Diffusion models have significantly advanced the field of image synthesis, making the protection of their intellectual property (IP) a critical concern. Existing IP protection methods primarily focus on embedding watermarks into generated images by altering the structure of the diffusion process. However, these approaches inevitably compromise the quality of the generated images and are particularly vulnerable to fine-tuning attacks, especially for open-source models such as Stable Diffusion (SD). In this paper, we propose PlugMark, a novel plug-in zero-watermarking framework for diffusion models. The core idea of PlugMark is based on two observations: a classifier can be uniquely characterized by its decision boundaries, and a diffusion model can be uniquely represented by the knowledge acquired from training data.Building on this foundation, we introduce a diffusion knowledge extractor that can be plugged into a diffusion model to extract its knowledge and output a classification result. PlugMark subsequently generates boundary representations based on this classification result, serving as a zero-distortion watermark that uniquely represents the decision boundaries and, by extension, the knowledge of the diffusion model. Since only the extractor requires training, the performance of the original diffusion model remains unaffected.Extensive experimental results demonstrate that PlugMark can robustly extract high-confidence zero-watermarks from both the original model and its post-processed versions while effectively distinguishing them from non-post-processed diffusion models.
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Large Learning Rates Simultaneously Achieve Robustness to Spurious Correlations and Compressibility
PDF ↗Robustness and resource-efficiency are two highly desirable properties for modern machine learning models. However, achieving them jointly remains a challenge. In this paper, we identify high learning rates as a facilitator for simultaneously achieving robustness to spurious correlations and network compressibility. We demonstrate that large learning rates also produce desirable representation properties such as invariant feature utilization, class separation, and activation sparsity. Our findings indicate that large learning rates compare favorably to other hyperparameters and regularization methods, in consistently satisfying these properties in tandem. In addition to demonstrating the positive effect of large learning rates across diverse spurious correlation datasets, models, and optimizers, we also present strong evidence that the previously documented success of large learning rates in standard classification tasks is related to addressing hidden/rare spurious correlations in the training dataset. Our investigation of the mechanisms underlying this phenomenon reveals the importance of confident mispredictions of bias-conflicting samples under large learning rates.
Unlike closed-vocabulary 3D instance segmentation that is often trained end-to-end, open-vocabulary 3D instance segmentation (OV-3DIS) often leverages vision-language models (VLMs) to generate 3D instance proposals and classify them. While various concepts have been proposed from existing research, we observe that these individual concepts are not mutually exclusive but complementary. In this paper, we propose a new state-of-the-art solution for OV-3DIS by carefully designing a recipe to combine the concepts together and refining them to address key challenges. Our solution follows the two-stage scheme: 3D proposal generation and instance classification. We employ robust 3D tracking-based proposal aggregation to generate 3D proposals and remove overlapped or partial proposals by iterative merging/removal. For the classification stage, we replace the standard CLIP model with Alpha-CLIP, which incorporates object masks as an alpha channel to reduce background noise and obtain object-centric representation. Additionally, we introduce the standardized maximum similarity (SMS) score to normalize text-to-proposal similarity, effectively filtering out false positives and boosting precision. Our framework achieves state-of-the-art performance on ScanNet200 and S3DIS across all AP and AR metrics, even surpassing an end-to-end closed-vocabulary method.
Self-supervised contrastive learning models, such as CLIP, have set new benchmarks for vision-language models in many downstream tasks. However, their dependency on rigid one-to-one mappings overlooks the complex and often multifaceted relationships between and within texts and images. To this end, we introduce RankCLIP, a novel pretraining method that extends beyond the rigid one-to-one matching framework of CLIP and its variants. By extending the traditional pair-wise loss to list-wise, and leveraging both in-modal and cross-modal ranking consistency, RankCLIP improves the alignment process, enabling it to capture the nuanced many-to-many relationships between and within each modality. Through comprehensive experiments, we demonstrate the effectiveness of RankCLIP in various downstream tasks, notably achieving significant gains in zero-shot classifications over state-of-the-art methods, underscoring the importance of this enhanced learning process.
