Convolutional neural networks (CNNs) learn abstract features to perform object classification, but understanding these features remains challenging due to difficult-to-interpret results or high computational costs. We propose an automatic method to systematically visualize and objectively analyze learned features in CNNs. Specifically, we introduce a linking network that maps the penultimate layer of a pre-trained classifier to the latent space of a generative model (StyleGAN-XL), thereby enabling an interpretable, human-friendly visualization of the classifier's representations. Our findings indicate a congruent semantic order in both spaces, enabling a direct linear mapping between them. Training the linking network is computationally inexpensive and decoupled from training both the GAN and the classifier. We introduce an automatic pipeline that utilizes such GAN-based visualizations to quantify learned representations by analyzing activation changes in the classifier in the image domain. This quantification allows us to investigate learned representations in several thousand units simultaneously and to extract and visualize units selective for specific semantic concepts. Further, we illustrate how our method can quantify and interpret the classifier's decision boundary using counterfactual examples. Overall, our method offers systematic and objective perspectives on learned abstract representations in CNNs.
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Adversarial perturbation is a severe threat to deep learning-based systems such as classification and recognition because it makes the system output wrong answers. Designing robust systems against adversarial perturbation in a \textit{certifiable} manner is important, especially for security-related systems such as face recognition. However, most studies for certifiable robustness are about classifiers, which have quite different characteristics from recognition systems for verification; the former is used in the closed-set scenario, whereas the latter is used in the open-set scenario. In this study, we show that, similar to the image classifications, 1-Lipschitz condition is sufficient for certifiable robustness of the face recognition system. Furthermore, for the given pair of facial images, we derive the upper bound of adversarial perturbation where the 1-Lipschitz face recognition system remains robust. At last, we find that this theoretical result should be carefully applied in practice; Applying a training method to typical face recognition systems results in a very small upper bound for adversarial perturbation. We address this by proposing an alternative training method to attain a certifiably robust face recognition system with large upper bounds. All these theoretical results are supported by experiments on proof-of-concept implementation. We released our source to facilitate further study, which is available at \textcolor{red}{github}.
Ordinal regression is a fundamental problem within the field of computer vision, with customised well-trained models on specific tasks. While pre-trained vision-language models (VLMs) have exhibited impressive performance on various vision tasks, their potential for ordinal regression has received less exploration. In this study, we first investigate CLIP's potential for ordinal regression, from which we expect the model could generalise to different ordinal regression tasks and scenarios. Unfortunately, vanilla CLIP fails on this task, since current VLMs have a well-documented limitation of encapsulating compositional concepts such as number sense. We propose a simple yet effective method called NumCLIP to improve the quantitative understanding of VLMs. We disassemble the exact image to number-specific text matching problem into coarse classification and fine prediction stages. We discretize and phrase each numerical bin with common language concept to better leverage the available pre-trained alignment in CLIP. To consider the inherent continuous property of ordinal regression, we propose a novel fine-grained cross-modal ranking-based regularisation loss specifically designed to keep both semantic and ordinal alignment in CLIP's feature space. Experimental results on three general ordinal regression tasks demonstrate the effectiveness of NumCLIP, with 10% and 3.83% accuracy improvement on historical image dating and image aesthetics assessment task, respectively.
This paper addresses the problem of designing reliable prediction models that abstain from predictions when faced with uncertain or out-of-distribution samples - a recently proposed problem known as Selective Classification in the presence of Out-of-Distribution data (SCOD). We make three key contributions to SCOD. Firstly, we demonstrate that the optimal SCOD strategy involves a Bayes classifier for in-distribution (ID) data and a selector represented as a stochastic linear classifier in a 2D space, using i) the conditional risk of the ID classifier, and ii) the likelihood ratio of ID and out-of-distribution (OOD) data as input. This contrasts with suboptimal strategies from current OOD detection methods and the Softmax Information Retaining Combination (SIRC), specifically developed for SCOD. Secondly, we establish that in a distribution-free setting, the SCOD problem is not Probably Approximately Correct learnable when relying solely on an ID data sample. Third, we introduce POSCOD, a simple method for learning a Plugin estimate of the Optimal SCOD strategy from both an ID data sample and an unlabeled mixture of ID and OOD data. Our empirical results confirm the theoretical findings and demonstrate that our proposed method, POSCOD, outperforms existing OOD methods in effectively addressing the SCOD problem.
