We introduce Fish-Visual Trait Analysis (Fish-Vista), the first organismal image dataset designed for the analysis of visual traits of aquatic species directly from images using machine learning and computer vision methods. Fish-Vista contains 69,269 annotated images spanning 4,316 fish species, curated and organized to serve three downstream tasks: species classification, trait identification, and trait segmentation. Our work makes two key contributions. First, we provide a fully reproducible data processing pipeline to process fish images sourced from various museum collections, contributing to the advancement of AI in biodiversity science. We annotate the images with carefully curated labels from biological databases and manual annotations to create an AI-ready dataset of visual traits. Second, our work offers fertile grounds for researchers to develop novel methods for a variety of problems in computer vision such as handling long-tailed distributions, out-of-distribution generalization, learning with weak labels, explainable AI, and segmenting small objects. Dataset and code for Fish-Vista are available at https://github.com/Imageomics/Fish-Vista
论文检索
输入标题、作者或关键词，从 7,876 篇学术成果中精准定位
Deep Neural Networks (DNNs), backbone architecture in `almost' every computer vision task, are vulnerable to adversarial attacks, particularly physical out-of-distribution (OOD) adversarial patches. Existing defense models often struggle with interpreting these attacks in ways that align with human visual perception. Our proposed AdvPatchXAI approach introduces a generalized, robust, and explainable defense algorithm designed to defend DNNs against physical adversarial threats. AdvPatchXAI employs a novel patch decorrelation loss that reduces feature redundancy and enhances the distinctiveness of patch representations, enabling better generalization across unseen adversarial scenarios. It learns prototypical parts self-supervised, enhancing interpretability and correlation with human vision. The model utilizes a sparse linear layer for classification, making the decision process globally interpretable through a set of learned prototypes and locally explainable by pinpointing relevant prototypes within an image. Our comprehensive evaluation shows that AdvPatchXAI closes the "semantic" gap between latent space and pixel space and effectively handles unseen adversarial patches even perturbed with unseen corruptions, thereby significantly advancing DNN robustness in practical settings(https://github.com/tbvl22/Unified-resilient-and-Explainable-Adversarial-Patch-detector).
Medical Visual Question Answering (Med-VQA) systems benefit the interpretation of medical images containing critical clinical information. However, the challenge of noisy labels and limited high-quality datasets remains underexplored. To address this, we establish the first benchmark for noisy labels in Med-VQA by simulating human mislabeling with semantically designed noise types. More importantly, we introduce the DiN framework, which leverages a diffusion model to handle noisy labels in Med-VQA. Unlike the dominant classification-based VQA approaches that directly predict answers, our Answer Diffuser (AD) module employs a coarse-to-fine process, refining answer candidates with a diffusion model for improved accuracy. The Answer Condition Generator (ACG) further enhances this process by generating task-specific conditional information via integrating answer embeddings with fused image-question features. To address label noise, our Noisy Label Refinement(NLR) module introduces a robust loss function and dynamic answer adjustment to further boost the performance of the AD module. Our DiN framework consistently outperforms existing methods across multiple benchmarks with varying noise levels.
State Space Models (SSMs) have shown significant promise in Natural Language Processing (NLP) and, more recently, computer vision. This paper introduces a new methodology leveraging Mamba and Masked Autoencoder (MAE) networks for point cloud data in both supervised and self-supervised learning. We propose three key contributions to enhance Mamba's capability in processing complex point cloud structures. First, we exploit the spectrum of a graph Laplacian to capture patch connectivity, defining an isometry-invariant traversal order that is robust to viewpoints and better captures shape manifolds than traditional 3D grid-based traversals. Second, we adapt segmentation via a recursive patch partitioning strategy informed by Laplacian spectral components, allowing finer integration and segment analysis. Third, we address token placement in MAE for Mamba by restoring tokens to their original positions, which preserves essential order and improves learning. Extensive experiments demonstrate our approach's improvements in classification, segmentation, and few-shot tasks over state-of-the-art (SOTA) baselines.