Humans recognize objects after observing only a few examples, a remarkable capability enabled by their inherent language understanding of the real-world environment. Developing verbalized and interpretable representation can significantly improve model generalization in low-data settings. In this work, we propose Verbalized Representation Learning (VRL), a novel approach for automatically extracting human-interpretable features for object recognition using few-shot data. Our method uniquely captures inter-class differences and intra-class commonalities in the form of natural language by employing a Vision-Language Model (VLM) to identify key discriminative features between different classes and shared characteristics within the same class. These verbalized features are then mapped to numeric vectors through the VLM. The resulting feature vectors can be further utilized to train and infer with downstream classifiers. Experimental results show that, at the same model scale, VRL achieves a 24% absolute improvement over prior state-of-the-art methods while using 95% less data and a smaller model. Furthermore, compared to human-labeled attributes, the features learned by VRL exhibit a 20% absolute gain when used for downstream classification tasks.
Revisiting Efficient Semantic Segmentation: Learning Offsets for Better Spatial and Class Feature Alignment
PDF ↗Semantic segmentation is fundamental to vision systems requiring pixel-level scene understanding, yet deploying it on resource-constrained devices demands efficient architectures. Although existing methods achieve real-time inference through lightweight designs, we reveal their inherent limitation: misalignment between class representations and image features caused by a per-pixel classification paradigm. With experimental analysis, we find that this paradigm results in a highly challenging assumption for efficient scenarios: Image pixel features should not vary for the same category in different images. To address this dilemma, we propose a coupled dual-branch offset learning paradigm that explicitly learns feature and class offsets to dynamically refine both class representations and spatial image features. Based on the proposed paradigm, we construct an efficient semantic segmentation network, OffSeg. Notably, the offset learning paradigm can be adopted to existing methods with no additional architectural changes. Extensive experiments on four datasets, including ADE20K, Cityscapes, COCO-Stuff-164K, and Pascal Context, demonstrate consistent improvements with negligible parameters.
In this work, we introduce long-video masked-embedding autoencoders (LV-MAE), a self-supervised learning framework for long video representation.Our approach treats short- and long-span dependencies as two separate tasks.Such decoupling allows for a more intuitive video processing where short-span spatiotemporal primitives are first encoded and are then used to capture long-range dependencies across consecutive video segments. To achieve this, we leverage advanced off-the-shelf multimodal encoders to extract representations from short segments within the long video, followed by pre-training a masked-embedding autoencoder capturing high-level interactions across segments.LV-MAE is highly efficient to train and enables the processing of much longer videos by alleviating the constraint on the number of input frames.Furthermore, unlike existing methods that typically pre-train on short-video datasets, our approach offers self-supervised pre-training using long video samples (e.g., 20+ minutes video clips) at scale.Using LV-MAE representations, we achieve state-of-the-art results on three long-video benchmarks -- LVU, COIN, and Breakfast -- employing only a simple classification head for either attentive or linear probing.Finally, to assess LV-MAE pre-training and visualize its reconstruction quality, we leverage the video-language aligned space of short video representations to monitor LV-MAE through video-text retrieval.Our code will be made available upon publication.
Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis
PDF ↗While multiple instance learning (MIL) has shown to be a promising approach for histopathological whole slide image (WSI) analysis, its reliance on permutation invariance significantly limits its capacity to effectively uncover semantic correlations between instances within WSIs. Based on our empirical and theoretical investigations, we argue that approaches that are not permutation-invariant but better capture spatial correlations between instances can offer more effective solutions. In light of these findings, we propose a novel alternative to existing MIL for WSI analysis by learning to restore the order of instances from their randomly shuffled arrangement. We term this task as cracking an instance jigsaw puzzle problem, where semantic correlations between instances are uncovered. To tackle the instance jigsaw puzzles, we propose a novel Siamese network solution, which is theoretically justified by optimal transport theory. We validate the proposed method on WSI classification and survival prediction tasks, where the proposed method outperforms the recent state-of-the-art MIL competitors.
