, parallel image clustering is performed to partition large-scale informative web images into a large number of clusters. By grouping the visually-similar (near-duplicate) web images into the same cluster, our parallel image clustering algorithm can significantly reduce the huge uncertainty on the relatedness between the web images and their auxiliary text terms, which can provide a good starting point for supporting automatic image-text alignment. Finally, a relevance re-ranking algorithm is developed to identify the most relevant visual text terms for the visually-similar web images in the same cluster. Our experiments on large-scale web images have obtained very positive results.

Cloud computing is becoming increasingly popular due to its power in providing high-performance and flexible service capabilities. More and more internet users have accepted this innovative service model and been using various cloud-based services every day. However, these service-using data is quite valuable for marketing purposes, as it can reflect a user's interest and service-using pattern. Therefore, the privacy issues have been brought out. Recently, many studies focus on access control and other traditional security problems in cloud, and little studied on the topic of the private service data publishing. In this paper, we study the private service data publishing problem by representing the data with a hyper graph, which is quite efficient to illustrate complex relationships among users. We first formulate the problem with a popular background knowledge attack model named rank attack, and then provide an anonymization-based method to prevent the released data from such attacks. We also take data utility into consideration by defining specific information loss metrics. The performances of the methods have been validated by two sets of synthetic data.

Railway dangerous goods tracing is a typical application of the sensor network. Application correlation among the sensor, carriage and train is a graph relationship which can be described by using RDF frameworks. It requires data management system to manage a large scale of ever-increasing RDF data, and support semantic access for monitoring the safety state of the environment inside the carriage. For these problems, this paper proposes RDF data query and management method based on HBase and structure index, and optimization method of query engine. The method is enforced by rewriting SPARQL statements according of correlation degree between them, and at querying time, "structure-level" index is used to identify the groups of RDF data, then the "data-level" data matching utilizes the proposed scalable storage mechanism based on hash-oriented multiple table partition of data entity class. As shown in our experiments, our approach can effectively reduce the semantic query time, enhance storage scalability and effective support multi-criteria query of sensor data.

Service composition allows distributed application, such as multimedia application, to be composed from atomic service units and to adapt dynamically to users' requirements and environment conditions in pervasive computing system. It augments the adaptation action space for the application of pervasive computing. According to the multidimensional QoS (Quality of Service) requirement of pervasive computing system, we proposed a comprehensive service composition method to enhance the capability of application adaptation. First, according to a hierarchy policy model and a policy specification language, strengthened by event calculus, service discovery policy action integrating the situation of user, application, environment and resource can be triggered. Secondly, the proposed physical space model can support the location-aware service discovery and explicit range query to improve the efficiency of the query. To the end, an adaptation policy evaluation model is utilized to maximize an evaluation criterion–quality of satisfaction of users and environment by optimizing the optional service selection and the composition path. Through experiment and discussion of the algorithm, the paper further illustrates the great potential advantage of the solution to service composition.

Layered streaming in overlay networks has drawn great interests since not only can it accomodate large scales of clients but also it handles client heterogeneities. However, to our knowledge, there's still a lack of overlay construction (i.e. neighbor selection) approach suited for layered streaming, because i) In existing works neighbors are selected only based on their network conditions. However, a neighbor with good network condition may not be able to provide sufficient layers (e.g. a neighbor in the same LAN), ii) Previous works usually select "good" neighbors for the new node, ignoring that the joining of the new node could also be utilized to improve the performance of existing nodes. In this paper, OCals - a two-stage QoS aware overlay construction approach for layered streaming is proposed. The main contribution of OCals is that i) when selecting neighbor, it considers existing nodes' network conditions and their providing layers as a whole; ii) it guarantees the QoS for the new node as well as improves the QoS for existing nodes so that with the joining of new nodes, the performance of the overlay could be consecutively improved; iii) it's easy to implement and low time cost. Experiments demonstrate that compared with two other approaches: SCAMP (a pure random neighbor selection method) and Narada (a QoS aware method), the throughput and average packet delay of the layered streaming on top of the overlay constructed by OCals can be remarkably improved. Besides, the time spent on joining and recovery is very short.