概念解释

Our understanding of the generalization capabilities of neural networks NNs is still incomplete. Prevailing explanations are based on implicit biases of gradient descent GD but they cannot account for the capabilities of models from gradientfree methods nor the simplicity bias recently observed in untrained networks This paper seeks other sources of generalization in NNs. To understand the inductive biases provided by architectures independently from GD we examine untrained randomweight networks Even simple MLPs show strong inductive biases uniform sampling in weight space yields a very biased distribution of functions in terms of complexity But unlike common wisdom NNs do not have an inherent simplicity bias This property depends on components such as ReLUs residual connections and layer normalizations Alternative architectures can be built with a bias for any level of complexity. Transformers also inherit all these properties from their building blocks. We provide a fresh explanation for the success of deep learning independent from gradientbased training It points at promising avenues for controlling the solutions implemented by trained models.

3D visual grounding is a challenging task that often requires direct and dense supervision notably the semantic label for each object in the scene. In this paper we instead study the naturally supervised setting that learns from only 3D scene and QA pairs where prior works underperform. We propose the Language-Regularized Concept Learner (LARC) which uses constraints from language as regularization to significantly improve the accuracy of neuro-symbolic concept learners in the naturally supervised setting. Our approach is based on two core insights: the first is that language constraints (e.g. a word's relation to another) can serve as effective regularization for structured representations in neuro-symbolic models; the second is that we can query large language models to distill such constraints from language properties. We show that LARC improves performance of prior works in naturally supervised 3D visual grounding and demonstrates a wide range of 3D visual reasoning capabilities--from zero-shot composition to data efficiency and transferability. Our method represents a promising step towards regularizing structured visual reasoning frameworks with language-based priors for learning in settings without dense supervision.

Recent advancements in post-hoc and inherently interpretable methods have markedly enhanced the explanations of black box classifier models. These methods operate either through post-analysis or by integrating concept learning during model training. Although being effective in bridging the semantic gap between a model's latent space and human interpretation these explanation methods only partially reveal the model's decision-making process. The outcome is typically limited to high-level semantics derived from the last feature map. We argue that the explanations lacking insights into the decision processes at low and mid-level features are neither fully faithful nor useful. Addressing this gap we introduce the Multi-Level Concept Prototypes Classifier (MCPNet) an inherently interpretable model. MCPNet autonomously learns meaningful concept prototypes across multiple feature map levels using Centered Kernel Alignment (CKA) loss and an energy-based weighted PCA mechanism and it does so without reliance on predefined concept labels. Further we propose a novel classifier paradigm that learns and aligns multi-level concept prototype distributions for classification purposes via Class-aware Concept Distribution (CCD) loss. Our experiments reveal that our proposed MCPNet while being adaptable to various model architectures offers comprehensive multi-level explanations while maintaining classification accuracy. Additionally its concept distribution-based classification approach shows improved generalization capabilities in few-shot classification scenarios.

The rapid expansion of large-scale text-to-image diffusion models has raised growing concerns regarding their potential misuse in creating harmful or misleading content. In this paper we introduce MACE a finetuning framework for the task of mass concept erasure. This task aims to prevent models from generating images that embody unwanted concepts when prompted. Existing concept erasure methods are typically restricted to handling fewer than five concepts simultaneously and struggle to find a balance between erasing concept synonyms (generality) and maintaining unrelated concepts (specificity). In contrast MACE differs by successfully scaling the erasure scope up to 100 concepts and by achieving an effective balance between generality and specificity. This is achieved by leveraging closed-form cross-attention refinement along with LoRA finetuning collectively eliminating the information of undesirable concepts. Furthermore MACE integrates multiple LoRAs without mutual interference. We conduct extensive evaluations of MACE against prior methods across four different tasks: object erasure celebrity erasure explicit content erasure and artistic style erasure. Our results reveal that MACE surpasses prior methods in all evaluated tasks. Code is available at https://github.com/Shilin-LU/MACE.

Deep neural networks are prone to capture correlations between spurious attributes and class labels leading to low accuracy on some combinations of class labels and spurious attribute values. When a spurious attribute represents a protected class these low-accuracy groups can manifest discriminatory bias. Existing methods attempting to improve worst-group accuracy assume the training data validation data or both are reliably labeled by the spurious attribute. But a model may be perceived to be biased towards a concept that is not represented by pre-existing labels on the training data. In these situations the spurious attribute must be defined with external information. We propose Concept Correction a framework that represents a concept as a curated set of images from any source then labels each training sample by its similarity to the concept set to control spurious correlations. For example concept sets representing gender can be used to measure and control gender bias even without explicit labels. We demonstrate and evaluate an instance of the framework as Concept DRO which uses concept sets to estimate group labels then uses these labels to train with a state of the art distributively robust optimization objective. We show that Concept DRO outperforms existing methods that do not require labels of spurious attributes by up to 33.1% on three image classification datasets and is competitive with the best methods that assume access to labels. We consider how the size and quality of the concept set influences performance and find that even smaller manually curated sets of noisy AI-generated images are effective at controlling spurious correlations suggesting that high-quality reusable concept sets are easy to create and effective in reducing bias.

In this paper we propose a novel concept factorization method that seeks factor matrices using a cross-order positive semi-definite neighbor graph which provides comprehensive and complementary neighbor information of the data. The factor matrices are learned with bipartite graph partitioning which exploits explicit cluster structure of the data and is more geared towards clustering application. We develop an effective and efficient optimization algorithm for our method and provide elegant theoretical results about the convergence. Extensive experimental results confirm the effectiveness of the proposed method.

