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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

来源：评论

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Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models

Open-Vocabulary Panoptic Segmentation with Text-to-Image Dif...

引用

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

作者： Xu, Jiarui Liu, Sifei Vahdat, Arash Byeon, Wonmin Wang, Xiaolong De Meo, Shalini Univ Calif San Diego La Jolla CA 92093 USA NVIDIA Santa Clara CA USA

ISBN: (纸本)9798350301298

We present ODISE: Open-vocabulary DIffusion-based panoptic SEgmentation, which unifies pre-trained textimage diffusion and discriminative models to perform open-vocabulary panoptic segmentation. Text-to-image diffusion models have the remarkable ability to generate high-quality images with diverse open-vocabulary language descriptions. This demonstrates that their internal representation space is highly correlated with open concepts in the real world. Text-image discriminative models like CLIP, on the other hand, are good at classifying images into open-vocabulary labels. We leverage the frozen internal representations of both these models to perform panoptic segmentation of any category in the wild. Our approach outperforms the previous state of the art by significant margins on both open-vocabulary panoptic and semantic segmentation tasks. In particular, with COCO training only, our method achieves 23.4 PQ and 30.0 mIoU on the ADE20K dataset, with 8.3 PQ and 7.9 mIoU absolute improvement over the previous state of the art. We open-source our code and models at https://***/NVlabs/ODISE.

关键词： and reasoning language vision

来源：评论

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Manipulating Transfer Learning for Property Inference

Manipulating Transfer Learning for Property Inference

引用

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

作者： Tian, Yulong Suya, Fnu Suri, Anshuman Xu, Fengyuan Evans, David Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Univ Virginia Charlottesville VA USA

ISBN: (纸本)9798350301298

Transfer learning is a popular method for tuning pre-trained (upstream) models for different downstream tasks using limited data and computational resources. We study how an adversary with control over an upstream model used in transfer learning can conduct property inference attacks on a victim's tuned downstream model. For example, to infer the presence of images of a specific individual in the downstream training set. We demonstrate attacks in which an adversary can manipulate the upstream model to conduct highly effective and specific property inference attacks (AUC score > 0.9), without incurring significant performance loss on the main task. The main idea of the manipulation is to make the upstream model generate activations (intermediate features) with different distributions for samples with and without a target property, thus enabling the adversary to distinguish easily between downstream models trained with and without training examples that have the target property. Our code is available at https:// ***/yulongt23/Transfer-Inference.

关键词： accountability ethics in vision fairness privacy Transparency

来源：评论

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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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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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Blur Interpolation Transformer for Real-World Motion from Blur

Blur Interpolation Transformer for Real-World Motion from Bl...

引用

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

作者： Zhong, Zhihang Cao, Mingdeng Ji, Xiang Zheng, Yinqiang Sato, Imari Univ Tokyo Tokyo Japan Natl Inst Informat Tokyo Japan

ISBN: (纸本)9798350301298

This paper studies the challenging problem of recovering motion from blur, also known as joint deblurring and interpolation or blur temporal super-resolution. The challenges are twofold: 1) the current methods still leave considerable room for improvement in terms of visual quality even on the synthetic dataset, and 2) poor generalization to real-world data. To this end, we propose a blur interpolation transformer (BiT) to effectively unravel the underlying temporal correlation encoded in blur. Based on multi-scale residual Swin transformer blocks, we introduce dual-end temporal supervision and temporally symmetric ensembling strategies to generate effective features for time-varying motion rendering. In addition, we design a hybrid camera system to collect the first real-world dataset of one-to-many blur-sharp video pairs. Experimental results show that BiT has a significant gain over the state-of-the-art methods on the public dataset Adobe240. Besides, the proposed real-world dataset effectively helps the model generalize well to real blurry scenarios. Code and data are available at https://***/zzh-tech/BiT.

关键词： Low-level vision

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An Empirical Study of End-to-End Video-Language Transformers with Masked Visual Modeling

An Empirical Study of End-to-End Video-Language Transformers...

引用

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

作者： Fu, Tsu-Jui Li, Linjie Gan, Zhe Lin, Kevin Wang, William Yang Wang, Lijuan Liu, Zicheng UC Santa Barbara Santa Barbara CA 93106 USA Microsoft Redmond WA USA

ISBN: (纸本)9798350301298

Masked visual modeling (MVM) has been recently proven effective for visual pre-training. While similar re-constructive objectives on video inputs (e.g., masked frame modeling) have been explored in video-language (VidL) pre-training, previous studies fail to find a truly effective MVM strategy that can largely benefit the downstream performance. In this work, we systematically examine the potential of MVM in the context of VidL learning. Specifically, we base our study on a fully end-to-end VIdeO-LanguagE Transformer (VIOLET) [15], where the supervision from MVM training can be backpropogated to the video pixel space. In total, eight different reconstructive targets of MVM are explored, from low-level pixel values and oriented gradients to high-level depth maps, optical flow, discrete visual tokens and latent visual features. We conduct comprehensive experiments and provide insights into the factors leading to effective MVM training, resulting in an enhanced model VIOLETv2. Empirically, we show VIOLETv2 pre-trained with MVM objective achieves notable improvements on 13 VidL benchmarks, ranging from video question answering, video captioning, to text-to-video retrieval.(1)

关键词： and reasoning language vision

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STDLens: Model Hijacking-resilient Federated Learning for Object Detection

STDLens: Model Hijacking-resilient Federated Learning for Ob...

