Verifying Robust Unlearning: Probing Residual Knowledge in Unlearned Models

Machine Unlearning (MUL) is crucial for privacy protection and content regulation, yet recent studies reveal that traces of forgotten information persist in unlearned models, enabling adversaries to resurface removed knowledge. Existing verification methods only confirm whether unlearning was executed, failing to detect such residual information leaks. To address this, we introduce the concept of Robust Unlearning, ensuring models are indistinguishable from retraining and resistant to adversarial recovery. To empirically evaluate whether unlearning techniques meet this security standard, we propose the Unlearning Mapping Attack (UMA), a post-unlearning verification framework that actively probes models for forgotten traces using adversarial queries. Extensive experiments on discriminative and generative tasks show that existing unlearning techniques remain vulnerable, even when passing existing verification metrics. By establishing UMA as a practical verification tool, this study sets a new standard for assessing and enhancing machine unlearning security.

Multimodal Coreference Resolution for Chinese Social Media Dialogues: Dataset and Benchmark Approach

Multimodal coreference resolution (MCR) aims to identify mentions referring to the same entity across different modalities, such as text and visuals, and is essential for understanding multimodal content. In the era of rapidly growing mutimodal content and social media, MCR is particularly crucial for interpreting user interactions and bridging text-visual references to improve communication and personalization. However, MCR research for real-world dialogues remains unexplored due to the lack of sufficient data resources.To address this gap, we introduce TikTalkCoref, the first Chinese multimodal coreference dataset for social media in real-world scenarios, derived from the popular Douyin short-video platform. This dataset pairs short videos with corresponding textual dialogues from user comments and includes manually annotated coreference clusters for both person mentions in the text and the coreferential person head regions in the corresponding video frames. We also present an effective benchmark approach for MCR, focusing on the celebrity domain, and conduct extensive experiments on our dataset, providing reliable benchmark results for this newly constructed dataset. We will release the TikTalkCoref dataset to facilitate future research on MCR for real-world social media dialogues.

From Dataset to Real-world: General 3D Object Detection via Generalized Cross-domain Few-shot Learning

LiDAR-based 3D object detection datasets have been pivotal for autonomous driving, yet they cover a limited range of objects, restricting the model's generalization across diverse deployment environments. To address this, we introduce the first generalized cross-domain few-shot (GCFS) task in 3D object detection, which focuses on adapting a source-pretrained model for high performance on both common and novel classes in a target domain with few-shot samples. Our solution integrates multi-modal fusion and contrastive-enhanced prototype learning within one framework, holistically overcoming challenges related to data scarcity and domain adaptation in the GCFS setting. The multi-modal fusion module utilizes 2D vision-language models to extract rich, open-set semantic knowledge. To address biases in point distributions across varying structural complexities, we particularly introduce a physically-aware box searching strategy that leverages laser imaging principles to generate high-quality 3D box proposals from 2D insights, enhancing object recall. To effectively capture domain-specific representations for each class from limited target data, we further propose a contrastive-enhanced prototype learning, which strengthens the model's adaptability. We evaluate our approach with three GCFS benchmark settings, and extensive experiments demonstrate the effectiveness of our solution for GCFS tasks. The code will be publicly available.

Exploring the Impact of Temperature Scaling in Softmax for Classification and Adversarial Robustness

The softmax function is a fundamental component in deep learning. This study delves into the often-overlooked parameter within the softmax function, known as "temperature," providing novel insights into the practical and theoretical aspects of temperature scaling for image classification. Our empirical studies, adopting convolutional neural networks and transformers on multiple benchmark datasets, reveal that moderate temperatures generally introduce better overall performance. Through extensive experiments and rigorous theoretical analysis, we explore the role of temperature scaling in model training and unveil that temperature not only influences learning step size but also shapes the model's optimization direction. Moreover, for the first time, we discover a surprising benefit of elevated temperatures: enhanced model robustness against common corruption, natural perturbation, and non-targeted adversarial attacks like Projected Gradient Descent. We extend our discoveries to adversarial training, demonstrating that, compared to the standard softmax function with the default temperature value, higher temperatures have the potential to enhance adversarial training. The insights of this work open new avenues for improving model performance and security in deep learning applications.

