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Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillation

Constructing Deep Spiking Neural Networks from Artificial Ne...

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conference on computer vision and pattern recognition (CVPR)

作者： Qi Xu Yaxin Li Jiangrong Shen Jian K. Liu Huajin Tang Gang Pan School of Artificial Intelligence Dalin University of Technology College of Computer Science and Technology Zhejiang University School of Computing University of Leeds

Spiking neural networks (SNNs) are well-known as brain-inspired models with high computing efficiency, due to a key component that they utilize spikes as information units, close to the biological neural systems. Although spiking based models are energy efficient by taking advantage of discrete spike signals, their performance is limited by current network structures and their training methods. As discrete signals, typical SNNs cannot apply the gradient descent rules directly into parameter adjustment as artificial neural networks (ANNs). Aiming at this limitation, here we propose a novel method of constructing deep SNN models with knowledge distillation (KD) that uses ANN as the teacher model and SNN as the student model. Through the ANN-SNN joint training algorithm, the student SNN model can learn rich feature information from the teacher ANN model through the KD method, yet it avoids training SNN from scratch when communicating with non-differentiable spikes. Our method can not only build a more efficient deep spiking structure feasibly and reasonably but use few time steps to train the whole model compared to direct training or ANN to SNN methods. More importantly, it has a superb ability of noise immunity for various types of artificial noises and natural signals. The proposed novel method provides efficient ways to improve the performance of SNN through constructing deeper structures in a high-throughput fashion, with potential usage for light and efficient brain-inspired computing of practical scenarios.

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Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising

Spatially Adaptive Self-Supervised Learning for Real-World I...

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conference on computer vision and pattern recognition (CVPR)

作者： Junyi Li Zhilu Zhang Xiaoyu Liu Chaoyu Feng Xiaotao Wang Lei Lei Wangmeng Zuo School of Computer Science and Technology Harbin Institute of Technology China Peng Cheng Laboratory China

Significant progress has been made in self-supervised image denoising (SSID) in the recent few years. However, most methods focus on dealing with spatially independent noise, and they have little practicality on real-world sRGB images with spatially correlated noise. Although pixel-shuffle downsampling has been suggested for breaking the noise correlation, it breaks the original information of images, which limits the denoising performance. In this paper, we propose a novel perspective to solve this problem, i.e., seeking for spatially adaptive supervision for real-world sRGB image denoising. Specifically, we take into account the respective characteristics of flat and textured regions in noisy images, and construct supervisions for them separately. For flat areas, the supervision can be safely derived from non-adjacent pixels, which are much far from the current pixel for excluding the influence of the noise-correlated ones. And we extend the blind-spot network to a blind-neighborhood network (BNN) for providing supervision on flat areas. For textured regions, the supervision has to be closely related to the content of adjacent pixels. And we present a locally aware network (LAN) to meet the requirement, while LAN itself is selectively supervised with the output of BNN. Combining these two supervisions, a denoising network (e.g., U-Net) can be well-trained. Extensive experiments show that our method performs favorably against state-of-the-art SSID methods on real-world sRGB photographs. The code is available at https://***/nagejacob/SpatiallyAdaptiveSSID.

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Localized Semantic Feature Mixers for Efficient Pedestrian Detection in Autonomous Driving

Localized Semantic Feature Mixers for Efficient Pedestrian D...

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conference on computer vision and pattern recognition (CVPR)

作者： Abdul Hannan Khan Mohammed Shariq Nawaz Andreas Dengel Department of Computer Science RPTU Kaiserslautern-Landau German Research Center for Artificial Intelligence (DFKI GmbH) Kaiserslautern Germany