In representation learning, uniformity refers to the uniform feature distribution in the latent space (i.e., unit hypersphere). Previous work has shown that improving uniformity contributes to the learning of under-represented classes. However, most of the previous work focused on classification; the representation space of imbalanced regression remains unexplored. Classification-based methods are not suitable for regression tasks because they cluster features into distinct groups without considering the continuous and ordered nature essential for regression. In a geometric aspect, we uniquely focus on ensuring uniformity in the latent space for imbalanced regression through two key losses: enveloping and homogeneity. The enveloping loss encourages the induced trace to uniformly occupy the surface of a hypersphere, while the homogeneity loss ensures smoothness, with representations evenly spaced at consistent intervals. Our method integrates these geometric principles into the data representations via a Surrogate-driven Representation Learning (SRL) framework. Experiments with real-world regression and operator learning tasks highlight the importance of uniformity in imbalanced regression and validate the efficacy of our geometry-based loss functions.
Semi-supervised crowd counting is crucial for addressing the high annotation costs of densely populated scenes. Although several methods based on pseudo-labeling have been proposed, it remains challenging to effectively and accurately utilize unlabeled data. In this paper, we propose a novel framework called Taste More Taste Better (TMTB), which emphasizes both data and model aspects. Firstly, we explore a data augmentation technique well-suited for the crowd counting task. By inpainting the background regions, this technique can effectively enhance data diversity while preserving the fidelity of the entire scenes. Secondly, we introduce the Visual State Space Model as backbone to capture the global context information from crowd scenes, which is crucial for extremely crowded, low-light, and adverse weather scenarios. In addition to the traditional regression head for exact prediction, we employ an Anti-Noise classification head to provide less exact but more accurate supervision, since the regression head is sensitive to noise in manual annotations. We conduct extensive experiments on four benchmark datasets and show that our method outperforms state-of-the-art methods by a large margin. Code is publicly available on https://github.com/syhien/taste_more_taste_better.
Generalized Zero-Shot Learning (GZSL) addresses the challenge of classifying unseen classes in the presence of seen classes by leveraging semantic attributes to bridge the gap for unseen classes. However, in image based disease classification, such as glioma sub-typing, distinguishing between classes using image semantic attributes can be challenging. To address this challenge, we introduce a novel GZSL method that eliminates the dependency on semantic information. Specifically, we propose that the primary of most classification in clinic is risk stratification, and classes are inherently ordered rather than purely categorical. Based on this insight, we present an inter-class feature augmentation (IFA) module, where distributions of different classes are ordered by their risk levels in a learned feature space using pre-defined joint conditional Gaussian distribution model. This ordering enables the generation of unseen class features through feature mixing of adjacent seen classes, effectively transforming the zero-shot learning problem into a supervised learning task. Our method eliminates the need for explicit semantic information, avoiding the cross-modal alignment between visual and semantic features. Moreover, the IFA module for GZSL requires no structural modifications to the existing classification models. In the experiment, both in-house and public datasets are used to evaluate our method across different tasks, including glioma subtyping, Alzheimer's disease (AD) classification and diabetic retinopathy classification. Experimental results demonstrate that our method outperforms the state-of-the-art GZSL methods with statistical significance.
SATA: Spatial Autocorrelation Token Analysis for Enhancing the Robustness of Vision Transformers
PDF ↗Over the past few years, vision transformers (ViTs) have consistently demonstrated remarkable performance across various visual recognition tasks. However, attempts to enhance their robustness have yielded limited success, mainly focusing on different training strategies, input patch augmentation, or network structural enhancements. These approaches often involve extensive training and fine-tuning, which are time-consuming and resource-intensive. To tackle these obstacles, we introduce a novel approach named Spatial Autocorrelation Token Analysis (SATA). By harnessing spatial relationships between token features, SATA enhances both the representational capacity and robustness of ViT models. This is achieved through the analysis and grouping of tokens according to their spatial autocorrelation scores prior to their input into the Feed-Forward Network (FFN) block of the self-attention mechanism. Importantly, SATA seamlessly integrates into existing pre-trained ViT baselines without requiring retraining or additional fine-tuning, while concurrently improving efficiency by reducing the computational load of the FFN units. Experimental results show that the baseline ViTs enhanced with SATA not only achieve a new state-of-the-art top-1 accuracy on ImageNet-1K image classification (94.9%) but also establish new state-of-the-art performance across multiple robustness benchmarks, including ImageNet-A (top-1=63.6%), ImageNet-R (top-1=79.2%), and ImageNet-C (mCE=13.6%), all without requiring additional training or fine-tuning of baseline models. Availability: https://github.com/nick-nikzad/SATA
Mamba, an architecture with RNN-like token mixer of state space model (SSM), was recently introduced to address the quadratic complexity of the attention mechanism and subsequently applied to vision tasks. Nevertheless, the performance of Mamba for vision is often underwhelming when compared with convolutional and attention-based models. In this paper, we delve into the essence of Mamba, and conceptually conclude that Mamba is ideally suited for tasks with long-sequence and autoregressive characteristics. For vision tasks, as image classification on ImageNet does not align with either characteristic, we hypothesize that Mamba is not necessary for this task; Detection and segmentation tasks on COCO or ADE20K are also not autoregressive, yet they adhere to the long-sequence characteristic, so we believe it is still worthwhile to explore Mamba's potential for these tasks. To empirically verify our hypotheses, we construct a series of models named MambaOut through stacking Mamba blocks while removing their core token mixer, SSM. Experimental results strongly support our hypotheses. Specifically, our MambaOut model surpasses all visual Mamba models on ImageNet image classification, indicating that Mamba is indeed unnecessary for this task. As for detection and segmentation, MambaOut cannot match the performance of state-of-the-art visual Mamba models, demonstrating the potential of Mamba for long-sequence visual tasks.
