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Jimin Park, AHyun Ji, Minji Park, Mohammad Saidur Rahman, Se Eun Oh

Continual Learning (CL) for malware classification tackles the rapidly evolving nature of malware threats and the frequent emergence of new types. Generative Replay (GR)-based CL systems utilize a generative model to produce synthetic versions of past data, which are then combined with new data to retrain the primary model. Traditional machine learning techniques in this domain often struggle with catastrophic forgetting, where a model's performance on old data degrades over time. In this paper, we introduce a GR-based CL system that employs Generative Adversarial Networks (GANs) with feature matching loss to generate high-quality malware samples. Additionally, we implement innovative selection schemes for replay samples based on the model’s hidden representations. Our comprehensive evaluation across Windows and Android malware datasets in a class-incremental learning scenario -- where new classes are introduced continuously over multiple tasks -- demonstrates substantial performance improvements over previous methods. For example, our system achieves an average accuracy of 55% on Windows malware samples, significantly outperforming other GR-based models by 28%. This study provides practical insights for advancing GR-based malware classification systems.

Zhibin Ni, Chang Liu, Hai Wan, Xibin Zhao

Heterogeneous Graph Neural Networks (HGNNs) have achieved state-of-the-art performance in classifying molecular graphs, capitalizing on their ability to capture rich semantics. However, HGNNs for molecule property prediction exhibit significant susceptibility to adversarial attacks—a challenge that prior research has entirely overlooked. To fill this gap, this paper introduces the first study focused on robust graph-level representation learning tailored for heterogeneous molecular graphs. To achieve this goal, we propose a comprehensive Robust Heterogeneous Graph Classification (RHGC) framework grounded in the Information Bottleneck principle, which aims to identify the most informative and least noisy heterogeneous subgraphs to derive robust, holistic representations. This is specifically accomplished through a dedicated Node Semantic Purifier, which enhances node-level and semantic-level robustness by eliminating label-irrelevant interference using graph stochastic attention and the Hilbert-Schmidt Independence Criterion, along with a Global Graph Disentanglement method, which improves graph-level robustness by addressing information leak. Experiments on three molecular benchmarks demonstrate that RHGC enhances accuracy by an average of 5.06% under all three attack settings and meanwhile by 4.33% on clean data.

Yuwei Miao, Yuzhi Guo, Hehuan Ma, Jingquan Yan, Feng Jiang, Rui Liao, Junzhou Huang

Exploring the functions of genes and gene products is crucial to a wide range of fields, including medical research, evolutionary biology, and environmental science. However, discovering new functions largely relies on expensive and exhaustive wet lab experiments. Existing methods of automatic function annotation or prediction mainly focus on protein function prediction with sequence, 3D-structures or protein family information. In this study, we propose to tackle the gene function prediction problem by exploring Gene Ontology graph and annotation with BERT (GoBERT) to decipher the underlying relationships among gene functions. Our proposed novel function prediction task utilizes existing functions as inputs and generalizes the function prediction to gene and gene products. Specifically, two pre-train tasks are designed to jointly train GoBERT to capture both explicit and implicit relations of functions. Neighborhood prediction is a self-supervised multi-label classification task that captures the explicit function relations. Specified masking and recovering task helps GoBERT in finding implicit patterns among functions. The pre-trained GoBERT possess the ability to predict novel functions for various gene and gene products based on known functional annotations. Extensive experiments, biological case studies, and ablation studies are conducted to demonstrate the superiority of our proposed GoBERT.

Jian-Ping Mei, Weibin Zhang, Jie Chen, Xuyun Zhang, Tiantian Zhu

Malicious users attempt to replicate commercial models functionally at low cost by training a clone model with query responses. It is challenging to timely prevent such model-stealing attacks to achieve strong protection and maintain utility. In this paper, we propose a novel non-parametric detector called Account-aware Distribution Discrepancy (ADD) to recognize queries from malicious users by leveraging account-wise local dependency. We formulate each class as a Multivariate Normal distribution (MVN) in the feature space and measure the malicious score as the sum of weighted class-wise distribution discrepancy. The ADD detector is combined with random-based prediction poisoning to yield a plug-and-play defense module named D-ADD for image classification models. Results of extensive experimental studies show that D-ADD achieves strong defense against different types of attacks with little interference in serving benign users for both soft and hard-label settings.

