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CondenseNet V2: Sparse Feature Reactivation for Deep Networks

CondenseNet V2: Sparse Feature Reactivation for Deep Network...

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

作者： Yang, Le Jiang, Haojun Cai, Ruojin Wang, Yulin Song, Shiji Huang, Gao Tian, Qi Tsinghua Univ Dept Automat Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China Cornell Univ Ithaca NY 14853 USA Huawei Cloud & AI Ithaca NY USA

ISBN: (纸本)9781665445092

Reusing features in deep networks through dense connectivity is an effective way to achieve high computational efficiency. The recent proposed CondenseNet [14] has shown that this mechanism can befiirther improved if redundant features are removed. In this paper, we propose an alternative approach named sparse feature reactivation (SFR), aiming at actively increasing the utility of features for reusing. In the proposed network, named CondenseNetV2, each layer can simultaneously learn to 1) selectively reuse a set of most important features from preceding layers;and 2) actively update a set of preceding features to increase their utility for later layers. Our experiments show that the proposed models achieve promising performance on image classification (ImageNet and CIFAR) and object detection (MS COCO) in terms of both theoretical efficiency and practical speed.

关键词： Training computer vision Computational modeling Object detection computer architecture Search problems pattern recognition

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Animating General Image with Large Visual Motion Model

Animating General Image with Large Visual Motion Model

引用

conference on computer vision and pattern recognition (cvpr)

作者： Dengsheng Chen Xiaoming Wei Xiaolin Wei Meituan Beijing China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present the pioneering Large Visual Motion Model (LVMM), meticulously engineered to analyze the intrinsic dynamics encapsulated within real-world imagery. Our model, fortified with a wealth of prior knowledge extracted from billions of image pairs, demonstrates promising results in predicting a diverse spectrum of scene dynamics. As a result, it can infuse any generic image with authentic dynamic effects, enhancing its visual allure. Project page: https://***/densechen/LVMM.

关键词： Visualization computer vision Analytical models Computational modeling Dynamics Predictive models pattern recognition

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2PCNet: Two-Phase Consistency Training for Day-to-Night Unsupervised Domain Adaptive Object Detection

2PCNet: Two-Phase Consistency Training for Day-to-Night Unsu...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Mikhail Kennerley Jian-Gang Wang Bharadwaj Veeravalli Robby T. Tan Department of Electrical and Computer Engineering National University of Singapore Institute for Infocomm Research A*STAR

Object detection at night is a challenging problem due to the absence of night image annotations. Despite several domain adaptation methods, achieving high-precision results remains an issue. False-positive error propagation is still observed in methods using the well-established student-teacher framework, particularly for small-scale and low-light objects. This paper proposes a two-phase consistency unsupervised domain adaptation network, 2PCNet, to address these issues. The network employs high-confidence bounding-box predictions from the teacher in the first phase and appends them to the student's region proposals for the teacher to re-evaluate in the second phase, resulting in a combination of high and low confidence pseudolabels. The night images and pseudo-labels are scaled-down before being used as input to the student, providing stronger small-scale pseudo-labels. To address errors that arise from low-light regions and other night-related attributes in images, we propose a night-specific augmentation pipeline called NightAug. This pipeline involves applying random augmentations, such as glare, blur, and noise, to daytime images. Experiments on publicly available datasets demonstrate that our method achieves superior results to state-of-the-art methods by 20%, and to supervised models trained directly on the target data. 1 1 ***/mecarill/2pcnet

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LayoutDiffusion: Controllable Diffusion Model for Layout-to-Image Generation

LayoutDiffusion: Controllable Diffusion Model for Layout-to-...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Guangcong Zheng Xianpan Zhou Xuewei Li Zhongang Qi Ying Shan Xi Li College of Computer Science & Technology Zhejiang University Polytechnic Institute Zhejiang University ARC Lab Tencent PCG Shanghai Institute for Advanced Study of Zhejiang University Shanghai AI Lab Zhejiang-Singapore Innovation and AI Joint Research Lab

Recently, diffusion models have achieved great success in image synthesis. However, when it comes to the layout-to-image generation where an image often has a complex scene of multiple objects, how to make strong control over both the global layout map and each detailed object remains a challenging task. In this paper, we propose a diffusion model named LayoutDiffusion that can obtain higher generation quality and greater controllability than the previous works. To overcome the difficult multimodal fusion of image and layout, we propose to construct a structural image patch with region information and transform the patched image into a special layout to fuse with the normal layout in a unified form. Moreover, Layout Fusion Module (LFM) and Object-aware Cross Attention (OaCA) are proposed to model the relationship among multiple objects and designed to be object-aware and position-sensitive, allowing for precisely controlling the spatial related information. Extensive experiments show that our LayoutDiffusion out-performs the previous SOTA methods on FID, CAS by relatively 46.35%,26.70% on COCO-stuff and 44.29%,41.82% on VG. Code is available at https://***/ZGCTroy/LayoutDiffusion.