Autonomous driving systems rely heavily on the underlying perception module which needs to be both performant and efficient to allow precise decisions in realtime. Avoiding collisions with pedestrians is of topmost priority in any autonomous driving system. Therefore, pedestrian detection is one of the core parts of such systems' perception modules. Current state-of-the-art pedestrian detectors have two major issues. Firstly, they have long inference times which affect the efficiency of the whole perception module, and secondly, their performance in the case of small and heavily occluded pedestrians is poor: We propose Local-ized Semantic Feature Mixers (LSFM), a novel, anchor-free pedestrian detection architecture. It uses our novel Super Pixel Pyramid Pooling module instead of the, computation-ally costly, Feature Pyramid Networks for feature encoding. Moreover, our MLPMixer-based Dense Focal Detection Network is used as a light detection head, reducing computational effort and inference time compared to existing approaches. To boost the performance of the proposed architecture, we adapt and use mixup augmentation which improves the performance, especially in small and heavily occluded cases. We benchmark LSFM against the state-of-the-art on well-established traffic scene pedestrian datasets. The proposed LSFM achieves state-of-the-art performance in Caltech, City Persons, Euro City Persons, and TJU-Traffic-Pedestrian datasets while reducing the inference time on average by 55%. Further, LSFM beats the human baseline for the first time in the history of pedestrian detection. Finally, we conducted a cross-dataset evaluation which proved that our proposed LSFM generalizes well to unseen data.

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ScarceNet: Animal Pose Estimation with Scarce Annotations

ScarceNet: Animal Pose Estimation with Scarce Annotations

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conference on computer vision and pattern recognition (CVPR)

作者： Chen Li Gim Hee Lee Department of Computer Science National University of Singapore

Animal pose estimation is an important but underexplored task due to the lack of labeled data. In this paper, we tackle the task of animal pose estimation with scarce annotations, where only a small set of labeled data and unlabeled images are available. At the core of the solution to this problem setting is the use of the unlabeled data to compensate for the lack of well-labeled animal pose data. To this end, we propose the ScarceNet, a pseudo label-based approach to generate artificial labels for the unlabeled images. The pseudo labels, which are generated with a model trained with the small set of labeled images, are generally noisy and can hurt the performance when directly used for training. To solve this problem, we first use a small-loss trick to select reliable pseudo labels. Although effective, the selection process is improvident since numerous high-loss samples are left unused. We further propose to identify reusable samples from the high-loss samples based on an agreement check. Pseudo labels are re-generated to provide supervision for those reusable samples. Lastly, we introduce a student-teacher framework to enforce a consistency constraint since there are still samples that are neither reliable nor reusable. By combining the reliable pseudo label selection with the reusable sample re-labeling and the consistency constraint, we can make full use of the unlabeled data. We evaluate our approach on the challenging AP-10K dataset, where our approach outperforms existing semi-supervised approaches by a large margin. We also test on the TigDog dataset, where our approach can achieve better performance than domain adaptation based approaches when only very few annotations are available. Our code is available at the project website 1 1 https://***/chaneyddtt/ScarceNet.

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Physically Realizable Natural-Looking Clothing Textures Evade Person Detectors via 3D Modeling

Physically Realizable Natural-Looking Clothing Textures Evad...

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conference on computer vision and pattern recognition (CVPR)

作者： Zhanhao Hu Wenda Chu Xiaopei Zhu Hui Zhang Bo Zhang Xiaolin Hu Department of Computer Science and Technology Tsinghua University Beijing China Institute for Interdisciplinary Information Sciences Tsinghua University Beijing China School of Integrated Circuits Tsinghua University Beijing China Beijing Institute of Fashion Technology Beijing China IDG/McGovern Institute for Brain Research THBI Tsinghua University Beijing China Chinese Institute for Brain Research (CIBR) Beijing China

Recent works have proposed to craft adversarial clothes for evading person detectors, while they are either only effective at limited viewing angles or very conspicuous to humans. We aim to craft adversarial texture for clothes based on 3D modeling, an idea that has been used to craft rigid adversarial objects such as a 3D-printed turtle. Unlike rigid objects, humans and clothes are non-rigid, leading to difficulties in physical realization. In order to craft natural-looking adversarial clothes that can evade person detectors at multiple viewing angles, we propose adversarial camou-flage textures (AdvCaT) that resemble one kind of the typical textures of daily clothes, camouflage textures. We leverage the Voronoi diagram and Gumbel-softmax trick to parameterize the camouflage textures and optimize the parameters via 3D modeling. Moreover, we propose an efficient augmentation pipeline on 3D meshes combining topologically plausible projection (TopoProj) and Thin Plate Spline (TPS) to narrow the gap between digital and real-world objects. We printed the developed 3D texture pieces on fabric materials and tailored them into T-shirts and trousers. Experiments show high attack success rates of these clothes against multiple detectors.