Runxin Liu, Tian Xie, Jiaming Li, Lingyun Yu, Hongtao Xie

Detecting and grounding multi-modal media manipulation aims to categorize the type and localize the region of manipulation for image-text pairs in both two modalities. Existing methods have not sufficiently explored the intrinsic properties of the manipulated images, which contain both forgery and content features, leading to inefficient utilization. To address this problem, we propose an Image-Driven Decoupled Sequential Framework (IDseq), designed to decouple image features and rationally integrate them to accomplish different sub-tasks effectively. Specifically, IDseq employs two specially designed disentangled losses to guide the disentangled learning of forgery and content features. To efficiently leverage these features, we propose a Decoupled Image Manipulation Decoder (DIMD) that processes image tasks within a decoupled schema. We mitigate their exclusive competition by separating the image tasks into forgery-relevant and content-relevant components and training them without gradient interaction. Additionally, we utilize content features enhanced by the proposed Manipulation Indicator Generator (MIG) for the text tasks, which provide the maximal visual information as a reference while eliminating interference from unverified image data. Extensive experiments show the superiority of our IDseq, where it notably outperforms SOTA methods on the fine-grained classification by 3.8% in mAP and the forgery face grounding by 8.7% in IoUmean, even 1.3% in F1 on the most challenging manipulated text grounding.

Pengcheng Jiang, Cao Xiao, Tianfan Fu, Parminder Bhatia, Taha Kass-Hout, Jimeng Sun, Jiawei Han

Molecular representation learning is vital for various downstream applications, including the analysis and prediction of molecular properties and side effects. While Graph Neural Networks (GNNs) have been a popular framework for modeling molecular data, they often struggle to capture the full complexity of molecular representations. In this paper, we introduce a novel method called Gode, which accounts for the dual-level structure inherent in molecules. Molecules possess an intrinsic graph structure and simultaneously function as nodes within a broader molecular knowledge graph. Gode integrates individual molecular graph representations with multi-domain biochemical data from knowledge graphs. By pre-training two GNNs on different graph structures and employing contrastive learning, Gode effectively fuses molecular structures with their corresponding knowledge graph substructures. This fusion yields a more robust and informative representation, enhancing molecular property predictions by leveraging both chemical and biological information. When fine-tuned across 11 chemical property tasks, our model significantly outperforms existing benchmarks, achieving an average ROC-AUC improvement of 12.7% for classification tasks and an average RMSE/MAE improvement of 34.4% for regression tasks. Notably, Gode surpasses the current leading model in property prediction, with advancements of 2.2% in classification and 7.2% in regression tasks.

Zehua Han, Jing Xiao, Qirui Zhao, Zhexuan Cui, Yufeng Wang, Duona Zhang, Wenrui Ding

In complex electromagnetic environments, the identification and differentiation of diverse radio frequency (RF) emitters become particularly crucial. Existing RF fingerprinting methods demonstrate limitations when dealing with numerous unknown emitters, making it challenging for accurate classification and recognition. These limitations hinder the effective handling of specific unknown emitters.To address this issue, we introduce a novel RF fingerprinting method suitable for open-world conditions for the first time. We develop a novel RF fingerprinting model, Roinformer, to extract signal features with positional attention. We then leverage data augmentation strategies such as noise jitter and signal frame rearrangement to construct an effective pre-training model. Moreover, by incorporating instance-level similarity loss and a novel local entropy regularization approach, we significantly enhance the accuracy of known class identification and mitigate the catastrophic forgetting of known signal samples. Experimental results on three temporal signal datasets demonstrate that our method effectively recognizes both the known and unknown classes, outperforming several state-of-the-art methods by a large margin.