Advancements in brain imaging enable the decoding of thoughts and intentions from neural activities. However, the fMRI-to-video decoding of brain signals across multiple subjects encounters challenges arising from structural and coding disparities among individual brains, further compounded by the scarcity of paired fMRI-stimulus data. Addressing this issue, this paper introduces the fMRI Global-Local Functional Alignment (GLFA) projection, a novel approach that aligns fMRIs from diverse subjects into a unified brain space, thereby enhancing cross-subject decoding. Additionally, we present a meticulously curated fMRI-video paired dataset comprising a total of 75k fMRI-stimulus paired samples from 8 individuals. This dataset is approximately 4.5 times larger than the previous benchmark dataset. Building on this, we augment a transformer-based fMRI encoder with a diffusion video generator, delving into the realm of cross-subject fMRI-based video reconstruction. This innovative methodology faithfully captures semantic information from diverse brain signals, resulting in the generation of vivid videos and achieving an impressive average accuracy of 84.7\% in cross-subject semantic classification tasks.
The ProbCover method of Yehuda et al. is a well-motivated algorithm for active learning in low-budget regimes, which attempts to cover the data distribution with balls of a given radius at selected data points. We demonstrate, however, that the performance of this algorithm is extremely sensitive to the choice of this radius hyper-parameter, and that tuning it is quite difficult, with the original heuristic frequently failing. We thus introduce (and theoretically motivate) a generalized notion of coverage, including ProbCover's objective as a special case, but also allowing smoother notions that are far more robust to hyper-parameter choice. We propose an efficient greedy method to optimize this coverage, generalizing ProbCover's algorithm; due to its close connection to kernel herding, we call it MaxHerding. The objective can also be optimized non-greedily through a variant of k-medoids, clarifying the relationship to other low-budget active learning methods. In comprehensive experiments, MaxHerding surpasses existing active learning methods across multiple low-budget image classification benchmarks, and does so with less computational cost than most competitive methods.
Anomaly detection (including classification and segmentation) is a practical and challenging task due to the scarcity of anomaly samples in industrial inspection. Some existing anomaly detection methods address this issue by synthesizing anomalies with noise or external data. However, there is always a large semantic gap between synthetic anomalies and real-world anomalies, resulting in weak performance in anomaly detection. To solve the above problem, we propose a few-shot anomaly-driven generation method, which guides the diffusion model to generate more realistic and diverse anomalies with only a few real anomalies, thereby benefiting training anomaly detection models. Specifically, our work is divided into three stages. In the first stage, we learn the anomaly distribution based on a few given real anomalies and inject the learned knowledge into an embedding. In the second stage, we use the embedding and given bounding boxes to guide the diffusion model to generate realistic and diverse anomalies on specific objects (or textures). In the final stage, we propose a weakly-supervised anomaly detection method to train a more powerful model with generated anomalies. Our method builds upon DRAEM and DesTSeg as the foundation model and conducts experiments on the commonly used industrial anomaly detection dataset, MVTec. The experiments demonstrate that our generated anomalies effectively improve the model performance of both anomaly classification and segmentation tasks simultaneously, eg., DRAEM and DseTSeg achieved a 5.8% and 1.5% improvement in AU-PR metric on segmentation task, respectively. We will make the code and the generated anomalous images (70,760) available for reproducibility.
Reshaping the Online Data Buffering and Organizing Mechanism for Continual Test-Time Adaptation
PDF ↗Continual Test-Time Adaptation (CTTA) involves adapting a pre-trained source model to continually changing unsupervised target domains. In this paper, we systematically analyze the challenges of this task: online environment, unsupervised nature, and the risks of error accumulation and catastrophic forgetting under continual domain shifts. To address these challenges, we reshape the online data buffering and organizing mechanism for CTTA. By introducing an uncertainty-aware importance sampling approach, we dynamically evaluate and buffer the most significant samples with high certainty from the unsupervised, single-pass data stream. By efficiently organizing these samples into a class relation graph, we introduce a novel class relation preservation constraint to maintain the intrinsic class relations, efficiently overcoming catastrophic forgetting. Furthermore, a pseudo-target replay objective is incorporated to assign higher weights to these samples and mitigate error accumulation. Extensive experiments demonstrate the superiority of our method in both segmentation and classification CTTA tasks.