Applying pretraining-finetuning paradigm to event cameras presents significant challenges due to the scarcity of large-scale event datasets and the inherently sparse nature of event data, which increases the risk of overfitting during extensive pretraining.In this paper, we explore the transfer of pretrained image knowledge to the domain of event cameras to address this challenge. The key to our approach lies in adapting event data representations to align with image pretrained models while simultaneously integrating spatiotemporal information and mitigating data sparsity. To achieve this, we propose a lightweight SpatioTemporal information fusion Prompting (STP) method, which progressively fuses the spatiotemporal characteristics of event data through a dynamic perception module with multi-scale spatiotemporal receptive fields, enabling compatibility with image pretrained models.STP enhances event data representation by capturing local information within a large receptive field and performing global information exchange along the temporal dimension. This strategy effectively reduces sparse regions in event data while refining fine-grained details, all while preserving its inherent spatiotemporal structure. Our method significantly outperforms previous state-of-the-art approaches across classification, semantic segmentation, and optical flow estimation tasks. For instance, it achieves a top-1 accuracy of 68.87% (+4.04%) on N-ImageNet with only 1/10 of the pretraining parameters and 1/3 of the training epochs.
Infrared and visible image fusion (IVF) endeavors to engineer composite outputs by blending optimal virtues of divergent modalities. This paper reveals, unprecedentedly, the intrinsic "attention properties" of infrared images, which directly arise from their physical characteristics (i.e., heat distribution) and can be linked to attention mechanisms naturally, as observed in the gradient-weighted class activation mapping (Grad-CAM) visualization analysis of image classification models. To incorporate this property into IVF for better fusion, we propose the source infrared cross attention (I-SCA) and further extend it to the visible modality, subsequently introducing the source visible cross attention (V-SCA). The joint use of I-SCA and V-SCA greatly alleviate longstanding issues, such as insufficient and incomplete multimodal feature interaction and fusion, in IVF. Moreover, an auxiliary component for I-SCA and V-SCA, termed CBSM, is employed to boost the channel, map space, and suppress redundancy and misleading information of the source images. Specifically, we directly treat the CBSM-processed raw image as the query, while the intermediate features of another modality are treated as keys and values in I-SCA and V-SCA. Unlike attention mechanisms that divide images into patches or limit computations to local windows, our cross attention modules achieve smoother and more robust IVF through true global modeling across the entire image space with linear complexity. Comparison with current SOTA methods on three popular public datasets confirms its superiority.
Granular Concept Circuits: Toward a Fine-Grained Circuit Discovery for Concept Representations
PDF ↗Deep vision models have achieved remarkable classification performance by leveraging a hierarchical architecture in which human-interpretable concepts emerge through the composition of individual neurons across layers. Given the distributed nature of representations, pinpointing where specific visual concepts are encoded within a model remains a crucial yet challenging task. In this paper, we introduce an effective circuit discovery method, called Granular Concept Circuit (GCC)(Code is available at https://github.com/daheekwon/GCC)., in which each circuit represents a concept relevant to a given query. To construct each circuit, our method iteratively assesses inter-neuron connectivity, focusing on both functional dependencies and semantic alignment. By automatically discovering multiple circuits, each capturing specific concepts within that query, our approach offers a profound, concept-wise interpretation of models and is the first to identify circuits tied to specific visual concepts at a fine-grained level. We validate the versatility and effectiveness of GCCs across various deep image classification models.
ResQ: A Novel Framework to Implement Residual Neural Networks on Analog Rydberg Atom Quantum Computers
PDF ↗Research in quantum machine learning has recently proliferated due to the potential of quantum computing to accelerate machine learning. An area of machine learning that has not yet been explored is neural ordinary differential equation (neural ODE) based residual neural networks (ResNets), which aim to improve the effectiveness of neural networks using the principles of ordinary differential equations. In this work, we present our insights about why analog Rydberg atom quantum computers are especially well-suited for ResNets. We also introduce ResQ, a novel framework to optimize the dynamics of Rydberg atom quantum computers to solve classification problems in machine learning using analog quantum neural ODEs.
Designing effective foundation models requires high-quality evaluation datasets. With the emergence of audio-visual foundation models, reliable assessment of their multi-modal understanding is essential. The current gold standard for evaluating audio-visual understanding is the popular classification dataset VGGSound. However, our analysis identifies several critical issues in VGGSound, including incomplete labelling, partially overlapping classes, and misaligned modalities. These flaws lead to distorted evaluations of models' true auditory and visual capabilities. To address these limitations, we introduce VGGSounder, a comprehensively re-annotated, multi-label test set extending VGGSound that is explicitly designed to accurately evaluate audio-visual foundation models. VGGSounder features detailed modality annotations, enabling precise analyses of modality-specific performance and revealing previously unnoticed model limitations. We believe VGGSounder offers a robust and reliable benchmark supporting the future development of audio-visual foundation models.
