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Robust Single Image Reflection Removal Against Adversarial Attacks

Robust Single Image Reflection Removal Against Adversarial A...

引用

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

作者： Song, Zhenbo Zhang, Zhenyuan Zhang, Kaihao Luo, Wenhan Fan, Zhaoxin Ren, Wenqi Lu, Jianfeng Nanjing Univ Sci & Technol Nanjing Peoples R China Australian Natl Univ Canberra ACT Australia Sun Yat Sen Univ Shenzhen Campus Shenzhen Peoples R China Renmin Univ China Beijing Peoples R China

ISBN: (纸本)9798350301298

This paper addresses the problem of robust deep single-image reflection removal (SIRR) against adversarial attacks. Current deep learning based SIRR methods have shown significant performance degradation due to unnoticeable distortions and perturbations on input images. For a comprehensive robustness study, we first conduct diverse adversarial attacks specifically for the SIRR problem, i.e. towards different attacking targets and regions. Then we propose a robust SIRR model, which integrates the cross-scale attention module, the multi-scale fusion module, and the adversarial image discriminator. By exploiting the multi-scale mechanism, the model narrows the gap between features from clean and adversarial images. The image discriminator adaptively distinguishes clean or noisy inputs, and thus further gains reliable robustness. Extensive experiments on Nature, SIR2, and Real datasets demonstrate that our model remarkably improves the robustness of SIRR across disparate scenes.

关键词： Low-level vision

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Efficient and Explicit Modelling of Image Hierarchies for Image Restoration

Efficient and Explicit Modelling of Image Hierarchies for Im...

引用

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

作者： Li, Yawei Fan, Yuchen Xiang, Xiaoyu Demandolx, Denis Ranjan, Rakesh Timofte, Radu Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Meta Real Labs Menlo Pk CA 33137 USA Univ Wurzburg Wurzburg Germany Katholieke Univ Leuven Leuven Belgium

ISBN: (纸本)9798350301298

The aim of this paper is to propose a mechanism to efficiently and explicitly model image hierarchies in the global, regional, and local range for image restoration. To achieve that, we start by analyzing two important properties of natural images including cross-scale similarity and anisotropic image features. Inspired by that, we propose the anchored stripe self-attention which achieves a good balance between the space and time complexity of self-attention and the modelling capacity beyond the regional range. Then we propose a new network architecture dubbed GRL to explicitly model image hierarchies in the Global, Regional, and Local range via anchored stripe self-attention, window self-attention, and channel attention enhanced convolution. Finally, the proposed network is applied to 7 image restoration types, covering both real and synthetic settings. The proposed method sets the new state-of-the-art for several of those. Code will be available at https://***/ofsoundof/***.

关键词： Low-level vision

来源：评论

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A Practical Upper Bound for the Worst-Case Attribution Deviations

A Practical Upper Bound for the Worst-Case Attribution Devia...

引用

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

作者： Wang, Fan Kong, Adams Wai-Kin Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Nanyang Technol Univ Rapid Rich Object Search ROSE Lab IGP Singapore Singapore

ISBN: (纸本)9798350301298

Model attribution is a critical component of deep neural networks (DNNs) for its interpretability to complex models. Recent studies bring up attention to the security of attribution methods as they are vulnerable to attribution attacks that generate similar images with dramatically different attributions. Existing works have been investigating empirically improving the robustness of DNNs against those attacks;however, none of them explicitly quantifies the actual deviations of attributions. In this work, for the first time, a constrained optimization problem is formulated to derive an upper bound that measures the largest dissimilarity of attributions after the samples are perturbed by any noises within a certain region while the classification results remain the same. Based on the formulation, different practical approaches are introduced to bound the attributions above using Euclidean distance and cosine similarity under both l(2) and l(infinity)-norm perturbations constraints. The bounds developed by our theoretical study are validated on various datasets and two different types of attacks (PGD attack and IFIA attribution attack). Over 10 million attacks in the experiments indicate that the proposed upper bounds effectively quantify the robustness of models based on the worst-case attribution dissimilarities.