MDHP-Net: Detecting Injection Attacks on In-vehicle Network using Multi-Dimensional Hawkes Process and Temporal Model

The integration of intelligent and connected technologies in modern vehicles, while offering enhanced functionalities through Electronic Control Unit and interfaces like OBD-II and telematics, also exposes the vehicle's in-vehicle network (IVN) to potential cyberattacks. In this paper, we consider a specific type of cyberattack known as the injection attack. As demonstrated by empirical data from real-world cybersecurity adversarial competitions(available at https://mimic2024.xctf.org.cn/race/qwmimic2024 ), these injection attacks have excitation effect over time, gradually manipulating network traffic and disrupting the vehicle's normal functioning, ultimately compromising both its stability and safety. To profile the abnormal behavior of attackers, we propose a novel injection attack detector to extract long-term features of attack behavior. Specifically, we first provide a theoretical analysis of modeling the time-excitation effects of the attack using Multi-Dimensional Hawkes Process (MDHP). A gradient descent solver specifically tailored for MDHP, MDHP-GDS, is developed to accurately estimate optimal MDHP parameters. We then propose an injection attack detector, MDHP-Net, which integrates optimal MDHP parameters with MDHP-LSTM blocks to enhance temporal feature extraction. By introducing MDHP parameters, MDHP-Net captures complex temporal features that standard Long Short-Term Memory (LSTM) cannot, enriching temporal dependencies within our customized structure. Extensive evaluations demonstrate the effectiveness of our proposed detection approach.

ATLAS: Adapter-Based Multi-Modal Continual Learning with a Two-Stage Learning Strategy

While vision-and-language models significantly advance in many fields, the challenge of continual learning is unsolved. Parameter-efficient modules like adapters and prompts present a promising way to alleviate catastrophic forgetting. However, existing works usually learn individual adapters for each task, which may result in redundant knowledge among adapters. Moreover, they continue to use the original pre-trained model to initialize the downstream model, leading to negligible changes in the model's generalization compared to the original model. In addition, there is still a lack of research investigating the consequences of integrating a multi-modal model into the updating procedure for both uni-modal and multi-modal tasks and the subsequent impacts it has on downstream tasks. In this paper, we propose an adapter-based two-stage learning paradigm, a multi-modal continual learning scheme that consists of experience-based learning and novel knowledge expansion, which helps the model fully use experience knowledge and compensate for novel knowledge. Extensive experiments demonstrate that our method is proficient for continual learning. It expands the distribution of representation upstream while also minimizing the negative impact of forgetting previous tasks. Additionally, it enhances the generalization capability for downstream tasks. Furthermore, we incorporate both multi-modal and uni-modal tasks into upstream continual learning. We observe that learning from upstream tasks can help with downstream tasks. Our code will be available at: https://github.com/lihong2303/ATLAS.

InterMask: 3D Human Interaction Generation via Collaborative Masked Modelling

Generating realistic 3D human-human interactions from textual descriptions remains a challenging task. Existing approaches, typically based on diffusion models, often generate unnatural and unrealistic results. In this work, we introduce InterMask, a novel framework for generating human interactions using collaborative masked modeling in discrete space. InterMask first employs a VQ-VAE to transform each motion sequence into a 2D discrete motion token map. Unlike traditional 1D VQ token maps, it better preserves fine-grained spatio-temporal details and promotes spatial awareness within each token. Building on this representation, InterMask utilizes a generative masked modeling framework to collaboratively model the tokens of two interacting individuals. This is achieved by employing a transformer architecture specifically designed to capture complex spatio-temporal interdependencies. During training, it randomly masks the motion tokens of both individuals and learns to predict them. In inference, starting from fully masked sequences, it progressively fills in the tokens for both individuals. With its enhanced motion representation, dedicated architecture, and effective learning strategy, InterMask achieves state-of-the-art results, producing high-fidelity and diverse human interactions. It outperforms previous methods, achieving an FID of $5.154$ (vs $5.535$ for in2IN) on the InterHuman dataset and $0.399$ (vs $5.207$ for InterGen) on the InterX dataset. Additionally, InterMask seamlessly supports reaction generation without the need for model redesign or fine-tuning.