Existing datasets for 3D hand-object interaction are limited either in the data cardinality, data variations in interaction scenarios, or the quality of annotations. In this work, we present a comprehensive new training dataset for hand-object interaction called HOGraspNet. It is the only real dataset that captures full grasp taxonomies, providing grasp annotation and wide intraclass variations. Using grasp taxonomies as atomic actions, their space and time combinatorial can represent complex hand activities around objects. We select 22 rigid objects from the YCB dataset and 8 other compound objects using shape and size taxonomies, ensuring coverage of all hand grasp configurations. The dataset includes diverse hand shapes from 99 participants aged 10 to 74, continuous video frames, and a 1.5M RGB-Depth of sparse frames with annotations. It offers labels for 3D hand and object meshes, 3D keypoints, contact maps, and \emph{grasp labels}. Accurate hand and object 3D meshes are obtained by fitting the hand parametric model (MANO) and the hand implicit function (HALO) to multi-view RGBD frames, with the MoCap system only for objects. Note that HALO fitting does not require any parameter tuning, enabling scalability to the dataset's size with comparable accuracy to MANO. We evaluate HOGraspNet on relevant tasks: grasp classification and 3D hand pose estimation. The result shows performance variations based on grasp type and object class, indicating the potential importance of the interaction space captured by our dataset. The provided data aims at learning universal shape priors or foundation models for 3D hand-object interaction. Our dataset and code are available at https://hograspnet2024.github.io/.
Hyperbolic representations have shown remarkable efficacy in modeling inherent hierarchies and complexities within data structures. Hyperbolic neural networks have been commonly applied for learning such representations from data, but they often fall short in preserving the geometric structures of the original feature spaces. In response to this challenge, our work applies the Gromov-Wasserstein (GW) distance as a novel regularization mechanism within hyperbolic neural networks. The GW distance quantifies how well the original data structure is maintained after embedding the data in a hyperbolic space. Specifically, we explicitly treat the layers of the hyperbolic neural networks as a transport map and calculate the GW distance accordingly. We validate that the GW distance computed based on a training set well approximates the GW distance of the underlying data distribution. Our approach demonstrates consistent enhancements over current state-of-the-art methods across various tasks, including few-shot image classification, as well as semi-supervised graph link prediction and node classification.
Zero-shot Human-Object Interaction (HOI) detection has emerged as a frontier topic due to its capability to detect HOIs beyond a predefined set of categories. This task entails not only identifying the interactiveness of human-object pairs and localizing them but also recognizing both seen and unseen interaction categories. In this paper, we introduce a novel framework for zero-shot HOI detection using Conditional Multi-Modal Prompts, namely CMMP. This approach enhances the generalization of large foundation models, such as CLIP, when fine-tuned for HOI detection. Different from traditional prompt-learning methods, we propose learning decoupled vision and language prompts for interactiveness-aware visual feature extraction and generalizable interaction classification, respectively. Specifically, we integrate prior knowledge of different granularity into conditional vision prompts, including an input-conditioned instance-level prior and a global spatial configuration pattern prior. The former encourages the image encoder to treat instances belonging to seen or potentially unseen HOI concepts equally while the latter provides representative plausible spatial configuration of the human and object under interaction. Besides, we employ language-aware prompt learning with a consistency constraint to preserve the knowledge of the large foundation model to enable better generalization in the text branch. Extensive experiments demonstrate the efficacy of our detector with conditional multi-modal prompts, outperforming previous state-of-the-art on unseen classes of various zero-shot settings.
Vision-Language Pre-training (VLP) has shown the merits of analysing medical images. It efficiently learns visual representations by leveraging supervisions in their corresponding reports, and in turn facilitates analysis and interpretation of intricate imaging data. However, such observation is predominantly justified on single-modality data (mostly 2D images like X-rays), adapting VLP to learning unified representations for medical images in real scenario remains an open challenge. This arises from medical images often encompass a variety of modalities, especially modalities with different dimensions (e.g., 3D images like Computed Tomography), and there are almost no paired multi-dimension data here. To overcome the aforementioned challenges, we propose an \textbf{U}nified \textbf{Med}ical \textbf{I}mage Pre-training framework, namely UniMedI, which utilizes diagnostic reports as common semantic space to create unified representations for diverse modalities of medical images (especially for 2D and 3D images). Under the text's guidance, UniMedI effectively select text-related 2D slices from sophisticated 3D volume, which acts as pseudo-pairs to bridge 2D and 3D data, ultimately enhancing the consistency across various medical imaging modalities. To demonstrate the effectiveness and versatility of UniMedI, we evaluate its performance on both 2D and 3D images across several different datasets, covering a wide range of medical image tasks such as classification, segmentation, and retrieval. UniMedI has demonstrated superior performance in downstream tasks, showcasing its effectiveness in establishing a universal medical visual representation.