Gain from Neighbors: Boosting Model Robustness in the Wild via Adversarial Perturbations Toward Neighboring Classes
PDF ↗Recent approaches, such as data augmentation, adversarial training, and transfer learning, have shown potential in addressing the issue of performance degradation caused by distributional shifts. However, they typically demand careful design in terms of data or models and lack awareness of the impact of distributional shifts. In this paper, we observe that classification errors arising from distribution shifts tend to cluster near the true values, suggesting that misclassifications commonly occur in semantically similar, neighboring categories. Furthermore, robust advanced vision foundation models maintain larger inter-class distances while preserving semantic consistency, making them less vulnerable to such shifts. Building on these findings, we propose a new method called GFN (Gain From Neighbors), which uses gradient priors from neighboring classes to perturb input images and incorporates an inter-class distance-weighted loss to improve class separation. This approach encourages the model to learn more resilient features from data prone to errors, enhancing its robustness against shifts in diverse settings. In extensive experiments across various model architectures and benchmark datasets, GFN consistently demonstrated superior performance. For instance, compared to the current state-of-the-art TAPADL method, our approach achieved a higher corruption robustness of 41.4% on ImageNet-C (+2.3%), without requiring additional parameters and using only minimal data.
Inference-Scale Complexity in ANN-SNN Conversion for High-Performance and Low-Power Applications
PDF ↗Spiking Neural Networks (SNNs) have emerged as a promising substitute for Artificial Neural Networks (ANNs) due to their advantages of fast inference and low power consumption. However, the lack of efficient training algorithms has hindered their widespread adoption. Even efficient ANN-SNN conversion methods necessitate quantized training of ANNs to enhance the effectiveness of the conversion, incurring additional training costs. To address these challenges, we propose an efficient ANN-SNN conversion framework with only inference scale complexity. The conversion framework includes a local threshold balancing algorithm, which enables efficient calculation of the optimal thresholds and fine-grained adjustment of the threshold value by channel-wise scaling. We also introduce an effective delayed evaluation strategy to mitigate the influence of the spike propagation delays. We demonstrate the scalability of our framework in typical computer vision tasks: image classification, semantic segmentation, object detection, and video classification. Our algorithm outperforms existing methods, highlighting its practical applicability and efficiency. Moreover, we have evaluated the energy consumption of the converted SNNs, demonstrating their superior low-power advantage compared to conventional ANNs. This approach simplifies the deployment of SNNs by leveraging open-source pre-trained ANN models, enabling fast, low-power inference with negligible performance reduction.
Modern vision-language models (VLMs) develop patch embedding and convolution backbone within vector space, especially Euclidean ones, at the very founding. When expanding VLMs to a galaxy-scale for understanding astronomical phenomena, the integration of spherical space for planetary orbits and hyperbolic spaces for black holes raises two formidable challenges. a) The current pre-training model is confined to Euclidean space rather than a comprehensive geometric embedding. b) The predominant architecture lacks suitable backbones for anisotropic physical geometries. In this paper, we introduced Galaxy-Walker, a geometry-aware VLM, for the universe-level vision understanding tasks. We proposed the geometry prompt that generates geometry tokens by random walks across diverse spaces on a multi-scale physical graph, along with a geometry adapter that compresses and reshapes the space anisotropy in a mixture-of-experts manner. Extensive experiments demonstrate the effectiveness of our approach, with Galaxy-Walker achieving state-of-the-art performance in both galaxy property estimation (R2 scores up to 0.91) and morphology classification tasks (up to +0.17 F1 improvement in challenging features), significantly outperforming both domain-specific models and general-purpose VLMs.