关键词： Explainable computer vision

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Spatial-temporal Concept based Explanation of 3D ConvNets

Spatial-temporal Concept based Explanation of 3D ConvNets

引用

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

作者： Ji, Ying Wang, Yu Kato, Jien Nagoya Univ Nagoya Aichi Japan Hitotsubashi Univ Kunitachi Tokyo Japan Ritsumeikan Univ Kyoto Japan

ISBN: (纸本)9798350301298

Convolutional neural networks (CNNs) have shown remarkable performance on various tasks. Despite its widespread adoption, the decision procedure of the network still lacks transparency and interpretability, making it difficult to enhance the performance further. Hence, there has been considerable interest in providing explanation and interpretability for CNNs over the last few years. Explainable artificial intelligence (XAI) investigates the relationship between input images or videos and output predictions. Recent studies have achieved outstanding success in explaining 2D image classification ConvNets. On the other hand, due to the high computation cost and complexity of video data, the explanation of 3D video recognition ConvNets is relatively less studied. And none of them are able to produce a high-level explanation. In this paper, we propose a STCE (Spatial-temporal Concept-based Explanation) framework for interpreting 3D ConvNets. In our approach: (1) videos are represented with high-level supervoxels, similar supervoxels are clustered as a concept, which is straightforward for human to understand;and (2) the interpreting framework calculates a score for each concept, which reflects its significance in the ConvNet decision procedure. Experiments on diverse 3D ConvNets demonstrate that our method can identify global concepts with different importance levels, allowing us to investigate the impact of the concepts on a target task, such as action recognition, in-depth. The source codes are publicly available at https://***/yingji425/STCE.

关键词： Explainable computer vision

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MEDIC: Remove Model Backdoors via Importance Driven Cloning

MEDIC: Remove Model Backdoors via Importance Driven Cloning

引用

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

作者： Xu, Qiuling Tao, Guanhong Honorio, Jean Liu, Yingqi An, Shengwei Shen, Guangyu Cheng, Siyuan Zhang, Xiangyu Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9798350301298

We develop a novel method to remove injected backdoors in deep learning models. It works by cloning the benign behaviors of a trojaned model to a new model of the same structure. It trains the clone model from scratch on a very small subset of samples and aims to minimize a cloning loss that denotes the differences between the activations of important neurons across the two models. The set of important neurons varies for each input, depending on their magnitude of activations and their impact on the classification result. We theoretically show our method can better recover benign functions of the backdoor model. Meanwhile, we prove our method can be more effective in removing backdoors compared with fine-tuning. Our experiments show that our technique can effectively remove nine different types of backdoors with minor benign accuracy degradation, outperforming the state-of-the-art backdoor removal techniques that are based on fine-tuning, knowledge distillation, and neuron pruning.(1)

关键词： ethics in vision fairness*accountability privacy Transparency

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Divide and Conquer: Answering Questions with Object Factorization and Compositional Reasoning

Divide and Conquer: Answering Questions with Object Factoriz...

引用

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

作者： Chen, Shi Zhao, Qi Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA

ISBN: (纸本)9798350301298

Humans have the innate capability to answer diverse questions, which is rooted in the natural ability to correlate different concepts based on their semantic relationships and decompose difficult problems into sub-tasks. On the contrary, existing visual reasoning methods assume training samples that capture every possible object and reasoning problem, and rely on black-boxed models that commonly exploit statistical priors. They have yet to develop the capability to address novel objects or spurious biases in real-world scenarios, and also fall short of interpreting the rationales behind their decisions. Inspired by humans' reasoning of the visual world, we tackle the aforementioned challenges from a compositional perspective, and propose an integral framework consisting of a principled object factorization method and a novel neural module network. Our factorization method decomposes objects based on their key characteristics, and automatically derives prototypes that represent a wide range of objects. With these prototypes encoding important semantics, the proposed network then correlates objects by measuring their similarity on a common semantic space and makes decisions with a compositional reasoning process. It is capable of answering questions with diverse objects regardless of their availability during training, and overcoming the issues of biased question-answer distributions. In addition to the enhanced generalizability, our framework also provides an interpretable interface for understanding the decision-making process of models. Our code is available at https://***/szzexpoi/POEM.

关键词： and reasoning language vision

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Soft Augmentation for Image Classification

Soft Augmentation for Image Classification

引用

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

作者： Liu, Yang Yan, Shen Leal-Taixe, Laura Hays, James Ramanan, Deva Argo AI Pittsburgh PA 15222 USA

ISBN: (纸本)9798350301298

Modern neural networks are over-parameterized and thus rely on strong regularization such as data augmentation and weight decay to reduce overfitting and improve generalization. The dominant form of data augmentation applies invariant transforms, where the learning target of a sample is invariant to the transform applied to that sample. We draw inspiration from human visual classification studies and propose generalizing augmentation with invariant transforms to soft augmentation where the learning target softens non-linearly as a function of the degree of the transform applied to the sample: e.g., more aggressive image crop augmentations produce less confident learning targets. We demonstrate that soft targets allow for more aggressive data augmentation, offer more robust performance boosts, work with other augmentation policies, and interestingly, produce better calibrated models (since they are trained to be less confident on aggressively cropped/occluded examples). Combined with existing aggressive augmentation strategies, soft targets 1) double the top-1 accuracy boost across Cifar-10, Cifar-100, ImageNet-1K, and ImageNet-V2, 2) improve model occlusion performance by up to 4x, and 3) half the expected calibration error (ECE). Finally, we show that soft augmentation generalizes to self-supervised classification tasks. Code available at https://***/youngleox/soft_augmentation

关键词： detection recognition: Categorization retrieval

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How can objects help action recognition?