Biwei Cao, Qihang Wu, Jiuxin Cao, Bo Liu, Jie Gui

With the rapid development of the Internet, the information dissemination paradigm has changed and the efficiency has been improved greatly. While this also brings the quick spread of fake news and leads to negative impacts on cyberspace. Currently, the information presentation formats have evolved gradually, with the news formats shifting from texts to multimodal contents. As a result, detecting multimodal fake news has become one of the research hotspots. However, multimodal fake news detection research field still faces two main challenges: the inability to fully and effectively utilize multimodal information for detection, and the low credibility or static nature of the introduced external information, which limits dynamic updates. To bridge the gaps, we propose ERIC-FND, an external reliable information-enhanced multimodal contrastive learning framework for fake news detection. ERIC-FND strengthens the representation of news contents by entity-enriched external information enhancement method. It also enriches the multimodal news information via multimodal semantic interaction method where the multimodal constrative learning is employed to make different modality representations learn from each other. Moreover, an adaptive fusion method is taken to integrate the news representations from different dimensions for the eventual classification. Experiments are done on two commonly used datasets in different languages, X (Twitter) and Weibo. Experiment results demonstrate that our proposed model ERIC-FND outperforms existing state-of-the-art fake news detection methods under the same settings.

Pei Geng, Jian Yang, Shanshan Zhang

Human-Object Interaction (HOI) detection aims to predict the <Human, Interaction, Object> triplets, where the core challenge lies in recognizing the interaction of each human-object pair. Despite recent progress thanks to more advanced model architectures, HOI performance remains unsatisfactory. In this work, we first perform some failure analysis and find that the accuracy of the no-interaction category is extremely low, largely hindering the improvement of overall performance. We further look into the error types and find the mis-classification between no-interaction and with-interaction ones can be handled by human-object relation priors. Specifically, to better distinguish no-interaction from direct interactions, we propose 3D location prior, which indicates the distance between human and object; as of no-interaction vs. indirect interactions, we propose gaze area prior, which denotes whether human can see the object or not. The above two types of human-object relation priors are represented by text and are combined with the original visual features, generating multi-modal cues for interaction recognition.Experimental results on the HICO-DET and V-COCO datasets demonstrate that our proposed human-object relation priors are effective and our method HORP surpasses previous methods under various settings and scenarios. In particular, the usage of our priors significantly enhances the model's recognition ability for the no-interaction category.

Shunxin Wang, Raymond Veldhuis, Nicola Strisciuglio

Frequency shortcuts refer to specific frequency patterns that models heavily rely on for correct classification. Previous studies have shown that models trained on small image datasets often exploit such shortcuts, potentially impairing their generalization performance. However, existing methods for identifying frequency shortcuts require expensive computations and become impractical for analyzing models trained on large datasets. In this work, we propose the first approach to more efficiently analyze frequency shortcuts at a large scale. We show that both CNN and transformer models learn frequency shortcuts on ImageNet. We also expose that frequency shortcut solutions can yield good performance on out-of-distribution (OOD) test sets which largely retain texture information. However, these shortcuts, mostly aligned with texture patterns, hinder model generalization on rendition-based OOD test sets. These observations suggest that current OOD evaluations often overlook the impact of frequency shortcuts on model generalization. Future benchmarks could thus benefit from explicitly assessing and accounting for these shortcuts to build models that generalize across a broader range of OOD scenarios.

Sagar Soni, Akshay Dudhane, Hiyam Debary, Mustansar Fiaz, Muhammad Akhtar Munir, Muhammad Sohail Danish, Paolo Fraccaro, Campbell D Watson, Levente J Klein, Fahad Shahbaz Khan 等

