Federated learning (FL) systems enable collaborative model training among industrial Internet of Things (IIoT) devices but face significant security challenges, particularly in backdoor attacks, due to the nonindependent and identically distributed (non-IID) nature of data. To address this challenge, we propose a stability-enhanced dynamic backdoor defense approach in FL for IIoT, which maintains primary task accuracy while strengthening defenses in non-IID environments. Leveraging the similarity between data distribution and model updates, we segment non-IID scenarios into multiple quasi-IID environments. Our approach includes a dynamic client matching module, a malicious filtering module, and robust personalized aggregation to reduce the success rate of backdoor attacks while augmenting the resilience and precision of the aggregated model. The effectiveness of our strategy has been validated through analyses on the Modified National Institute of Standards and Technology database (MNIST), Canadian Institute for Advanced Research, 10 classes (CIFAR-10), Internet of Things (IoT)-23, and Washington University in St. Louis (WUSTL)-IIOT datasets in both IID and non-IID scenarios. Notably, on the IoT-23 and WUSTL-IIOT, the success rate of backdoor attacks was significantly reduced to 3.46%.

The abundance of instructional videos and their narrations over the Internet offers an exciting avenue for understanding procedural activities. In this work, we propose to learn video representation that encodes both action steps and their temporal ordering, based on a large-scale dataset of web instructional videos and their narrations, without using human annotations. Our method jointly learns a video representation to encode individual step concepts, and a deep probabilistic model to capture both temporal dependencies and immense individual variations in the step ordering. We empirically demonstrate that learning temporal ordering not only enables new capabilities for procedure reasoning, but also reinforces the recognition of individual steps. Our model significantly advances the state-of-the-art results on step classification (+2.8%/+3.3% on COIN / EPIC-Kitchens) and step forecasting (+7.4% on COIN). Moreover, our model attains promising results in zero-shot inference for step classification and fore-casting, as well as in predicting diverse and plausible steps for incomplete procedures. Our code is available at https://github.com/facebookresearch/ProcedureVRL.

As the number of object categories continues to increase in object classification, it is inevitable to have certain categories that are more confusing than others due to the proximity of their samples in the feature space. In this work, we conduct a detail analysis on confusing categories and propose a confusing categories identification and resolution (CCIR) scheme, which can be applied to any CNNbased object classification baseline method to further improve its performance. In the CCIR scheme, we first present a procedure to cluster confusing object categories together to form a confusion set automatically. Then, a binary-treestructured (BTS) clustering method is adopted to split a confusion set into multiple subsets. A classifier is subsequently learned within each subset to enhance its performance. Experimental results on the ImageNet ILSVRC2012 dataset show that the proposed CCIR scheme can offer a significant performance gain over the AlexNet and the VGG16.

Textual data such as tags, sentence descriptions are combined with visual cues to reduce the semantic gap for image retrieval applications in today's Multimodal Image Retrieval (MIR) systems. However, all tags are treated as equally important in these systems, which may result in misalignment between visual and textual modalities during MIR training. This will further lead to degenerated retrieval performance at query time. To address this issue, we investigate the problem of tag importance prediction, where the goal is to automatically predict the tag importance and use it in image retrieval. To achieve this, we first propose a method to measure the relative importance of object and scene tags from image sentence descriptions. Using this as the ground truth, we present a tag importance prediction model to jointly exploit visual, semantic and context cues. The Structural Support Vector Machine (SSVM) formulation is adopted to ensure efficient training of the prediction model. Then, the Canonical Correlation Analysis (CCA) is employed to learn the relation between the image visual feature and tag importance to obtain robust retrieval performance. Experimental results on three real-world datasets show a significant performance improvement of the proposed MIR with Tag Importance Prediction (MIR/TIP) system over other MIR systems.

In this paper, we present a compressed-domain video retargeting solution that operates without compromising the resizing quality. Existing video retargeting methods operate in the spatial (or pixel) domain. Such a solution is not practical if it is implemented in mobile devices due to its large memory requirement. In the proposed solution, each component of the retargeting system is designed to exploit the low-level compressed domain features extracted from the coded bit stream. For example, motion information is obtained directly from motion vectors. An efficient column shape mesh deformation is employed to solve the difficulty of sophisticated quad-shape mesh deformation in the compressed domain. The proposed solution achieves comparable (or slightly better) visual quality performance as compared with several state-of-the-art pixel-domain retargeting methods at lower computational and memory costs, making content-aware video resizing both scalable and practical in real-world applications.

To exploit both spatial and temporal correlation of video signal, a Second Order Prediction (SOP) scheme has been presented to eliminate the spatial correlation existing in motion compensated residue. Although SOP achieves better RD performance by adopting residue prediction in Mode Decision (MD) stage, it can be further improved by combining residue prediction into Motion Estimation (ME). Our analysis demonstrates that a more accurate motion vector could be obtained if both ME and MD take residue prediction into account. Implementation of the proposed algorithm into the H.264/AVC reference software demonstrates that the enhanced SOP can achieve up to 18.46% of bit-rate saving at equivalent objective quality.

AVS-Part 2 is a mainstream video coding standard with high compression efficiency similar to H.264/AVC. A technique named Second Order Prediction (SOP) has been presented based on H.264/AVC to decrease the signal correlation after motion-compensated prediction. To achieve better coding performance, this paper presents a method named Block-based Second Order Prediction (BSOP) to ameliorate SOP to adapt to the features of the motion-compensation in AVS-P2 with analysis and demonstration in detail. Experimental results show that the proposed BSOP can outperform AVS-P2 P-picture coding by 3.99% bit-rate saving (0.126dB BD-PSNR gain) on average, and performs better than SOP implemented on AVS by 1.81% bit-rate saving.

Based on a relatively simple motion assumption, the technique of motion-compensated prediction will result in large residue when dealing with video contents containing complex motions. To achieve higher coding efficiency, this paper presents a method named second order prediction (SOP) to exploit remaining signal correlation after motion-compensated prediction. Demonstration and analysis on the SOP concept are provided along with its technical details. Experimental results show that the proposed SOP can outperform H.264/AVC P-picture coding 10.28% bit-rate saving under the same PSNR.