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Dynamic Generative Targeted Attacks with pattern Injection

Dynamic Generative Targeted Attacks with Pattern Injection

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conference on computer vision and pattern recognition (CVPR)

作者： Weiwei Feng Nanqing Xu Tianzhu Zhang Yongdong Zhang University of Science and Technology of China Deep Space Exploration Lab

Adversarial attacks can evaluate model robustness and have been of great concern in recent years. Among various attacks, targeted attacks aim at misleading victim models to output adversary-desired predictions, which are more challenging and threatening than untargeted ones. Existing targeted attacks can be roughly divided into instance-specific and instance-agnostic attacks. Instance-specific attacks craft adversarial examples via iterative gradient updating on the specific instance. In contrast, instance-agnostic attacks learn a universal perturbation or a generative model on the global dataset to perform attacks. However, they rely too much on the classification boundary of substitute models, ignoring the realistic distribution of the target class, which may result in limited targeted attack performance. And there is no attempt to simultaneously combine the information of the specific instance and the global dataset. To deal with these limitations, we first conduct an analysis via a causal graph and propose to craft transferable targeted adversarial examples by injecting target patterns. Based on this analysis, we introduce a generative attack model composed of a cross-attention guided convolution module and a pattern injection module. Concretely, the former adopts a dynamic convolution kernel and a static convolution kernel for the specific instance and the global dataset, respectively, which can inherit the advantages of both instance-specific and instance-agnostic attacks. And the pattern injection module utilizes a pattern prototype to encode target patterns, which can guide the generation of targeted adversa rial examples. Besides, we also provide rigorous theoretical analysis to guarantee the effectiveness of our method. Extensive experiments demonstrate that our method shows superior performance than 10 existing adversarial attacks against 13 models.

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Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields

Exact-NeRF: An Exploration of a Precise Volumetric Parameter...

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conference on computer vision and pattern recognition (CVPR)

作者： Brian K. S. Isaac-Medina Chris G. Willcocks Toby P. Breckon Department of Computer Science Durham University UK Department of Engineering Durham University UK

Neural Radiance Fields (NeRF) have attracted significant attention due to their ability to synthesize novel scene views with great accuracy. However, inherent to their underlying formulation, the sampling of points along a ray with zero width may result in ambiguous representations that lead to further rendering artifacts such as aliasing in the final scene. To address this issue, the recent variant mipNeRF proposes an Integrated Positional Encoding (IPE) based on a conical view frustum. Although this is expressed with an integral formulation, mip-NeRF instead approximates this integral as the expected value of a multivariate Gaussian distribution. This approximation is reliable for short frustums but degrades with highly elongated regions, which arises when dealing with distant scene objects under a larger depth of field. In this paper, we explore the use of an exact approach for calculating the IPE by using a pyramid-based integral formulation instead of an approximated conical-based one. We denote this formulation as Exact-NeRF and contribute the first approach to offer a precise analytical solution to the IPE within the NeRF domain. Our exploratory work illustrates that such an exact formulation (Exact-NeRF) matches the accuracy of mip-NeRF and furthermore provides a natural extension to more challenging scenarios without further modification, such as in the case of unbounded scenes. Our contribution aims to both address the hitherto unexplored issues of frustum approximation in earlier NeRF work and additionally provide insight into the potential future consideration of analytical solutions in future NeRF extensions.

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Curricular Contrastive Regularization for Physics-Aware Single Image Dehazing

Curricular Contrastive Regularization for Physics-Aware Sing...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yu Zheng Jiahui Zhan Shengfeng He Junyu Dong Yong Du College of Computer Science and Technology Ocean University of China School of Computing and Information Systems Singapore Management University