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Dynamic Coarse-to-Fine Learning for Oriented Tiny Object Detection

Dynamic Coarse-to-Fine Learning for Oriented Tiny Object Det...

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conference on computer vision and pattern recognition (cvpr)

作者： Chang Xu Jian Ding Jinwang Wang Wen Yang Huai Yu Lei Yu Gui-Song Xia School of Electronic Information Wuhan University School of Computer Science Wuhan University

Detecting arbitrarily oriented tiny objects poses intense challenges to existing detectors, especially for label assignment. Despite the exploration of adaptive label assignment in recent oriented object detectors, the extreme geometry shape and limited feature of oriented tiny objects still induce severe mismatch and imbalance issues. Specifically, the position prior, positive sample feature, and instance are mismatched, and the learning of extreme-shaped objects is biased and unbalanced due to little proper feature supervision. To tackle these issues, we propose a dynamic prior along with the coarse-to-fine assigner, dubbed DCFL. For one thing, we model the prior, label assignment, and object representation all in a dynamic manner to alleviate the mismatch issue. For another, we leverage the coarse prior matching and finer posterior constraint to dynamically assign labels, providing appropriate and relatively balanced supervision for diverse instances. Extensive experiments on six datasets show substantial improvements to the baseline. Notably, we obtain the state-of-the-art performance for one-stage detectors on the DOTA-v1.5, DOTA-v2.0, and DIOR-R datasets under single-scale training and testing. Codes are available at https://***/Chasel-Tsui/mmrotate-dcfl.

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Visualizing Adapted Knowledge in Domain Transfer

Visualizing Adapted Knowledge in Domain Transfer

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

作者： Hou, Yunzhong Zheng, Liang Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9781665445092

A source model trained on source data and a target model learned through unsupervised domain adaptation (UDA) usually encode different knowledge. To understand the adaptation process, we portray their knowledge difference with image translation. Specifically, we feed a translated image and its original version to the two models respectively, formulating two branches. Through updating the translated image, we force similar outputs from the two branches. When such requirements are met, differences between the two images can compensate for and hence represent the knowledge difference between models. To enforce similar outputs from the two branches and depict the adapted knowledge, we propose a source-free image translation method that generates source-style images using only target images and the two models. We visualize the adapted knowledge on several datasets with different UDA methods and find that generated images successfully capture the style difference between the two domains. For application, we show that generated images enable further tuning of the target model without accessing source data.

关键词： Adaptation models computer vision Computational modeling Force Data visualization Data models pattern recognition

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FACESEC: A Fine-grained Robustness Evaluation Framework for Face recognition Systems

FACESEC: A Fine-grained Robustness Evaluation Framework for ...

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

作者： Tong, Liang Chen, Zhengzhang Ni, Jingchao Cheng, Wei Song, Dongjin Chen, Haifeng Vorobeychik, Yevgeniy Washington Univ St Louis MO 14263 USA NEC Labs Amer Princeton NJ 08540 USA Univ Connecticut Storrs CT USA

ISBN: (纸本)9781665445092

We present FACESEC, a framework for fine-grained robustness evaluation of face recognition systems. FACESEC evaluation is performed along four dimensions of adversarial modeling: the nature of perturbation (e.g., pixel-level or face accessories), the attacker's system knowledge (about training data and learning architecture), goals (dodging or impersonation), and capability (tailored to individual inputs or across sets of these). We use FACESEC to study five face recognition systems in both closed-set and open-set settings, and to evaluate the state-of-the-art approach for defending against physically realizable attacks on these. We find that accurate knowledge of neural architecture is significantly more important than knowledge of the training data in black-box attacks. Moreover, we observe that open-set face recognition systems are more vulnerable than closed-set systems under different types of attacks. The efficacy of attacks for other threat model variations, however, appears highly dependent on both the nature of perturbation and the neural network architecture. For example, attacks that involve adversarial face masks are usually more potent, even against adversarially trained models, and the ArcFace architecture tends to be more robust than the others.

