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Semantic-Aware Multi-Label Adversarial Attacks

Semantic-Aware Multi-Label Adversarial Attacks

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Mahmood, Hassan El Hamifar, Ehsan Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9798350353006

Despite its importance, generating attacks for multi-label learning (MLL) models has received much less attention compared to multi-class recognition. Attacking an MLL model by optimizing a loss on the target set of labels has often the undesired consequence of changing the predictions for other labels. On the other hand, adding a loss on the remaining labels to keep them fixed leads to highly negatively correlated gradient directions, reducing the attack effectiveness. In this paper, we develop a framework for crafting effective and semantic-aware adversarial attacks for MLL. First, to obtain an attack that leads to semantically consistent predictions across all labels, we find a minimal superset of the target labels, referred to as consistent target set. To do so, we develop an efficient search algorithm over a knowledge graph, which encodes label dependencies. Next, we propose an optimization that searches for an attack that modifies the predictions of labels in the consistent target set while ensuring other labels will not get affected. This leads to an efficient algorithm that projects the gradient of the consistent target set loss onto the orthogonal direction of the gradient of the loss on other labels. Our framework can generate attacks on different target set sizes and for MLL with thousands of labels (as in OpenImages). Finally, by extensive experiments on three datasets and several MLL models, we show that our method generates both successful and semantically consistent attacks.(1)

关键词： Adversarial Attacks Adversarial Robustness Multi-label Attacks Multi-label recognition Semantic Attacks

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Progressive Semantic-Guided vision Transformer for Zero-Shot Learning

Progressive Semantic-Guided Vision Transformer for Zero-Shot...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Chen, Shiming Hou, Wenjin Khan, Salman Khan, Fahad Shahbaz Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates Huazhong Univ Sci & Technol Wuhan Peoples R China Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden

ISBN: (纸本)9798350353006

Zero-shot learning (ZSL) recognizes the unseen classes by conducting visual-semantic interactions to transfer semantic knowledge from seen classes to unseen ones, supported by semantic information (e.g., attributes). However, existing ZSL methods simply extract visual features using a pre-trained network backbone (i.e., CNN or ViT), which fail to learn matched visual-semantic correspondences for representing semantic-related visual features as lacking of the guidance of semantic information, resulting in undesirable visual-semantic interactions. To tackle this issue, we propose a progressive semantic-guided vision transformer for zero-shot learning (dubbed ZSLViT). ZSLViT mainly considers two properties in the whole network: i) discover the semantic-related visual representations explicitly, and ii) discard the semantic-unrelated visual information. Specifically, we first introduce semantic-embedded token learning to improve the visual-semantic correspondences via semantic enhancement and discover the semantic-related visual tokens explicitly with semantic-guided token attention. Then, we fuse low semantic-visual correspondence visual tokens to discard the semantic-unrelated visual information for visual enhancement. These two operations are integrated into various encoders to progressively learn semantic-related visual representations for accurate visual-semantic interactions in ZSL. The extensive experiments show that our ZSLViT achieves significant performance gains on three popular benchmark datasets, i.e., CUB, SUN, and AWA2.

关键词： zero-shot learning

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Using Language-Aligned Gesture Embeddings for Understanding Gestures Accompanying Math Terms

Using Language-Aligned Gesture Embeddings for Understanding ...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Maidment, Tristan Patel, Purav J. Walker, Erin Kovashka, Adriana Univ Pittsburgh Intelligent Syst Program Pittsburgh PA 15260 USA Univ Pittsburgh Comp Sci Pittsburgh PA 15260 USA Univ Pittsburgh Learning Res & Dev Ctr Pittsburgh PA 15260 USA Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9798350365474

In this paper, we introduce an approach for recognizing and classifying gestures that accompany mathematical terms, in a new collection we name the "GAMT" dataset. Our method uses language as a means of providing context to classify gestures. Specifically, we use a CLIP-style framework to construct a shared embedding space for gestures and language, experimenting with various methods for encoding gestures within this space. We evaluate our method on our new dataset containing a wide array of gestures associated with mathematical terms. The shared embedding space leads to a substantial improvement in gesture classification. Furthermore, we identify an efficient model that excelled at classifying gestures from our unique dataset, thus contributing to the further development of gesture recognition in diverse interaction scenarios.