Considering the ill-posed nature, contrastive regularization has been developed for single image dehazing, introducing the information from negative images as a lower bound. However, the contrastive samples are non-consensual, as the negatives are usually represented distantly from the clear (i.e., positive) image, leaving the solution space still under-constricted. Moreover, the interpretability of deep dehazing models is underexplored towards the physics of the hazing process. In this paper, we propose a novel curricular contrastive regularization targeted at a consensual contrastive space as opposed to a non-consensual one. Our negatives, which provide better lower-bound constraints, can be assembled from 1) the hazy image, and 2) corresponding restorations by other existing methods. Further, due to the different similarities between the embeddings of the clear image and negatives, the learning difficulty of the multiple components is intrinsically imbalanced. To tackle this issue, we customize a curriculum learning strategy to reweight the importance of different negatives. In addition, to improve the interpretability in the feature space, we build a physics-aware dual-branch unit according to the atmospheric scattering model. With the unit, as well as curricular contrastive regularization, we establish our dehazing network, named C 2 PNet. Extensive experiments demonstrate that our C 2 PNet significantly outperforms state-of-the-art methods, with extreme PSNR boosts of 3.94dB and 1.50dB, respectively, on SOTS-indoor and SOTS-outdoor datasets. Code is available at https://***/YuZheng9/C2PNet.

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Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning

Out-of-Distributed Semantic Pruning for Robust Semi-Supervis...

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conference on computer vision and pattern recognition (CVPR)

作者： Yu Wang Pengchong Qiao Chang Liu Guoli Song Xiawu Zheng Jie Chen School of Electronic and Computer Engineering Peking University Shenzhen China AI for Science(AI4S)-Preferred Program Peking University Shenzhen Graduate School China Peng Cheng Laboratory Shenzhen China Department of Automation and BNRist Tsinghua University Beijing China

Recent advances in robust semi-supervised learning (SSL) typically filter out-of-distribution (OOD) information at the sample level. We argue that an overlooked problem of robust SSL is its corrupted information on semantic level, practically limiting the development of the field. In this paper, we take an initial step to explore and propose a unified framework termed OOD Semantic Pruning (OSP), which aims at pruning OOD semantics out from in-distribution (ID) features. Specifically, (i) we propose an aliasing OOD matching module to pair each ID sample with an OOD sample with semantic overlap. (ii) We design a soft orthogonality regularization, which first transforms each ID feature by suppressing its semantic component that is collinear with paired OOD sample. It then forces the predictions before and after soft orthogonality decomposition to be consistent. Being practically simple, our method shows a strong performance in OOD detection and ID classification on challenging benchmarks. In particular, OSP surpasses the previous state-of-the-art by 13.7% on accuracy for ID classification and 5.9% on AUROC for OOD detection on TinyImageNet dataset. The source codes are publicly available at https://***/rain305f/OSP.

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Hard Sample Matters a Lot in Zero-Shot Quantization

Hard Sample Matters a Lot in Zero-Shot Quantization

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conference on computer vision and pattern recognition (CVPR)

作者： Huantong Li Xiangmiao Wu Fanbing Lv Daihai Liao Thomas H. Li Yonggang Zhang Bo Han Mingkui Tan South China University of Technology Information Technology R&D Innovation Center of Peking University Changsha Hisense Intelligent System Research Institute Co. Ltd School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen China Hong Kong Baptist University Key Laboratory of Big Data and Intelligent Robot Ministry of Education PengCheng Laboratory

Zero-shot quantization (ZSQ) is promising for compressing and accelerating deep neural networks when the data for training full-precision models are inaccessible. In ZSQ, network quantization is performed using synthetic samples, thus, the performance of quantized models depends heavily on the quality of synthetic samples. Nonetheless, we find that the synthetic samples constructed in existing ZSQ methods can be easily fitted by models. Accordingly, quantized models obtained by these methods suffer from significant performance degradation on hard samples. To address this issue, we propose HArd sample Synthesizing and Training (HAST). Specifically, HAST pays more attention to hard samples when synthesizing samples and makes synthetic samples hard to fit when training quantized models. HAST aligns features extracted by full-precision and quantized models to ensure the similarity between features extracted by these two models. Extensive experiments show that HAST significantly outperforms existing ZSQ methods, achieving performance comparable to models that are quantized with real data.