Automated analysis of vast Earth observation data via interactive Vision-Language Models (VLMs) can unlock new opportunities for environmental monitoring, disaster response, and resource management. Existing generic VLMs do not perform well on Remote Sensing data, while the recent Geo-spatial VLMs remain restricted to a fixed resolution and few sensor modalities. In this paper, we introduce EarthDial, a conversational assistant specifically designed for Earth Observation (EO) data, transforming complex, multi-sensory Earth observations into interactive, natural language dialogues. EarthDial supports multispectral, multi-temporal, and multi-resolution imagery, enabling a wide range of remote sensing tasks, including classification, detection, captioning, question answering, visual reasoning, and visual grounding. To achieve this, we introduce an extensive instruction tuning dataset comprising over 11.11M instruction pairs covering RGB, Synthetic Aperture Radar (SAR), and multispectral modalities such as Near-Infrared (NIR) and infrared. Furthermore, EarthDial handles bi-temporal and multi-temporal sequence analysis for applications like change detection. Our extensive experimental results on 44 downstream datasets demonstrate that EarthDial outperforms existing generic and domain specific models, achieving better generalization across various EO tasks. Our source codes and pre-trained models are at https://github.com/hiyamdebary/EarthDial.

Senyu Hou, Gaoxia Jiang, Jia Zhang, Shangrong Yang, Husheng Guo, Yaqing Guo, Wenjian Wang

In image classification, the label quality of training data critically influences model generalization, especially for deep neural networks (DNNs). Traditionally, learning from noisy labels (LNL) can improve the generalization of DNNs through complex architectures or a series of robust techniques, but its performance improvement is limited by the discriminative paradigm. Unlike traditional ways, we resolve the LNL problems from the perspective of robust label generation, based on diffusion models within the generative paradigm. To expand the diffusion model into a robust classifier that explicitly accommodates more noise knowledge, we propose a Directional Label Diffusion (DLD) model. It disentangles the diffusion process into two paths, i.e., directional diffusion and random diffusion. Specifically, directional diffusion simulates the corruption of true labels into a directed noise distribution, prioritizing the removal of likely noise, whereas random diffusion introduces inherent randomness to support label recovery. This architecture enable DLD to gradually infer labels from an initial random state, interpretably diverging from the specified noise distribution. To adapt the model to diverse noisy environments, we design a low-cost label pre-correction method that automatically supplies more accurate label information to the diffusion model, without requiring manual intervention or additional iterations. Our approach outperforms state-of-the-art methods on both simulated and real-world noisy datasets. Code is available at https://github.com/SenyuHou/DLD.

Xu Han, Yuan Tang, Jinfeng Xu, Xianzhi Li

We introduce Monarch Sparse Tuning (MoST), the first reparameterization-based parameter-efficient fine-tuning (PEFT) method tailored for 3D representation learning. Unlike existing adapter-based and prompt-tuning 3D PEFT methods, MoST introduces no additional inference overhead and is compatible with many 3D representation learning backbones. At its core, we present a new family of structured matrices for 3D point clouds, Point Monarch, which can capture local geometric features of irregular points while offering high expressiveness. MoST reparameterizes the dense update weight matrices as our sparse Point Monarch matrices, significantly reducing parameters while retaining strong performance. Experiments on various backbones show that MoST is simple, effective, and highly generalizable. It captures local features in point clouds, achieving state-of-the-art results on multiple benchmarks, e.g., 97.5% acc. on ScanObjectNN (PB_50_RS) and 96.2% on ModelNet40 classification, while it can also combine with other matrix decompositions (e.g., Low-rank, Kronecker) to further reduce parameters.

Qi Zhu, Jiangwei Lao, Deyi Ji, Junwei Luo, Kang Wu, Yingying Zhang, Lixiang Ru, Jian Wang, Jingdong Chen, Ming Yang 等

