Fishers Harvest Parallel Unlearning in Inherited Model Networks

Unlearning in various learning frameworks remains challenging, with the continuous growth and updates of models exhibiting complex inheritance relationships. This paper presents a novel unlearning framework, which enables fully parallel unlearning among models exhibiting inheritance. A key enabler is the new Unified Model Inheritance Graph (UMIG), which captures the inheritance using a Directed Acyclic Graph (DAG).Central to our framework is the new Fisher Inheritance Unlearning (FIUn) algorithm, which utilizes the Fisher Information Matrix (FIM) from initial unlearning models to pinpoint impacted parameters in inherited models. By employing FIM, the FIUn method breaks the sequential dependencies among the models, facilitating simultaneous unlearning and reducing computational overhead. We further design to merge disparate FIMs into a single matrix, synchronizing updates across inherited models. Experiments confirm the effectiveness of our unlearning framework. For single-class tasks, it achieves complete unlearning with 0\% accuracy for unlearned labels while maintaining 94.53\% accuracy for retained labels on average. For multi-class tasks, the accuracy is 1.07\% for unlearned labels and 84.77\% for retained labels on average. Our framework accelerates unlearning by 99\% compared to alternative methods.

Open-Vocabulary X-ray Prohibited Item Detection via Fine-tuning CLIP

X-ray prohibited item detection is an essential component of security check and categories of prohibited item are continuously increasing in accordance with the latest laws. Previous works all focus on close-set scenarios, which can only recognize known categories used for training and often require time-consuming as well as labor-intensive annotations when learning novel categories, resulting in limited real-world applications. Although the success of vision-language models (e.g. CLIP) provides a new perspectives for open-set X-ray prohibited item detection, directly applying CLIP to X-ray domain leads to a sharp performance drop due to domain shift between X-ray data and general data used for pre-training CLIP. To address aforementioned challenges, in this paper, we introduce distillation-based open-vocabulary object detection (OVOD) task into X-ray security inspection domain by extending CLIP to learn visual representations in our specific X-ray domain, aiming to detect novel prohibited item categories beyond base categories on which the detector is trained. Specifically, we propose X-ray feature adapter and apply it to CLIP within OVOD framework to develop OVXD model. X-ray feature adapter containing three adapter submodules of bottleneck architecture, which is simple but can efficiently integrate new knowledge of X-ray domain with original knowledge, further bridge domain gap and promote alignment between X-ray images and textual concepts. Extensive experiments conducted on PIXray and PIDray datasets demonstrate that proposed method performs favorably against other baseline OVOD methods in detecting novel categories in X-ray scenario. It outperforms previous best result by 15.2 AP50 and 1.5 AP50 on PIXray and PIDray with achieving 21.0 AP50 and 27.8 AP50 respectively.

MMCL: Boosting Deformable DETR-Based Detectors with Multi-Class Min-Margin Contrastive Learning for Superior Prohibited Item Detection

Prohibited Item detection in X-ray images is one of the most effective security inspection methods.However, differing from natural light images, the unique overlapping phenomena in X-ray images lead to the coupling of foreground and background features, thereby lowering the accuracy of general object detectors.Therefore, we propose a Multi-Class Min-Margin Contrastive Learning (MMCL) method that, by clarifying the category semantic information of content queries under the deformable DETR architecture, aids the model in extracting specific category foreground information from coupled features.Specifically, after grouping content queries by the number of categories, we employ the Multi-Class Inter-Class Exclusion (MIE) loss to push apart content queries from different groups. Concurrently, the Intra-Class Min-Margin Clustering (IMC) loss is utilized to attract content queries within the same group, while ensuring the preservation of necessary disparity. As training, the inherent Hungarian matching of the model progressively strengthens the alignment between each group of queries and the semantic features of their corresponding category of objects. This evolving coherence ensures a deep-seated grasp of category characteristics, consequently bolstering the anti-overlapping detection capabilities of models.MMCL is versatile and can be easily plugged into any deformable DETR-based model with dozens of lines of code. Extensive experiments on the PIXray and OPIXray datasets demonstrate that MMCL significantly enhances the performance of various state-of-the-art models without increasing complexity. The code has been released at https://github.com/anonymity0403/MMCL.

LIKO: LiDAR, Inertial, and Kinematic Odometry for Bipedal Robots

High-frequency and accurate state estimation is crucial for biped robots. This paper presents a tightly-coupled LiDAR-Inertial-Kinematic Odometry (LIKO) for biped robot state estimation based on an iterated extended Kalman filter. Beyond state estimation, the foot contact position is also modeled and estimated. This allows for both position and velocity updates from kinematic measurement. Additionally, the use of kinematic measurement results in an increased output state frequency of about 1kHz. This ensures temporal continuity of the estimated state and makes it practical for control purposes of biped robots. We also announce a biped robot dataset consisting of LiDAR, inertial measurement unit (IMU), joint encoders, force/torque (F/T) sensors, and motion capture ground truth to evaluate the proposed method. The dataset is collected during robot locomotion, and our approach reached the best quantitative result among other LIO-based methods and biped robot state estimation algorithms. The dataset and source code will be available at https://github.com/Mr-Zqr/LIKO.

AO-DETR: Anti-Overlapping DETR for X-Ray Prohibited Items Detection

Prohibited item detection in X-ray images is one of the most essential and highly effective methods widely employed in various security inspection scenarios. Considering the significant overlapping phenomenon in X-ray prohibited item images, we propose an Anti-Overlapping DETR (AO-DETR) based on one of the state-of-the-art general object detectors, DINO. Specifically, to address the feature coupling issue caused by overlapping phenomena, we introduce the Category-Specific One-to-One Assignment (CSA) strategy to constrain category-specific object queries in predicting prohibited items of fixed categories, which can enhance their ability to extract features specific to prohibited items of a particular category from the overlapping foreground-background features. To address the edge blurring problem caused by overlapping phenomena, we propose the Look Forward Densely (LFD) scheme, which improves the localization accuracy of reference boxes in mid-to-high-level decoder layers and enhances the ability to locate blurry edges of the final layer. Similar to DINO, our AO-DETR provides two different versions with distinct backbones, tailored to meet diverse application requirements. Extensive experiments on the PIXray and OPIXray datasets demonstrate that the proposed method surpasses the state-of-the-art object detectors, indicating its potential applications in the field of prohibited item detection. The source code will be released at https://github.com/Limingyuan001/AO-DETR-test.

Decentralized Federated Unlearning on Blockchain

Blockchained Federated Learning (FL) has been gaining traction for ensuring the integrity and traceability of FL processes. Blockchained FL involves participants training models locally with their data and subsequently publishing the models on the blockchain, forming a Directed Acyclic Graph (DAG)-like inheritance structure that represents the model relationship. However, this particular DAG-based structure presents challenges in updating models with sensitive data, due to the complexity and overhead involved. To address this, we propose Blockchained Federated Unlearning (BlockFUL), a generic framework that redesigns the blockchain structure using Chameleon Hash (CH) technology to mitigate the complexity of model updating, thereby reducing the computational and consensus costs of unlearning tasks.Furthermore, BlockFUL supports various federated unlearning methods, ensuring the integrity and traceability of model updates, whether conducted in parallel or serial. We conduct a comprehensive study of two typical unlearning methods, gradient ascent and re-training, demonstrating the efficient unlearning workflow in these two categories with minimal CH and block update operations. Additionally, we compare the computation and communication costs of these methods.