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On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering

On the Effects of Self-supervision and Contrastive Alignment...

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conference on computer vision and pattern recognition (CVPR)

作者： Daniel J. Trosten Sigurd Løkse Robert Jenssen Michael C. Kampffmeyer Department of Physics and Technology UiT The Arctic University of Norway UiT Machine Learning group Visual Intelligence Centre Norwegian Computing Center Department of Computer Science University of Copenhagen Pioneer Centre for AI

Self-supervised learning is a central component in recent approaches to deep multi-view clustering (MVC). However, we find large variations in the development of self-supervision-based methods for deep MVC, potentially slowing the progress of the field. To address this, we present Deep-MVC, a unified framework for deep MVC that includes many recent methods as instances. We leverage our framework to make key observations about the effect of self-supervision, and in particular, drawbacks of aligning representations with contrastive learning. Further, we prove that contrastive alignment can negatively influence cluster separability, and that this effect becomes worse when the number of views increases. Motivated by our findings, we develop several new DeepMVC instances with new forms of self-supervision. We conduct extensive experiments and find that (i) in line with our theoretical findings, contrastive alignments decreases performance on datasets with many views; (ii) all methods benefit from some form of self-supervision; and (iii) our new instances outperform previous methods on several datasets. Based on our results, we suggest several promising directions for future research. To enhance the openness of the field, we provide an open-source implementation of DeepMVC, including recent models and our new instances. Our implementation includes a consistent evaluation protocol, facilitating fair and accurate evaluation of methods and components 1 1 Code: https://***/DanielTrosten/DeepMVC.

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Bi-directional Feature Fusion Generative Adversarial Network for Ultra-high Resolution Pathological Image Virtual Re-staining

Bi-directional Feature Fusion Generative Adversarial Network...

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conference on computer vision and pattern recognition (CVPR)

作者： Kexin Sun Zhineng Chen Gongwei Wang Jun Liu Xiongjun Ye Yu-Gang Jiang Collaborative Innovation Center of Intelligent Visual Computing School of Computer Science & Shanghai Fudan University Shanghai Qi Zhi Institute Peking University People's Hospital Department of Urology National Cancer Center & National Clinical Research Center for Cancer Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College

The cost of pathological examination makes virtual restaining of pathological images meaningful. However, due to the ultra-high resolution of pathological images, traditional virtual restaining methods have to divide a WSI image into patches for model training and inference. Such a limitation leads to the lack of global information, resulting in observable differences in color, brightness and contrast when the re-stained patches are merged to generate an image of larger size. We summarize this issue as the square effect. Some existing methods try to solve this issue through overlapping between patches or simple postprocessing. But the former one is not that effective, while the latter one requires carefully tuning. In order to eliminate the square effect, we design a bidirectional feature fusion generative adversarial network (BFF-GAN) with a global branch and a local branch. It learns the inter-patch connections through the fusion of global and local features plus patch-wise attention. We perform experiments on both the private dataset RCC and the public dataset ANHIR. The results show that our model achieves competitive performance and is able to generate extremely real images that are deceptive even for experienced pathologists, which means it is of great clinical significance.

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StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning

StyleAdv: Meta Style Adversarial Training for Cross-Domain F...

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conference on computer vision and pattern recognition (CVPR)

作者： Yuqian Fu Yu Xie Yanwei Fu Yu-Gang Jiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Purple Mountain Laboratories Nanjing China School of Data Science Fudan University