Open-world interpretation aims to accurately localize and recognize all objects within images by vision-language models (VLMs). While substantial progress has been made in this task for natural images, the advancements for remote sensing (RS) images still remain limited, primarily due to these two challenges. 1) Existing RS semantic categories are limited, particularly for pixel-level interpretation datasets. 2) Distinguishing among diverse RS spatial regions solely by language space is challenging due to the dense and intricate spatial distribution in open-world RS imagery. To address the first issue, we develop a fine-grained RS interpretation dataset, Sky-SA, which contains 183,375 high-quality local image-text pairs with full-pixel manual annotations, covering 1,763 category labels, exhibiting richer semantics and higher density than previous datasets. Afterwards, to solve the second issue, we introduce the vision-centric principle for vision-language modeling. Specifically, in the pre-training stage, the visual self-supervised paradigm is incorporated into image-text alignment, reducing the degradation of general visual representation capabilities of existing paradigms. Then, we construct a visual-relevance knowledge graph across open-category texts and further develop a novel vision-centric image-text contrastive loss for fine-tuning with text prompts. This new model, denoted as SkySense-O, demonstrates impressive zero-shot capabilities on a thorough evaluation encompassing 14 datasets over 4 tasks, from recognizing to reasoning and classification to localization. Specifically, it outperforms the latest models such as SegEarth-OV, GeoRSCLIP, and VHM by a large margin, i.e., 11.95%, 8.04% and 3.55% on average respectively.

Xiao Guo, Xiufeng Song, Yue Zhang, Xiaohong Liu, Xiaoming Liu

Deepfake detection is a long-established research topic vital for mitigating the spread of malicious misinformation. Unlike prior methods that provide either binary classification results or textual explanations separately, we introduce a novel method capable of generating both simultaneously. Our method harnesses the multi-modal learning capability of the pre-trained CLIP and the unprecedented interpretability of large language models (LLMs) to enhance both the generalization and explainability of deepfake detection. Specifically, we introduce a multi-modal face forgery detector (M2F2-Det) that employs tailored face forgery prompt learning, incorporating the pre-trained CLIP to improve generalization to unseen forgeries. Also, M2F2-Det incorporates an LLM to provide detailed textual explanations of its detection decisions, enhancing interpretability by bridging the gap between natural language and subtle cues of facial forgeries. Empirically, we evaluate M2F2-Det on both detection and explanation generation tasks, where it achieves state-of-the-art performance, demonstrating its effectiveness in identifying and explaining diverse forgeries. Source code and models are available at this link.

Itay Benou, Tammy Riklin Raviv

Modern deep neural networks have now reached human-level performance across a variety of tasks. However, unlike humans they lack the ability to explain their decisions by showing where and telling what concepts guided them. In this work, we present a unified framework for transforming any vision neural network into a spatially and conceptually interpretable model. We introduce a spatially-aware concept bottleneck layer that projects "black-box" features of pre-trained backbone models into interpretable concept maps, without requiring human labels. By training a classification layer over this bottleneck, we obtain a self-explaining model that articulates which concepts most influenced its prediction, along with heatmaps that ground them in the input image. Accordingly, we name this method "Spatially-Aware and Label-Free Concept Bottleneck Model" (SALF-CBM). Our results show that the proposed SALF-CBM: (1) Outperforms non-spatial CBM methods, as well as the original backbone, on a variety of classification tasks; (2) Produces high-quality spatial explanations, outperforming widely used heatmap-based methods on a zero-shot segmentation task; (3) Facilitates model exploration and debugging, enabling users to query specific image regions and refine the model's decisions by locally editing its concept maps.

Yang Yue, Yulin Wang, Chenxin Tao, Pan Liu, Shiji Song, Gao Huang

Humans can develop internal world models that encode common sense knowledge, telling them how the world works and predicting the consequences of their actions. This concept has emerged as a promising direction for establishing general-purpose machine-learning models in recent preliminary works, e.g., for visual representation learning. In this paper, we present CheXWorld, the first effort towards a self-supervised world model for radiographic images. Specifically, our work develops a unified framework that simultaneously models three aspects of medical knowledge essential for qualified radiologists, including 1) local anatomical structures describing the fine-grained characteristics of local tissues (e.g., architectures, shapes, and textures); 2) global anatomical layouts describing the global organization of the human body (e.g., layouts of organs and skeletons); and 3) domain variations that encourage CheXWorld to model the transitions across different appearance domains of radiographs (e.g., varying clarity, contrast, and exposure caused by collecting radiographs from different hospitals, devices, or patients). Empirically, we design tailored qualitative and quantitative analyses, revealing that CheXWorld successfully captures these three dimensions of medical knowledge. Furthermore, transfer learning experiments across eight medical image classification and segmentation benchmarks showcase that CheXWorld significantly outperforms existing SSL methods and large-scale medical foundation models. Code & pre-trained models are available at https://github.com/LeapLabTHU/CheXWorld.