关键词： action clip cnn comunication dataset embeddings gesture hci language-aligned multimodal non-verbal recognition semantic tcn transformers

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Classifier Guided Cluster Density Reduction for Dataset Selection

Classifier Guided Cluster Density Reduction for Dataset Sele...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Chang, Cheng Long, Keyu Li, Zijian Rai, Himanshu Layer 6 AI Toronto ON Canada

ISBN: (纸本)9798350365474

In this paper, we address the challenge of selecting an optimal dataset from a source pool with annotations to enhance performance on a target dataset derived from a different source. This is important in scenarios where it is hard to afford on-the-fly dataset annotation and is also the theme of the second Visual Data Understanding (VDU) Challenge. Our solution, the Classifier Guided Cluster Density Reduction (CCDR) framework, operates in two stages. Initially, we employ a filtering technique to identify images that align with the target dataset's distribution. Subsequently, we implement a graph-based cluster density reduction method, steered by a classifier that approximates the distance between the target distribution and source distribution. This classifier is trained to distinguish between images that resemble the target dataset and those that do not, facilitating the pruning process shown in Figure 1. Our approach maintains a balance between selecting pertinent images that match the target distribution and eliminating redundant ones that do not contribute to the enhancement of the detection model. We demonstrate the superiority of our method over various baselines in object detection tasks, particularly in optimizing the training set distribution on the region100 dataset. We have released our code here: https://***/ himsR/DataCVChallenge-2024/tree/main

关键词： computer vision Data Search deep learning domain Transfer

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Gesture recognition: Focus on the Hands 31

Gesture Recognition: Focus on the Hands

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31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Narayana, Pradyumna Beveridge, J. Ross Draper, Bruce A. Colorado State Univ Ft Collins CO 80523 USA

ISBN: (纸本)9781538664209

Gestures are a common form of human communication and important for human computer interfaces (HCI). Recent approaches to gesture recognition use deep learning methods, including multi-channel methods. We show that when spatial channels are focused on the hands, gesture recognition improves significantly, particularly when the channels are fused using a sparse network. Using this technique, we improve performance on the ChaLearn IsoGD dataset from a previous best of 67.71% to 82.07%, and on the NVIDIA dataset from 83.8% to 91.28%.

关键词： Gesture recognition Training Network architecture Feature extraction Optical imaging Human computer interaction computer vision

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Anchor-based Robust Finetuning of vision-Language Models

Anchor-based Robust Finetuning of Vision-Language Models

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Han, Jinwei Lin, Zhiwen Sun, Zhongyisun Gao, Yingguo Yan, Ke Ding, Shouhong Gao, Yuan Xia, Gui-Song Wuhan Univ Sch Comp Sci Wuhan Peoples R China Tencent YouTu Lab Shenzhen Peoples R China Wuhan Univ Elect Informat Sch Wuhan Peoples R China

ISBN: (纸本)9798350353006

We aim at finetuning a vision-language model without hurting its out-of-distribution (OOD) generalization. We address two types of OOD generalization, i.e., i) domain shift such as natural to sketch images, and ii) zero-shot capability to recognize the category that was not contained in the finetune data. Arguably, the diminished OOD generalization after finetuning stems from the excessively simplified finetuning target, which only provides the class information, such as "a photo of a [CLASS]". This is distinct from the process in that CLIP was pretrained, where there is abundant text supervision with rich semantic information. Therefore, we propose to compensate for the finetune process using auxiliary supervision with rich semantic information, which acts as anchors to preserve the OOD generalization. Specifically, two types of anchors are elaborated in our method, including i) text-compensated anchor which uses the images from the finetune set but enriches the text supervision from a pretrained captioner, ii) image-text-pair anchor which is retrieved from the dataset similar to pre-training data of CLIP according to the downstream task, associating with the original CLIP text with rich semantics. Those anchors are utilized as auxiliary semantic information to maintain the original feature space of CLIP, thereby preserving the OOD generalization capabilities. Comprehensive experiments demonstrate that our method achieves in-distribution performance akin to conventional finetuning while attaining new state-of-the-art results on domain shift and zero-shot learning benchmarks.

关键词： Anchor Robust Finetuning

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RegionPLC: Regional Point-Language Contrastive Learning for Open-World 3D Scene Understanding

RegionPLC: Regional Point-Language Contrastive Learning for ...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Yang, Jihan Ding, Runyu Deng, Weipeng Wang, Zhe Qi, Xiaojuan Univ Hong Kong Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9798350353006

We propose a lightweight and scalable Regional Point-Language Contrastive learning framework, namely RegionPLC, for open-world 3D scene understanding, aiming to identify and recognize open-set objects and categories. Specifically, based on our empirical studies, we introduce a 3D-aware SFusion strategy that fuses 3D vision-language pairs derived from multiple 2D foundation models, yielding high-quality, dense region-level language descriptions without human 3D annotations. Subsequently, we devise a region-aware point-discriminative contrastive learning objective to enable robust and effective 3D learning from dense regional language supervision. We carry out extensive experiments on ScanNet, ScanNet200, and nuScenes datasets, and our model outperforms prior 3D open-world scene understanding approaches by an average of 17.2% and 9.1% for semantic and instance segmentation, respectively, while maintaining greater scalability and lower resource demands. Furthermore, our method has the flexibility to be effortlessly integrated with language models to enable open-ended grounded 3D reasoning without extra task-specific training. Code will be released at github.