Classifying images with an interpretable decision-making process is a long-standing problem in computer vision. In recent years, Prototypical Part Networks has gained traction as an approach for self-explainable neural networks, due to their ability to mimic human visual reasoning by providing explanations based on prototypical object parts. However, the quality of the explanations generated by these methods leaves room for improvement, as the prototypes usually focus on repetitive and redundant concepts. Leveraging recent advances in prototype learning, we present a framework for part-based interpretable image classification that learns a set of semantically distinctive object parts for each class, and provides diverse and comprehensive explanations. The core of our method is to learn the part-prototypes in a non-parametric fashion, through clustering deep features extracted from foundation vision models that encode robust semantic information. To quantitatively evaluate the quality of explanations provided by ProtoPNets, we introduce Distinctiveness Score and Comprehensiveness Score. Through evaluation on CUB-200-2011, Stanford Cars and Stanford Dogs datasets, we show that our framework compares favourably against existing ProtoPNets while achieving better interpretability.
Due to the irregular and disordered data structure in 3D point clouds, prior works have focused on designing more sophisticated local representation methods to capture these complex local patterns. However, the recognition performance has saturated over the past few years, indicating that increasingly complex and redundant designs no longer make improvements to local learning. This phenomenon prompts us to diverge from the trend in 3D vision and instead pursue an alternative and successful solution: deeper neural networks. In this paper, we propose DeepLA-Net, a series of very deep networks for point cloud analysis. The key insight of our approach is to exploit a small but mighty local learning block, which reduces 10xfewer FLOPs, enabling the construction of very deep networks. Furthermore, we design a training supervision strategy to ensure smooth gradient backpropagation and optimization in very deep networks. We construct the DeepLA-Net family with a depth of up to 120 blocks --- at least 5xdeeper than recent methods --- trained on a single RTX 3090. An ensemble of the DeepLA-Net achieves state-of-the-art performance on classification and segmentation tasks of S3DIS Area5 (+2.2% mIoU), ScanNet test set (+1.6% mIoU), ScanObjectNN (+2.1% OA), and ShapeNetPart (+0.9% cls.mIoU).
Generalized Category Discovery (GCD) is an intriguing open-world problem that has garnered increasing attention. Given a dataset that includes both labelled and unlabelled images, GCD aims to categorize all images in the unlabelled subset, regardless of whether they belong to known or unknown classes. In GCD, the common practice typically involves applying a spherical projection operator at the end of the self-supervised pretrained backbone, operating within Euclidean or spherical space. However, both of these spaces have been shown to be suboptimal for encoding samples that possess hierarchical structures. In contrast, hyperbolic space exhibits exponential volume growth relative to radius, making it inherently strong at capturing the hierarchical structure of samples from both seen and unseen categories. Therefore, we propose to tackle the category discovery challenge in the hyperbolic space. We introduce HypCD, a simple Hyperbolic framework for learning hierarchy-aware representations and classifiers for generalized Category Discovery. HypCD first transforms the Euclidean embedding space of the backbone network into hyperbolic space, facilitating subsequent representation and classification learning by considering both hyperbolic distance and the angle between samples. This approach is particularly helpful for knowledge transfer from known to unknown categories in GCD. We thoroughly evaluate HypCD on public GCD benchmarks, by applying it to various baseline and state-of-the-art methods, consistently achieving significant improvements.
Hate speech on social media in Turkey remains a critical issue, frequently targeting minority groups. Effective moderation requires not only detecting hateful posts but also identifying the specific hateful expressions within them. To address this, we introduce HATECAT-TR, a span-annotated dataset of Turkish tweets, containing 4465 hateful spans across 2981 posts, each directed at one of eight minority groups. Annotations were created using a semi-automated approach, combining GPT-4o-generated spans with human expert review to ensure accuracy. Each hateful span is categorized into one of five discourse types, enabling a fine-grained analysis of the nature and intent behind hateful content. We frame span detection as binary and multi-class token classification tasks and utilize the state-of-the-art language models to establish a baseline performance for the new dataset. Our findings highlight the challenges of detecting and categorizing implicit hate speech, particularly when spans are subtle and highly contextual. The source code is available at github.com/boun-tabi/hatecat-tr and HATECAT-TR can be shared by complying with the terms of X upon contacting the authors.