Cross-Domain Few-Shot Learning (CD-FSL) is a recently emerging task that tackles few-shot learning across different domains. It aims at transferring prior knowledge learned on the source dataset to novel target datasets. The CD-FSL task is especially challenged by the huge domain gap between different datasets. Critically, such a domain gap actually comes from the changes of visual styles, and wave-SAN [10] empirically shows that spanning the style distribution of the source data helps alleviate this issue. However, wave-SAN simply swaps styles of two images. Such a vanilla operation makes the generated styles “real” and “easy”, which still fall into the original set of the source styles. Thus, inspired by vanilla adversarial learning, a novel model-agnostic meta Style Adversarial training (StyleAdv) method together with a novel style adversarial attack method is proposed for CD-FSL. Particularly, our style attack method synthesizes both “virtual” and “hard” adversarial styles for model training. This is achieved by perturbing the original style with the signed style gradients. By continually attacking styles and forcing the model to recognize these challenging adversarial styles, our model is gradually robust to the visual styles, thus boosting the generalization ability for novel target datasets. Besides the typical CNN-based backbone, we also employ our StyleAdv method on large-scale pre-trained vision transformer. Extensive experiments conducted on eight various target datasets show the effectiveness of our method. Whether built upon ResNet or ViT, we achieve the new state of the art for CD-FSL. Code is available at https://***/lovelyqian/StyleAdv-CDFSL.

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Deep Hashing with Minimal-Distance-Separated Hash Centers

Deep Hashing with Minimal-Distance-Separated Hash Centers

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conference on computer vision and pattern recognition (CVPR)

作者： Liangdao Wang Yan Pan Cong Liu Hanjiang Lai Jian Yin Ye Liu School of Computer Science and Engineering Sun Yat-Sen University Big Data Department Lizhi Inc.

Deep hashing is an appealing approach for large-scale image retrieval. Most existing supervised deep hashing methods learn hash functions using pairwise or triple image similarities in randomly sampled mini-batches. They suffer from low training efficiency, insufficient coverage of data distribution, and pair imbalance problems. Recently, central similarity quantization (CSQ) attacks the above problems by using “hash centers” as a global similarity metric, which encourages the hash codes of similar images to approach their common hash center and distance themselves from other hash centers. Although achieving SOTA retrieval performance, CSQ falls short of a worst-case guarantee on the minimal distance between its constructed hash centers, i.e. the hash centers can be arbitrarily close. This paper presents an optimization method that finds hash centers with a constraint on the minimal distance between any pair of hash centers, which is non-trivial due to the non-convex nature of the problem. More importantly, we adopt the Gilbert-Varshamov bound from coding theory, which helps us to obtain a large minimal distance while ensuring the empirical feasibility of our optimization approach. With these clearly-separated hash centers, each is assigned to one image class, we propose several effective loss functions to train deep hashing networks. Extensive experiments on three datasets for image retrieval demonstrate that the proposed method achieves superior retrieval performance over the state-of-the-art deep hashing methods.

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Detecting and Grounding Multi-Modal Media Manipulation

Detecting and Grounding Multi-Modal Media Manipulation

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conference on computer vision and pattern recognition (CVPR)

作者： Rui Shao Tianxing Wu Ziwei Liu School of Computer Science and Technology Harbin Institute of Technology (Shenzhen) S-Lab Nanyang Technological University

Misinformation has become a pressing issue. Fake media, in both visual and textual forms, is widespread on the web. While various deepfake detection and text fake news detection methods have been proposed, they are only designed for single-modality forgery based on binary classification, let alone analyzing and reasoning subtle forgery traces across different modalities. In this paper, we high-light a new research problem for multi-modal fake media, namely Detecting and Grounding Multi-Modal Media Manipulation (DGM 4 ). DGM 4 aims to not only detect the authenticity of multi-modal media, but also ground the manipulated content (i.e., image bounding boxes and text tokens), which requires deeper reasoning of multi-modal media manipulation. To support a large-scale investigation, we construct the first DGM 4 dataset, where image-text pairs are manipulated by various approaches, with rich annotation of diverse manipulations. Moreover, we propose a novel HierArchical Multi-modal Manipulation rEasoning tRansformer (HAMMER) to fully capture the fine-grained interaction between different modalities. HAMMER performs 1) manipulation-aware contrastive learning between two uni-modal encoders as shallow manipulation reasoning, and 2) modality-aware cross-attention by multi-modal aggregator as deep manipulation reasoning. Dedicated manipulation detection and grounding heads are integrated from shallow to deep levels based on the interacted multi-modal information. Finally, we build an extensive bench-mark and set up rigorous evaluation metrics for this new research problem. Comprehensive experiments demonstrate the superiority of our model; several valuable observations are also revealed to facilitate future research in multi-modal media manipulation.

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