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Combining Implicit-Explicit View Correlation for Light Field Semantic Segmentation

Combining Implicit-Explicit View Correlation for Light Field...

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conference on computer vision and pattern recognition (CVPR)

作者： Ruixuan Cong Da Yang Rongshan Chen Sizhe Wang Zhenglong Cui Hao Sheng State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beihang Hangzhou Innovation Institute Yuhang Faculty of Applied Sciences Macao Polytechnic University

Since light field simultaneously records spatial information and angular information of light rays, it is considered to be beneficial for many potential applications, and semantic segmentation is one of them. The regular variation of image information across views facilitates a comprehensive scene understanding. However, in the case of limited memory, the high-dimensional property of light field makes the problem more intractable than generic semantic segmentation, manifested in the difficulty of fully exploiting the relationships among views while maintaining contextual information in single view. In this paper, we propose a novel network called LF-IENet for light field semantic segmentation. It contains two different manners to mine complementary information from surrounding views to segment central view. One is implicit feature integration that leverages attention mechanism to compute inter-view and intra-view similarity to modulate features of central view. The other is explicit feature propagation that directly warps features of other views to central view under the guidance of disparity. They complement each other and jointly realize complementary information fusion across views in light field. The proposed method achieves outperforming performance on both real-world and synthetic light field datasets, demonstrating the effectiveness of this new architecture.

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Boost vision Transformer with GPU-Friendly Sparsity and Quantization

Boost Vision Transformer with GPU-Friendly Sparsity and Quan...

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conference on computer vision and pattern recognition (CVPR)

作者： Chong Yu Tao Chen Zhongxue Gan Jiayuan Fan Academy for Engineering and Technology Fudan University NVIDIA Corporation School for Information Science and Technology Fudan University

The transformer extends its success from the language to the vision domain. Because of the stacked self-attention and cross-attention blocks, the acceleration deployment of vision transformer on GPU hardware is challenging and also rarely studied. This paper thoroughly designs a compression scheme to maximally utilize the GPU-friendly 2:4 fine-grained structured sparsity and quantization. Specially, an original large model with dense weight parameters is first pruned into a sparse one by 2:4 structured pruning, which considers the GPU's acceleration of 2:4 structured sparse pattern with FP16 data type, then the floating-point sparse model is further quantized into a fixed-point one by sparse-distillation-aware quantization aware training, which considers GPU can provide an extra speedup of 2:4 sparse calculation with integer tensors. A mixed-strategy knowledge distillation is used during the pruning and quantization process. The proposed compression scheme is flexible to support supervised and unsupervised learning styles. Experiment results show GPUSQ-ViT scheme achieves state-of-the-art compression by reducing vision transformer models $\mathbf{6.4}-\mathbf{12.7}\times$ on model size and $\mathbf{30.3}-\mathbf{62} \times$ on FLOPs with negligible accuracy degradation on ImageNet classification, COCO detection and ADE20K segmentation benchmarking tasks. Moreover, GPUSQ-ViT can boost actual deployment performance by $\mathbf{1.39}-\mathbf{1.79}\times$ and $\mathbf{3.22}-\mathbf{3.43}\times$ of latency and throughput on A100 GPU, and $\mathbf{1.57}-\mathbf{1.69}\times$ and $\mathbf{2.11}-\mathbf{2.51}\times$ improvement of latency and throughput on AGX Orin.

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Multi-Centroid Task Descriptor for Dynamic Class Incremental Inference

Multi-Centroid Task Descriptor for Dynamic Class Incremental...