From Objects to Events: Unlocking Complex Visual Understanding in Object Detectors via LLM-guided Symbolic Reasoning
PDF ↗Current object detectors excel at entity localization and classification, yet exhibit inherent limitations in event recognition capabilities. This deficiency arises from their architecture's emphasis on discrete object identification rather than modeling the compositional reasoning, inter-object correlations, and contextual semantics essential for comprehensive event understanding. To address this challenge, we present a novel framework that expands the capability of standard object detectors beyond mere object recognition to complex event understanding through LLM-guided symbolic reasoning. Our key innovation lies in bridging the semantic gap between object detection and event understanding without requiring expensive task-specific training. The proposed plug-and-play framework interfaces with any open-vocabulary detector while extending their inherent capabilities across architectures. At its core, our approach combines (i) a symbolic regression mechanism exploring relationship patterns among detected entities and (ii) a LLM-guided strategically guiding the search toward meaningful expressions. These discovered symbolic rules transform low-level visual perception into interpretable event understanding, providing a transparent reasoning path from objects to events with strong transferability across domains.We compared our training-free framework against specialized event recognition systems across diverse application domains. Experiments demonstrate that our framework enhances multiple object detector architectures to recognize complex events such as illegal fishing activities (75 %AUROC, +8.36 %improvement), construction safety violations (+15.77%), and abnormal crowd behaviors (+23.16%).
Wildlife observation plays an important role in biodiversity conservation, necessitating robust methodologies for monitoring wildlife populations and interspecies interactions. Recent advances in computer vision have significantly contributed to automating fundamental wildlife observation tasks, such as animal detection and species identification. However, accurately identifying species from indirect evidence like footprints and feces remains relatively underexplored, despite its importance in contributing to wildlife monitoring. To bridge this gap, we introduce AnimalClue, the first large-scale dataset for species identification from images of indirect evidence. Our dataset consists of 159,605 bounding boxes encompassing five categories of indirect clues: footprints, feces, eggs, bones, and feathers. It covers 968 species, 200 families, and 65 orders. Each image is annotated with species-level labels, bounding boxes or segmentation masks, and fine-grained trait information, including activity patterns and habitat preferences. Unlike existing datasets primarily focused on direct visual features (e.g., animal appearances), AnimalClue presents unique challenges for classification, detection, and instance segmentation tasks due to the need for recognizing more detailed and subtle visual features. In our experiments, we extensively evaluate representative vision models and identify key challenges in animal identification from their traces. Our dataset and code are available at https://dahlian00.github.io/AnimalCluePage/.
Adversarial examples, crafted with imperceptible perturbations, reveal a significant vulnerability of Deep Neural Networks (DNNs). More critically, the transferability of adversarial examples allows attackers to induce unreasonable predictions without requiring knowledge about the target model. DNNs exhibit spatial invariance, meaning that the position of an object does not affect the classification result. However, existing input transformation-based adversarial attacks solely focus on behavioral patterns at a singular position, failing to fully exploit the spatial invariance exhibited by DNNs across multiple positions, thus constraining the transferability of adversarial examples. To address this, we propose a multi-scale, multi-position input transformation-based attack called Spatial Invariance Diversity (SID). Specifically, SID uses hybrid spatial-spectral fusion mechanisms within localized receptive fields, followed by multi-scale spatial downsampling and positional perturbations via random transformations, thereby crafting an ensemble of inputs to activate diverse behavioral patterns for effective adversarial perturbations. Extensive experiments on the ImageNet dataset demonstrate that SID could achieve better transferability than the current state-of-the-art input transformation-based attacks. Additionally, SID can be flexibly integrated with other input transformation-based or gradient-based attacks, further enhancing the transferability of adversarial examples. The code is available at https://github.com/TheMoss7/SID.