Evaluating Gait Symmetry with a Smart Robotic Walker: A Novel Approach to Mobility Assessment

Gait asymmetry, a consequence of various neurological or physical conditions such as aging and stroke, detrimentally impacts bipedal locomotion, causing biomechanical alterations, increasing the risk of falls and reducing quality of life. Addressing this critical issue, this paper introduces a novel diagnostic method for gait symmetry analysis through the use of an assistive robotic Smart Walker equipped with an innovative asymmetry detection scheme. This method analyzes sensor measurements capturing the interaction torque between user and walker. By applying a seasonal-trend decomposition tool, we isolate gait-specific patterns within these data, allowing for the estimation of stride durations and calculation of a symmetry index. Through experiments involving 5 experimenters, we demonstrate the Smart Walker's capability in detecting and quantifying gait asymmetry by achieving an accuracy of 84.9% in identifying asymmetric cases in a controlled testing environment. Further analysis explores the classification of these asymmetries based on their underlying causes, providing valuable insights for gait assessment. The results underscore the potential of the device as a precise, ready-to-use monitoring tool for personalized rehabilitation, facilitating targeted interventions for enhanced patient outcomes.

Distilling High Diagnostic Value Patches for Whole Slide Image Classification Using Attention Mechanism

Multiple Instance Learning (MIL) has garnered widespread attention in the field of Whole Slide Image (WSI) classification as it replaces pixel-level manual annotation with diagnostic reports as labels, significantly reducing labor costs. Recent research has shown that bag-level MIL methods often yield better results because they can consider all patches of the WSI as a whole. However, a drawback of such methods is the incorporation of more redundant patches, leading to interference. To extract patches with high diagnostic value while excluding interfering patches to address this issue, we developed an attention-based feature distillation multi-instance learning (AFD-MIL) approach. This approach proposed the exclusion of redundant patches as a preprocessing operation in weakly supervised learning, directly mitigating interference from extensive noise. It also pioneers the use of attention mechanisms to distill features with high diagnostic value, as opposed to the traditional practice of indiscriminately and forcibly integrating all patches. Additionally, we introduced global loss optimization to finely control the feature distillation module. AFD-MIL is orthogonal to many existing MIL methods, leading to consistent performance improvements. This approach has surpassed the current state-of-the-art method, achieving 91.47% ACC (accuracy) and 94.29% AUC (area under the curve) on the Camelyon16 (Camelyon Challenge 2016, breast cancer), while 93.33% ACC and 98.17% AUC on the TCGA-NSCLC (The Cancer Genome Atlas Program: non-small cell lung cancer). Different feature distillation methods were used for the two datasets, tailored to the specific diseases, thereby improving performance and interpretability.

Forecasting Automotive Supply Chain Shortfalls with Heterogeneous Time Series

Operational disruptions can significantly impact companies performance. Ford, with its 37 plants globally, uses 17 billion parts annually to manufacture six million cars and trucks. With up to ten tiers of suppliers between the company and raw materials, any extended disruption in this supply chain can cause substantial financial losses. Therefore, the ability to forecast and identify such disruptions early is crucial for maintaining seamless operations. In this study, we demonstrate how we construct a dataset consisting of many multivariate time series to forecast first-tier supply chain disruptions, utilizing features related to capacity, inventory, utilization, and processing, as outlined in the classical Factory Physics framework. This dataset is technically challenging due to its vast scale of over five hundred thousand time series. Furthermore, these time series, while exhibiting certain similarities, also display heterogeneity within specific subgroups. To address these challenges, we propose a novel methodology that integrates an enhanced Attention Sequence to Sequence Deep Learning architecture, using Neural Network Embeddings to model group effects, with a Survival Analysis model. This model is designed to learn intricate heterogeneous data patterns related to operational disruptions. Our model has demonstrated a strong performance, achieving 0.85 precision and 0.8 recall during the Quality Assurance (QA) phase across Ford's five North American plants. Additionally, to address the common criticism of Machine Learning models as black boxes, we show how the SHAP framework can be used to generate feature importance from the model predictions. It offers valuable insights that can lead to actionable strategies and highlights the potential of advanced machine learning for managing and mitigating supply chain risks in the automotive industry.