关键词： computer vision Systematics Face recognition Perturbation methods Neural networks Training data computer architecture

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Progressive Semantic Segmentation

Progressive Semantic Segmentation

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

作者： Chuong Huynh Anh Tuan Tran Khoa Luu Minh Hoai VinAI Res Hanoi Vietnam VinUniversity Hanoi Vietnam Univ Arkansas Fayetteville AR 72701 USA SUNY Stony Brook Stony Brook NY 11790 USA

ISBN: (纸本)9781665445092

The objective of this work is to segment high-resolution images without overloading GPU memory usage or losing the fine details in the output segmentation map. The memory constraint means that we must either downsample the big image or divide the image into local patches for separate processing. However, the former approach would lose the fine details, while the latter can be ambiguous due to the lack of a global picture. In this work, we present MagNet, a multi-scale framework that resolves local ambiguity by looking at the image at multiple magnification levels. MagNet has multiple processing stages, where each stage corresponds to a magnification level, and the output of one stage is fed into the next stage for coarse-to-fine information propagation. Each stage analyzes the image at a higher resolution than the previous stage, recovering the previously lost details due to the lossy downsampling step, and the segmentation output is progressively refined through the processing stages. Experiments on three high-resolution datasets of urban views, aerial scenes, and medical images show that MagNet consistently outperforms the state-of-the-art methods by a significant margin. Code is available at https://***/VinAIResearch/MagNet.

关键词： Image segmentation computer vision Image resolution Codes Magnetic separation Magnetic resonance imaging Semantics

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AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles

AdvSim: Generating Safety-Critical Scenarios for Self-Drivin...

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

作者： Wang, Jingkang Pun, Ava Tu, James Manivasagam, Sivabalan Sadat, Abbas Casas, Sergio Ren, Mengye Urtasun, Raquel Univ Toronto Toronto ON Canada Uber ATG Pittsburgh PA 15201 USA Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9781665445092

As self-driving systems become better, simulating scenarios where the autonomy stack may fail becomes more important. Traditionally, those scenarios are generated for a few scenes with respect to the planning module that takes ground-truth actor states as input. This does not scale and cannot identify all possible autonomy failures, such as perception failures due to occlusion. In this paper, we propose AdvSim, an adversarial framework to generate safetycritical scenarios for any LiDAR-based autonomy system. Given an initial traffic scenario, AdvSim modifies the actors' trajectories in a physically plausible manner and updates the LiDAR sensor data to match the perturbed world. Importantly, by simulating directly from sensor data, we obtain adversarial scenarios that are safety-critical for the full autonomy stack. Our experiments show that our approach is general and can identify thousands of semantically meaningful safety-critical scenarios for a wide range of modern self-driving systems. Furthermore, we show that the robustness and safety of these systems can be further improved by training them with scenarios generated by AdvSim.

关键词： Training computer vision Laser radar Robustness Safety Trajectory Planning

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SelfAugment: Automatic Augmentation Policies for Self-Supervised Learning

SelfAugment: Automatic Augmentation Policies for Self-Superv...

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

作者： Reed, Colorado J. Metzger, Sean Srinivas, Aravind Darrell, Trevor Keutzer, Kurt Univ Calif Berkeley BAIR Dept Comp Sci Berkeley CA 94720 USA Weill Neurosci Inst Grad Grp Bioengn Berkeley UCSF San Francisco CA USA UCSF Neurol Surg San Francisco CA USA

ISBN: (纸本)9781665445092

A common practice in unsupervised representation learning is to use labeled data to evaluate the quality of the learned representations. This supervised evaluation is then used to guide critical aspects of the training process such as selecting the data augmentation policy. However, guiding an unsupervised training process through supervised evaluations is not possible for real-world data that does not actually contain labels (which may be the case, for example, in privacy sensitive fields such as medical imaging). Therefore, in this work we show that evaluating the learned representations with a self-supervised image rotation task is highly correlated with a standard set of supervised evaluations (rank correlation > 0.94). We establish this correlation across hundreds of augmentation policies, training settings, and network architectures and provide an algorithm (SelfAugment) to automatically and efficiently select augmentation policies without using supervised evaluations. Despite not using any labeled data, the learned augmentation policies perform comparably with augmentation policies that were determined using exhaustive supervised evaluations.

关键词： Training computer vision Data privacy Correlation Image recognition Network architecture pattern recognition

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