Understanding 3D cell shape is crucial in biomedical research, where morphology serves as a key indicator of disease, cellular state, and drug response. However, many existing 3D point cloud classification models lack interpretability, limiting their utility for extracting biologically meaningful insights. In this work, we unify standard point cloud backbones and feature aggregation strategies within a Multiple Instance Learning (MIL) framework to enable inherently interpretable classification. Our approach, PointMIL, improves classification performance while providing fine-grained point-level explanations without relying on post hoc analysis. We demonstrate state-of-the-art mACC (97.3%) and F1 (97.5%) in the IntrA biomedical dataset and evaluate the interpretability using quantitative and qualitative metrics. Additionally, we introduce ATLAS-1, a novel dataset of drug-treated 3D cancer cells, and use it to show how PointMIL captures fine-grained morphological effects of chemical treatments. Beyond biomedical applications, PointMIL generalises to standard benchmarks such as ModelNet40 and ScanObjectNN, offering interpretable 3D object recognition across domains
Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation
PDF ↗Recently, multi-view learning (MVL) has garnered significant attention due to its ability to fuse discriminative information from multiple views. However, real-world multi-view datasets are often heterogeneous and imperfect, which usually causes MVL methods designed for specific combinations of views to lack application potential and limits their effectiveness. To address this issue, we propose a novel robust MVL method (namely RML) with simultaneous representation fusion and alignment. Specifically, we introduce a simple yet effective multi-view transformer fusion network where we transform heterogeneous multi-view data into homogeneous word embeddings, and then integrate multiple views by the sample-level attention mechanism to obtain a fused representation. Furthermore, we propose a simulated perturbation based multi-view contrastive learning framework that dynamically generates the noise and unusable perturbations for simulating imperfect data conditions. The simulated noisy and unusable data obtain two distinct fused representations, and we utilize contrastive learning to align them for learning discriminative and robust representations. Our RML is self-supervised and can also be applied for downstream tasks as a regularization. In experiments, we employ it in multi-view unsupervised clustering, noise-label classification, and as a plug-and-play module for cross-modal hashing retrieval. Extensive comparison experiments and ablation studies validate RML's effectiveness. Code is available at https://github.com/SubmissionsIn/RML.
Flow-MIL: Constructing Highly-expressive Latent Feature Space For Whole Slide Image Classification Using Normalizing Flow
PDF ↗Whole Slide Image (WSI) classification has been widely used in pathological diagnosis and prognosis prediction, and it is commonly formulated as a weakly-supervised Multiple Instance Learning (MIL) problem because of the large size of WSIs and the difficulty of obtaining fine-grained annotations. In the MIL formulation, a WSI is treated as a bag and the patches cut from it are treated as its instances, and most existing methods first extract instance features and then aggregate them into bag feature using attention-based mechanism for bag-level prediction. These models are trained using only bag-level labels, so they often lack instance-level insights and lose detailed semantic information, which limits their bag-level classification performance and damages their ability to explore high-expressive information. In this paper, we propose Flow-MIL, which leverages normalizing flow-based Latent Semantic Embedding Space (LSES) to enhance feature representation. By mapping patches into the simple and highly-expressive latent space LSES, Flow-MIL achieves effective slide-level aggregation while preserving critical semantic information. We also introduce Gaussian Mixture Model-based Latent Semantic Prototypes (LSP) within the LSES to capture class-specific pathological distribution for each class and refine pseudo instance labels. Extensive experiments on three public WSI datasets show that Flow-MIL outperforms recent SOTA methods in both bag-level and instance-level classification and offers improved interpretability.
Compared to 2D data, the scale of point cloud data in different domains available for training, is quite limited. Researchers have been trying to combine these data of different domains for masked autoencoder (MAE) pre-training to leverage such a data scarcity issue. However, the prior knowledge learned from mixed domains may not align well with the downstream 3D point cloud analysis tasks, leading to degraded performance. To address such an issue, we propose the Domain-Adaptive Point Cloud Masked Autoencoder (DAP-MAE), an MAE pre-training method, to adaptively integrate the knowledge of cross-domain datasets for general point cloud analysis. In DAP-MAE, we design a heterogeneous domain adapter that utilizes an adaptation mode during pre-training, enabling the model to comprehensively learn information from point clouds across different domains, while employing a fusion mode in the fine-tuning to enhance point cloud features. Meanwhile, DAP-MAE incorporates a domain feature generator to guide the adaptation of point cloud features to various downstream tasks. With only one pre-training, DAP-MAE achieves excellent performance across four different point cloud analysis tasks, reaching 95.18% in object classification on ScanObjectNN and 88.45% in facial expression recognition on Bosphorus. The code will be released at https://github.com/CVI-SZU/DAP-MAE