Addressing biases in computer vision models is crucial for real-world AI deployments. However mitigating visual biases is challenging due to their unexplainable nature often identified indirectly through visualization or sample statistics which necessitates additional human supervision for interpretation. To tackle this issue we propose the Bias-to-Text (B2T) framework which interprets visual biases as keywords. Specifically we extract common keywords from the captions of mispredicted images to identify potential biases in the model. We then validate these keywords by measuring their similarity to the mispredicted images using a vision-language scoring model. The keyword explanation form of visual bias offers several advantages such as a clear group naming for bias discovery and a natural extension for debiasing using these group names. Our experiments demonstrate that B2T can identify known biases such as gender bias in CelebA background bias in Waterbirds and distribution shifts in ImageNet-R/C. Additionally B2T uncovers novel biases in larger datasets such as Dollar Street and ImageNet. For example we discovered a contextual bias between \keyword bee and \keyword flower in ImageNet. We also highlight various applications of B2T keywords including debiased training CLIP prompting and model comparison.

Co-salient object detection (CoSOD) aims to identify the common and salient (usually in the foreground) regions across a given group of images. Although achieving significant progress state-of-the-art CoSODs could be easily affected by some adversarial perturbations leading to substantial accuracy reduction. The adversarial perturbations can mislead CoSODs but do not change the high-level semantic information (e.g. concept) of the co-salient objects. In this paper we propose a novel robustness enhancement framework by first learning the concept of the co-salient objects based on the input group images and then leveraging this concept to purify adversarial perturbations which are subsequently fed to CoSODs for robustness enhancement. Specifically we propose CosalPure containing two modules i.e. group-image concept learning and concept-guided diffusion purification. For the first module we adopt a pre-trained text-to-image diffusion model to learn the concept of co-salient objects within group images where the learned concept is robust to adversarial examples. For the second module we map the adversarial image to the latent space and then perform diffusion generation by embedding the learned concept into the noise prediction function as an extra condition. Our method can effectively alleviate the influence of the SOTA adversarial attack containing different adversarial patterns including exposure and noise. The extensive results demonstrate that our method could enhance the robustness of CoSODs significantly.

While recent Vision-Language (VL) models excel at open-vocabulary tasks it is unclear how to use them with specific or uncommon concepts. Personalized Text-to-Image Retrieval (TIR) or Generation (TIG) are recently introduced tasks that represent this challenge where the VL model has to learn a concept from few images and respectively discriminate or generate images of the target concept in arbitrary contexts. We identify the ability to learn new meanings and their compositionality with known ones as two key properties of a personalized system. We show that the available benchmarks offer a limited validation of personalized textual concept learning from images with respect to the above properties and introduce ConCon-Chi as a benchmark for both personalized TIR and TIG designed to fill this gap. We modelled the new-meaning concepts by crafting chimeric objects and formulating a large varied set of contexts where we photographed each object. To promote the compositionality assessment of the learned concepts with known contexts we combined different contexts with the same concept and vice-versa. We carry out a thorough evaluation of state-of-the-art methods on the resulting dataset. Our study suggests that future work on personalized TIR and TIG methods should focus on the above key properties and we propose principles and a dataset for their performance assessment. Dataset: https://doi.org/10.48557/QJ1166 and code: https://github.com/hsp-iit/concon-chi_benchmark.

In order to gain insights about the decision-making of different visual recognition backbones we propose two methodologies sub-explanation counting and cross-testing that systematically applies deep explanation algorithms on a dataset-wide basis and compares the statistics generated from the amount and nature of the explanations. These methodologies reveal the difference among networks in terms of two properties called compositionality and disjunctivism. Transformers and ConvNeXt are found to be more compositional in the sense that they jointly consider multiple parts of the image in building their decisions whereas traditional CNNs and distilled transformers are less compositional and more disjunctive which means that they use multiple diverse but smaller set of parts to achieve a confident prediction. Through further experiments we pinpointed the choice of normalization to be especially important in the compositionality of a model in that batch normalization leads to less compositionality while group and layer normalization lead to more. Finally we also analyze the features shared by different backbones and plot a landscape of different models based on their feature-use similarity.

This paper addresses the challenge of learning a local visual pattern of an object from one image and generating images depicting objects with that pattern. Learning a localized concept and placing it on an object in a target image is a nontrivial task as the objects may have different orientations and shapes. Our approach builds upon recent advancements in visual concept learning. It involves acquiring a visual concept (e.g. an ornament) from a source image and subsequently applying it to an object (e.g. a chair) in a target image. Our key idea is to perform in-context concept learning acquiring the local visual concept within the broader context of the objects they belong to. To localize the concept learning we employ soft masks that contain both the concept within the mask and the surrounding image area. We demonstrate our approach through object generation within an image showcasing plausible embedding of in-context learned concepts. We also introduce methods for directing acquired concepts to specific locations within target images employing cross-attention mechanisms and establishing correspondences between source and target objects. The effectiveness of our method is demonstrated through quantitative and qualitative experiments along with comparisons against baseline techniques.

Few-shot learning (FSL) facilitates a variety of computer vision tasks yet remains vulnerable to adversarial attacks. Existing adversarially robust FSL methods rely on either visual similarity learning or class concept learning. Our analysis reveals that these two learning paradigms are complementary exhibiting distinct robustness due to their unique decision boundary types (concepts clustering by the visual similarity label vs. classification by the class labels). To bridge this gap we propose a novel framework unifying adversarially robust similarity learning and class concept learning. Specifically we distill parameters from both network branches into a "unified embedding model" during robust optimization and redistribute them to individual network branches periodically. To capture generalizable robustness across diverse branches we initialize adversaries in each episode with cross-branch class-wise "global adversarial perturbations" instead of less informative random initialization. We also propose a branch robustness harmonization to modulate the optimization of similarity and class concept learners via their relative adversarial robustness. Extensive experiments demonstrate the state-of-the-art performance of our method in diverse few-shot scenarios.