关键词： 3D vision embodied AI Open-world

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HyperLeaf2024-A Hyperspectral Imaging Dataset for Classification and Regression of Wheat Leaves

HyperLeaf2024-A Hyperspectral Imaging Dataset for Classifica...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Laprade, William Michael Pieta, Pawel Kutuzova, Svetlana Westergaard, Jesper Cairo Nielsen, Mads Christensen, Svend Dahl, Anders Bjorholm Tech Univ Denmark Dept Appl Math & Comp Sci Copenhagen Denmark Univ Copenhagen Dept Comp Sci Copenhagen Denmark Univ Copenhagen Dept Plant & Environm Sci Copenhagen Denmark

ISBN: (纸本)9798350365474

Hyperspectral imaging is a widely used method in remote sensing, particularly for use in airborne and satellitebased land surveillance. Its versatility is, however, much larger and has also seen usage in everything ranging from food processing and surveillance to astronomy and waste sorting. It is also gaining inroads with agricultural research. With most available datasets focusing on perpixel classification, there is, however, a potential for hyperspectral whole-image analysis, but there is a severe lack of datasets for whole-image analysis. To help fill this gap and facilitate methodological development in wholeimage hyperspectral image analysis, we introduce the HyperLeaf2024 dataset. The dataset consists of 2410 hyperspectral images of wheat leaves, along with associated classification and regression targets at both the leaf level and the plot level. In addition to the dataset, we also provide experiments showing the importance of pretraining and highlighting the future research direction in whole-image hyperspectral image analysis.

关键词： dataset deep learning hyperspectral imaging plant science

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Initialization Noise in Image Gradients and Saliency Maps

Initialization Noise in Image Gradients and Saliency Maps

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Woerl, Ann-Christin Disselhoff, Jan Wand, Michael Johannes Gutenberg Univ Mainz Inst Comp Sci Mainz Germany

ISBN: (纸本)9798350301298

In this paper, we examine gradients of logits of image classification CNNs by input pixel values. We observe that these fluctuate considerably with training randomness, such as the random initialization of the networks. We extend our study to gradients of intermediate layers, obtained via GradCAM, as well as popular network saliency estimators such as DeepLIFT, SHAP, LIME, Integrated Gradients, and SmoothGrad. While empirical noise levels vary, qualitatively different attributions to image features are still possible with all of these, which comes with implications for interpreting such attributions, in particular when seeking data-driven explanations of the phenomenon generating the data. Finally, we demonstrate that the observed artefacts can be removed by marginalization over the initialization distribution by simple stochastic integration.

关键词： Explainable computer vision

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Leveraging Pre-trained Multi-task Deep Models for Trustworthy Facial Analysis in Affective Behaviour Analysis in-the-Wild

Leveraging Pre-trained Multi-task Deep Models for Trustworth...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Saychenko, Andrey V. r Sber AI Lab Moscow Russia HSE Univ Lab Algorithms & Technol Network Anal Nizhnii Novgorod Russia

ISBN: (纸本)9798350365474

This article presents our results for the sixth Affective Behavior Analysis in-the-wild (ABAW) competition. To improve the trustworthiness of facial analysis, we study the possibility of using pre-trained deep models that extract reliable emotional features without the need to fine-tune the neural networks for a downstream task. In particular, we introduce several lightweight models based on MobileViT, MobileFaceNet, EfficientNet, and DDAMFN architectures trained in multi-task scenarios to recognize facial expressions, valence, and arousal on static photos. These neural networks extract frame-level features fed into a simple classifier, e.g., linear feed-forward neural network, to predict emotion intensity, compound expressions, and valence/arousal. Experimental results for three tasks from the sixth ABAW challenge demonstrate that our approach lets us significantly improve quality metrics on validation sets compared to existing non-ensemble techniques. As a result, our solutions took second place in the compound expression recognition competition.

关键词： Compound facial expressition recognition Emotional Mimicry Intensity estimation HSEmotion library lightweight neural networks Valence-Arousal prediction

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