Who Speaks Matters: Analysing the Influence of the Speaker’s Linguistic Identity on Hate Classification
PDF ↗Large Language Models (LLMs) offer a lucrative promise for scalable content moderation, including hate speech detection. However, they are also known to be brittle and biased against marginalised communities and dialects. This requires their applications to high-stakes tasks like hate speech detection to be critically scrutinized. In this work, we investigate the robustness of hate speech classification using LLMs particularly when explicit and implicit markers of the speaker’s ethnicity are injected into the input. For explicit markers, we inject a phrase that mentions the speaker’s linguistic identity. For the implicit markers, we inject dialectal features. By analysing how frequently model outputs flip in the presence of these markers, we reveal varying degrees of brittleness across 3 LLMs and 1 LM and 5 linguistic identities. We find that the presence of implicit dialect markers in inputs causes model outputs to flip more than the presence of explicit markers. Further, the percentage of flips varies across ethnicities. Finally, we find that larger models are more robust. Our findings indicate the need for exercising caution in deploying LLMs for high-stakes tasks like hate speech detection.
Extracting entities and relations from scientific text is challenging due to long sentences with densely packed entities. Pipeline approaches address this by first extracting entities and then predicting relations between all possible entity pairs. Since the relation extraction phase operates over this exhaustive set, the inclusion of candidate pairs that may be semantically related but lack syntactic proximity introduces precision errors, ultimately reducing Rel+ F1 metric. We propose a simple yet effective syntactic filtering method based on dependency parsing to prune unlikely entity pairs before relation prediction. By leveraging syntactic proximity in the dependency parse tree, our approach retains structurally plausible pairs and reduces false positives in downstream relation classification. Our method is grounded in consistent statistical patterns observed across all evaluated datasets, reinforcing its generalizability and effectiveness. We integrate this filtering step into architectures such as PL-Marker and HGERE, and evaluate its impact across multiple datasets. Our method improves Rel+ F1 scores significantly by an absolute increase of 3.5–10.3% on SciERC, SciER, and ACE05 datasets. These results highlight the importance of syntactic cues for accurate relation extraction in complex domains like scientific literature.
Identifying causal relationships rather than spurious correlations between words and class labels plays a crucial role in building robust text classifiers. Previous studies proposed using causal effects to distinguish words that are causally related to the sentiment, and then building robust text classifiers using words with high causal effects. However, we find that when a sentence has multiple causally related words simultaneously, the magnitude of causal effects will be significantly reduced, which limits the applicability of previous causal effect-based methods in distinguishing causally related words from spuriously correlated ones. To fill this gap, in this paper, we introduce both the probability of necessity (PN) and probability of sufficiency (PS), aiming to answer the counterfactual question that ‘if a sentence has a certain sentiment in the presence/absence of a word, would the sentiment change in the absence/presence of that word?’. Specifically, we first derive the identifiability of PN and PS under different sentiment monotonicities, and calibrate the estimation of PN and PS via the estimated average treatment effect. Finally, the robust text classifier is built by identifying the words with larger PN and PS as causally related words, and other words as spuriously correlated words, based on a contrastive learning approach name CPNS is proposed to achieve robust sentiment classification. Extensive experiments are conducted on public datasets to validate the effectiveness of our method.
Large language models perform surprisingly well on many zero-shot classification tasks, but are difficult to fairly compare to supervised classifiers due to the lack of a modifiable decision boundary. In this work, we propose and evaluate a method that transforms binary classification tasks into pairwise comparisons between instances within a dataset, using LLMs to produce relative rankings of those instances. Repeated pairwise comparisons can be used to score instances using the Elo rating system (used in chess and other competitions), inducing a confidence ordering over instances in a dataset. We evaluate scheduling algorithms for their ability to minimize comparisons, and show that our proposed algorithm leads to improved classification performance, while also providing more information than traditional zero-shot classification.