How can objects help action recognition?

引用

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

作者： Zhou, Xingyi Arnab, Anurag Sun, Chen Schmid, Cordelia Google Res Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

Current state-of-the-art video models process a video clip as a long sequence of spatio-temporal tokens. However, they do not explicitly model objects, their interactions across the video, and instead process all the tokens in the video. In this paper, we investigate how we can use knowledge of objects to design better video models, namely to process fewer tokens and to improve recognition accuracy. This is in contrast to prior works which either drop tokens at the cost of accuracy, or increase accuracy whilst also increasing the computation required. First, we propose an object-guided token sampling strategy that enables us to retain a small fraction of the input tokens with minimal impact on accuracy. And second, we propose an object-aware attention module that enriches our feature representation with object information and improves overall accuracy. Our resulting model, ObjectViViT, achieves better performance when using fewer tokens than strong baselines. In particular, we match our baseline with 30%, 40%, and 60% of the input tokens on SomethingElse, Something-something v2, and Epic-Kitchens, respectively. When we use Object-ViViT to process the same number of tokens as our baseline, we improve by 0.6 to 4.2 points on these datasets.

关键词： Video: Action and event understanding

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Learning to Zoom and Unzoom

Learning to Zoom and Unzoom

引用

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

作者： Thavamani, Chittesh Li, Mengtian Ferroni, Francesco Ramanan, Deva Carnegie Mellon Univ Pittsburgh PA 15213 USA Argo AI Pittsburgh PA USA Waymo Mountain View CA USA Nvidia Santa Clara CA USA

ISBN: (纸本)9798350301298

Many perception systems in mobile computing, autonomous navigation, and AR/VR face strict compute constraints that are particularly challenging for high-resolution input images. Previous works propose nonuniform downsamplers that "learn to zoom" on salient image regions, reducing compute while retaining task-relevant image information. However, for tasks with spatial labels (such as 2D/3D object detection and semantic segmentation), such distortions may harm performance. In this work (LZU), we "learn to zoom" in on the input image, compute spatial features, and then "unzoom" to revert any deformations. To enable efficient and differentiable unzooming, we approximate the zooming warp with a piecewise bilinear mapping that is invertible. LZU can be applied to any task with 2D spatial input and any model with 2D spatial features, and we demonstrate this versatility by evaluating on a variety of tasks and datasets: object detection on Argoverse-HD, semantic segmentation on Cityscapes, and monocular 3D object detection on nuScenes. Interestingly, we observe boosts in performance even when high-resolution sensor data is unavailable, implying that LZU can be used to "learn to upsample" as well. Code and additional visuals are available at https://***/lzu/.

关键词： Efficient and scalable vision

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REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory

REVEAL: Retrieval-Augmented Visual-Language Pre-Training wit...

引用

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

作者： Hu, Ziniu Iscen, Ahmet Sun, Chen Wang, Zirui Chang, Kai-Wei Sun, Yizhou Schmid, Cordelia Ross, David A. Fathi, Alireza Univ Calif Los Angeles Los Angeles CA 90024 USA Google Res Mountain View CA USA

ISBN: (纸本)9798350301298

In this paper, we propose an end-to-end Retrieval-Augmented Visual Language Model (REVEAL) that learns to encode world knowledge into a large-scale memory, and to retrieve from it to answer knowledge-intensive queries. REVEAL consists of four key components: the memory, the encoder, the retriever and the generator. The large-scale memory encodes various sources of multimodal world knowledge (e.g. image-text pairs, question answering pairs, knowledge graph triplets, etc.) via a unified encoder. The retriever finds the most relevant knowledge entries in the memory, and the generator fuses the retrieved knowledge with the input query to produce the output. A key novelty in our approach is that the memory, encoder, retriever and generator are all pre-trained end-to-end on a massive amount of data. Furthermore, our approach can use a diverse set of multimodal knowledge sources, which is shown to result in significant gains. We show that REVEAL achieves state-of-the-art results on visual question answering and image captioning. The project page of this work is ***.

关键词： and reasoning language vision

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