Existing automated symbolic optimizer design methods necessitate the use of proxies, often resulting in significant performance degradation when transferring to a target domain. In this paper, we propose a learning based model called Symbolic Optimizer Learner (SOL) that can discover high-performance symbolic optimizers directly on the target. SOL is integrated with symbols and can be directly transformed into a symbolic optimizer. In addition, an unrolled optimization approach is introduced for SOL training. SOL can be embedded into the training process of neural networks, optimizing the target directly without any proxies. Our extensive experiments demonstrate the good performance and high generalizability of SOL through diverse tasks, ranging from classifications to adversarial attacks, from GNN to NLP tasks. On image classification, SOL achieved ~5x speedup and ~3\% accuracy gain. On adversarial attacks, SOL achieved the best attack success rate across seven SOTA defense models. On GNN training, SOL discovered optimizers can outperform Adam on three different datasets. On BERT fine-tuning, SOL also outperformed AdamW on five benchmarks.
Recent model inversion attack algorithms permit adversaries to reconstruct a neural network's private training data just by repeatedly querying the network and inspecting its outputs. In this work, we develop a novel network architecture that leverages sparse-coding layers to obtain superior robustness to this class of attacks. Three decades of computer science research has studied sparse coding in the context of image denoising, object recognition, and adversarial misclassification settings, but to the best of our knowledge, its connection to state-of-the-art privacy vulnerabilities remains unstudied. However, sparse coding architectures suggest an advantageous means to defend against model inversion attacks because they allow us to control the amount of irrelevant private information encoded in a network's intermediate representations in a manner that can be computed efficiently during training and that is known to have little effect on classification accuracy. Specifically, compared to networks trained with a variety of state-of-the-art defenses, our sparse-coding architectures maintain comparable or higher classification accuracy while degrading state-of-the-art training data reconstructions by factors of 1.1 to 18.3 across a variety of reconstruction quality metrics (PSNR, SSIM, FID). This performance advantage holds across 5 datasets ranging from CelebA faces to medical images and MNIST, and across various state-of-the-art SGD-based and GAN-based inversion attacks, including Plug-and-Play attacks. We provide a comprehensive performance evaluation codebase to encourage further research in this regard.
Adversarial robustness has been studied extensively in image classification, especially for the $\ell_\infty$-threat model, but significantly less so for related tasks such as object detection and semantic segmentation. Attacks on semantic segmentation models turn out to be harder than for image classification. We propose novel attacks and motivated by their complementary properties, we put them into an attack ensemble called SEA. We use SEA to show that existing attacks can severely overestimate the robustness of semantic segmentation models. Perhaps surprisingly, existing attempts of adversarial training for semantic segmentation turn out to yield only weakly robust models or are even completely non-robust. We investigate why previous adaptations of adversarial training to semantic segmentation failed and identify insufficient training time and number of attack steps as key elements. In turn we show how recently proposed robust ImageNet backbones can be used to obtain adversarially robust semantic segmentation models with up to six times less training time for Pascal VOC and the more challenging ADE20k.
Machine learning models are vulnerable to tiny adversarial input perturbations optimized to cause a very large output error. To measure this vulnerability, we need reliable methods that can find such adversarial perturbations. For image classification models, evaluation methodologies have emerged that have stood the test of time. However, we argue that in the area of semantic segmentation, a good approximation of the sensitivity to adversarial perturbations requires significantly more effort than what is currently considered satisfactory. To support this claim, we re-evaluate a number of well-known robust segmentation models in an extensive empirical study. We propose new attacks and combine them with the strongest attacks available in the literature. We also analyze the sensitivity of the models in fine detail. The results indicate that most of the state-of-the-art models have a dramatically larger sensitivity to adversarial perturbations than previously reported. We also demonstrate a size-bias: small objects are often more easily attacked, even if the large objects are robust, a phenomenon not revealed by current evaluation metrics. Our results also demonstrate that a diverse set of strong attacks is necessary, because different models are often vulnerable to different attacks.