David Mildenberger, Paul Hager, Daniel Rueckert, Martin J. Menten

Supervised contrastive learning (SupCon) has proven to be a powerful alternative to the standard cross-entropy loss for classification of multi-class balanced datasets. However, it struggles to learn well-conditioned representations of datasets with long-tailed class distributions. This problem is potentially exacerbated for binary imbalanced distributions, which are commonly encountered during many real-world problems such as medical diagnosis. In experiments on seven binary datasets of natural and medical images, we show that the performance of SupCon decreases with increasing class imbalance. To substantiate these findings, we introduce two novel metrics that evaluate the quality of the learned representation space. By measuring the class distribution in local neighborhoods, we are able to uncover structural deficiencies of the representation space that classical metrics cannot detect. Informed by these insights, we propose two new supervised contrastive learning strategies tailored to binary imbalanced datasets that improve the structure of the representation space and increase downstream classification accuracy over standard SupCon by up to 35%.

Abdelrahman Shaker, Syed Talal Wasim, Salman Khan, Juergen Gall, Fahad Shahbaz Khan

State-space models (SSMs) have recently shown promise in capturing long-range dependencies with subquadratic computational complexity, making them attractive for various applications. However, purely SSM-based models face critical challenges related to stability and achieving state-of-the-art performance in computer vision tasks. Our paper addresses the challenges of scaling SSM-based models for computer vision, particularly the instability and inefficiency of large model sizes. We introduce a parameter-efficient modulated group mamba layer that divides the input channels into four groups and applies our proposed SSM-based efficient Visual Single Selective Scanning (VSSS) block independently to each group, with each VSSS block scanning in one of the four spatial directions. The Modulated Group Mamba layer also wraps the four VSSS blocks into a channel modulation operator to improve cross-channel communication. Furthermore, we introduce a distillation-based training objective to stabilize the training of large models, leading to consistent performance gains. Our comprehensive experiments demonstrate the merits of the proposed contributions, leading to superior performance over existing methods for image classification on ImageNet-1K, object detection, instance segmentation on MS-COCO, and semantic segmentation on ADE20K. Our tiny variant with 23M parameters achieves state-of-the-art performance with a classification top-1 accuracy of 83.3% on ImageNet-1K, while being 26% efficient in terms of parameters, compared to the best existing Mamba design of same model size. Code and models are available at: https://github.com/Amshaker/GroupMamba

Linwei Chen, Lin Gu, Liang Li, Chenggang Yan, Ying Fu

While Dynamic Convolution (DY-Conv) has shown promising performance by enabling adaptive weight selection through multiple parallel weights combined with an attention mechanism, the frequency response of these weights tends to exhibit high similarity, resulting in high parameter costs but limited adaptability. In this work, we introduce Frequency Dynamic Convolution (FDConv), a novel approach that mitigates these limitations by learning a fixed parameter budget in the Fourier domain. FDConv divides this budget into frequency-based groups with disjoint Fourier indices, enabling the construction of frequency-diverse weights without increasing the parameter cost. To further enhance adaptability, we propose Kernel Spatial Modulation (KSM) and Frequency Band Modulation (FBM). KSM dynamically adjusts the frequency response of each filter at the spatial level, while FBM decomposes weights into distinct frequency bands in the frequency domain and modulates them dynamically based on local content. Extensive experiments on object detection, segmentation, and classification validate the effectiveness of FDConv. We demonstrate that when applied to ResNet-50, FDConv achieves superior performance with a modest increase of +3.6M parameters, outperforming previous methods that require substantial increases in parameter budgets (e.g., CondConv +90M, KW +76.5M). Moreover, FDConv seamlessly integrates into a variety of architectures, including ConvNeXt, Swin-Transformer, offering a flexible and efficient solution for modern vision tasks. The code is made publicly available at https://github.com/Linwei-Chen/FDConv.