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conference on computer vision and pattern recognition (CVPR)

作者： Tenghao Cai Zhizhong Zhang Xin Tan Yanyun Qu Guannan Jiang Chengjie Wang Yuan Xie School of Computer Science and Technology East China Normal University Shanghai China School of Informatics Xiamen University Fujian China CATL China Tencent Youtu Lab

Incremental learning could be roughly divided into two categories, i.e., class- and task-incremental learning. The main difference is whether the task ID is given during evaluation. In this paper, we show this task information is indeed a strong prior knowledge, which will bring significant improvement over class-incremental learning baseline, e.g., DER [39]. Based on this observation, we propose a gate network to predict the task ID for class incremental inference. This is challenging as there is no explicit semantic relationship between categories in the concept of task. Therefore, we propose a multi-centroid task descriptor by assuming the data within a task can form multiple clusters. The cluster centers are optimized by pulling relevant sample-centroid pairs while pushing others away, which ensures that there is at least one centroid close to a given sample. To select relevant pairs, we use class prototypes as proxies and solve a bipartite matching problem, making the task descriptor representative yet not degenerate to uni-modal. As a result, our dynamic inference network is trained independently of baseline and provides a flexible, efficient solution to distinguish between tasks. Extensive experiments show our approach achieves state-of-the-art results, e.g., we achieve 72.41% average accuracy on CIFAR100-BOS50, outperforming DER by 3.40%.

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MISC210K: A Large-Scale Dataset for Multi-Instance Semantic Correspondence

MISC210K: A Large-Scale Dataset for Multi-Instance Semantic ...

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conference on computer vision and pattern recognition (CVPR)

作者： Yixuan Sun Yiwen Huang Haijing Guo Yuzhou Zhao Runmin Wu Yizhou Yu Weifeng Ge Wenqiang Zhang Academy of Engineering & Technology Fudan University Shanghai China School of Computer Science Fudan University Shanghai China The University of Hong Kong Hong Kong China

Semantic correspondence have built up a new way for object recognition. However current single-object matching schema can be hard for discovering commonalities for a category and far from the real-world recognition tasks. To fill this gap, we design the multi-instance semantic correspondence task which aims at constructing the correspondence between multiple objects in an image pair. To support this task, we build a multi-instance semantic correspondence (MISC) dataset from COCO Detection 2017 task called MISC210K. We construct our dataset as three steps: (1) category selection and data cleaning; (2) keypoint design based on 3D models and object description rules; (3) human-machine collaborative annotation. Following these steps, we select 34 classes of objects with 4,812 challenging images annotated via a well designed semi-automatic workflow, and finally acquire 218,179 image pairs with instance masks and instance-level keypoint pairs annotated. We design a dual-path collaborative learning pipeline to train instance-level co-segmentation task and fine-grained level correspondence task together. Benchmark evaluation and further ablation results with detailed analysis are provided with three future directions proposed. Our project is available on https://***/YXSUNMADMAX/MISC210K.

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Null-text Inversion for Editing Real Images using Guided Diffusion Models

Null-text Inversion for Editing Real Images using Guided Dif...

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conference on computer vision and pattern recognition (CVPR)

作者： Ron Mokady Amir Hertz Kfir Aberman Yael Pritch Daniel Cohen-Or Google Research The Blavatnik School of Computer Science Tel Aviv University

Recent large-scale text-guided diffusion models provide powerful image generation capabilities. Currently, a massive effort is given to enable the modification of these images using text only as means to offer intuitive and versatile editing tools. To edit a real image using these state-of-the-art tools, one must first invert the image with a meaningful text prompt into the pretrained model's domain. In this paper, we introduce an accurate inversion technique and thus facilitate an intuitive text-based modification of the image. Our proposed inversion consists of two key novel components: (i) Pivotal inversion for diffusion models. While current methods aim at mapping random noise samples to a single input image, we use a single pivotal noise vector for each timestamp and optimize around it. We demonstrate that a direct DDIM inversion is inadequate on its own, but does provide a rather good anchor for our optimization. (ii) Null-text optimization, where we only modify the unconditional textual embedding that is used for classifier-free guidance, rather than the input text embedding. This allows for keeping both the model weights and the conditional embedding intact and hence enables applying prompt-based editing while avoiding the cumbersome tuning of the model's weights. Our null-text inversion, based on the publicly available Stable Diffusion model, is extensively evaluated on a variety of images and various prompt editing, showing high-fidelity editing of real images.

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