引用

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

作者： Chow, Ka-Ho Liu, Ling Wei, Wenqi Ilhan, Fatih Wu, Yanzhao Georgia Instutite Technol Atlanta GA 30332 USA

ISBN: (纸本)9798350301298

Federated Learning (FL) has been gaining popularity as a collaborative learning framework to train deep learning-based object detection models over a distributed population of clients. Despite its advantages, FL is vulnerable to model hijacking. The attacker can control how the object detection system should misbehave by implanting Trojaned gradients using only a small number of compromised clients in the collaborative learning process. This paper introduces STDLens, a principled approach to safeguarding FL against such attacks. We first investigate existing mitigation mechanisms and analyze their failures caused by the inherent errors in spatial clustering analysis on gradients. Based on the insights, we introduce a three-tier forensic framework to identify and expel Trojaned gradients and reclaim the performance over the course of FL. We consider three types of adaptive attacks and demonstrate the robustness of STDLens against advanced adversaries. Extensive experiments show that STDLens can protect FL against different model hijacking attacks and outperform existing methods in identifying and removing Trojaned gradients with significantly higher precision and much lower false-positive rates. The source code is available at https: //***/git-disl/STDLens.

关键词： accountability ethics in vision fairness privacy Transparency

来源：评论

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Normalizing Flow based Feature Synthesis for Outlier-Aware Object Detection

Normalizing Flow based Feature Synthesis for Outlier-Aware O...

引用

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

作者： Kumar, Nishant Segvic, Sinisa Eslami, Abouzar Gumhold, Stefan Tech Univ Dresden Dresden Germany Univ Zagreb FER Zagreb Croatia Carl Zeiss Meditec AG Jena Germany

ISBN: (纸本)9798350301298

Real-world deployment of reliable object detectors is crucial for applications such as autonomous driving. However, general-purpose object detectors like Faster R-CNN are prone to providing overconfident predictions for outlier objects. Recent outlier-aware object detection approaches estimate the density of instance-wide features with class-conditional Gaussians and train on synthesized outlier features from their low-likelihood regions. However, this strategy does not guarantee that the synthesized outlier features will have a low likelihood according to the other class-conditional Gaussians. We propose a novel outlier-aware object detection framework that distinguishes outliers from inlier objects by learning the joint data distribution of all inlier classes with an invertible normalizing flow. The appropriate sampling of the flow model ensures that the synthesized outliers have a lower likelihood than inliers of all object classes, thereby modeling a better decision boundary between inlier and outlier objects. Our approach significantly outperforms the state-of-the-art for outlier-aware object detection on both image and video datasets.

关键词： detection recognition: Categorization retrieval

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DualVector: Unsupervised Vector Font Synthesis with Dual-Part Representation

DualVector: Unsupervised Vector Font Synthesis with Dual-Par...

引用

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

作者： Liu, Ying-Tian Zhang, Zhifei Guo, Yuan-Chen Fisher, Matthew Wang, Zhaowen Zhang, Song-Hai Tsinghua Univ Dept Comp Sci & Technol BNRist Beijing Peoples R China Adobe Res San Francisco CA USA

ISBN: (纸本)9798350301298

Automatic generation of fonts can be an important aid to typeface design. Many current approaches regard glyphs as pixelated images, which present artifacts when scaling and inevitable quality losses after vectorization. On the other hand, existing vector font synthesis methods either fail to represent the shape concisely or require vector supervision during training. To push the quality of vector font synthesis to the next level, we propose a novel dual-part representation for vector glyphs, where each glyph is modeled as a collection of closed "positive" and "negative" path pairs. The glyph contour is then obtained by boolean operations on these paths. We first learn such a representation only from glyph images and devise a subsequent contour refinement step to align the contour with an image representation to further enhance details. Our method, named DualVector, outperforms state-of-the-art methods in vector font synthesis both quantitatively and qualitatively. Our synthesized vector fonts can be easily converted to common digital font formats like TrueType Font for practical use. The code is released at https://***/thuliu-yt16/dualvector.

关键词： vision + graphics

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