SeA: Semantic Adversarial Augmentation for Last Layer Features from Unsupervised Representation Learning
PDF ↗With the success of deep learning, deep features that are extracted as outputs from the last few layers of a pre-trained deep model have attracted much attention. Unlike hand-crafted features, deep features are data/task-dependent, while still performing well on different downstream tasks. With the recent advancement in unsupervised representation learning, in this work, we revisit the performance of the last layer features extracted from self-supervised pre-trained models. Compared with fine-tuning that can explore diverse augmentations, e.g., random crop/flipping, in the original input space, obtaining appropriate semantic augmentation in the feature space of extracted deep features is challenging. To unleash the potential of deep features, we propose a novel semantic adversarial augmentation (SeA) in the feature space for learning with fixed deep features. Experiments are conducted on $11$ benchmark downstream classification tasks with $4$ popular pre-trained models. Our method is $2\%$ better than the baseline without SeA on average. Moreover, compared to the expensive fine-tuning that is expected to give better performance, SeA shows a comparable performance on $6$ out of $11$ tasks, demonstrating the effectiveness of our proposal in addition to its efficiency.
Vision-language pre-training such as CLIP enables zero-shot transfer that can classify images according to the candidate class names. While CLIP demonstrates an impressive zero-shot performance on diverse downstream tasks, the distribution from the target data has not been leveraged sufficiently. In this work, we study a novel online zero-shot transfer scenario, where each image arrives in a random order for classification and is visited only once to obtain prediction immediately without storing its representation. Compared with the vanilla zero-shot classification, the proposed problem preserves its flexibility for online service but considers the statistics of the arrived images as the side information to capture the distribution of target data better. To tackle the challenge of effective online optimization, we first develop online label learning to model the target data distribution. Then, the proxy of each class in the vision space can be further optimized with the proposed online proxy learning method to mitigate the modality gap between images and text. The convergence of both online strategies can be theoretically guaranteed. By combining the predicted label from the online label learning and proxy learning, our online zero-shot transfer method (OnZeta) can trade between bias and variance in predictions, which helps achieve $78.94\%$ accuracy on ImageNet without accessing the entire data set. Moreover, extensive experiments on other 13 downstream tasks with different vision encoders show a more than $3\%$ improvement on average, which demonstrates the effectiveness of our proposal.
Concept Bottleneck Models (CBMs) have recently been proposed to address the `black-box' problem of deep neural networks, by first mapping images to a human-understandable concept space and then linearly combining concepts for classification. Such models typically require first coming up with a set of concepts relevant to the task and then aligning the representations of a feature extractor to map to these concepts. However, even with powerful foundational feature extractors like CLIP, there are no guarantees that the specified concepts are detectable, calling into question the faithfulness of using them as explanations. In this work, we leverage recent advances in mechanistic interpretability and propose a novel CBM approach --- called Discover-then-Name-CBM (DN-CBM) --- that inverts the typical paradigm: instead of pre-selecting concepts based on the downstream classification task, we use sparse autoencoders to first discover concepts learnt by the model, and then name them and train linear probes for classification. Our concept extraction strategy is efficient, since it is agnostic to the downstream task, and uses concepts already known to the model. We perform a comprehensive evaluation across multiple datasets and CLIP architectures and show that our method yields semantically meaningful concepts, assigns appropriate names to them that make them easy to interpret, and yields performant and interpretable CBMs.
Different from human nature, it is still common practice today for vision tasks to train deep learning models only initially and on fixed datasets. A variety of approaches have recently addressed handling continual data streams. However, extending these methods to manage out-of-distribution (OOD) scenarios has not effectively been investigated. On the other hand, it has recently been shown that non-continual Neural Mesh Models exhibit strong performance in generalizing to such OOD scenarios. To leverage this decisive property in a continual learning setting, we propose incremental Neural Mesh Models that can be extended with new meshes over time. In addition, we present a latent space initialization strategy that enables us to allocate feature space for future unseen classes in advance and a positional regularization term that forces the features of the different classes to consistently stay in respective latent space regions. We demonstrate the effectiveness of our method through extensive experiments on the Pascal3D and ObjectNet3D datasets and show that our approach outperforms the baselines for classification by 2-6% in the in-domain and by 6-50% in the OOD setting. Our work also presents the first incremental learning approach for pose estimation.