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NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for vision Transformers

NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Qua...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yijiang Liu Huanrui Yang Zhen Dong Kurt Keutzer Li Du Shanghang Zhang Nanjing University University of California Berkeley National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University

The complicated architecture and high training cost of vision transformers urge the exploration of post-training quantization. However, the heavy-tailed distribution of vision transformer activations hinders the effectiveness of previous post-training quantization methods, even with advanced quantizer designs. Instead of tuning the quantizer to better fit the complicated activation distribution, this paper proposes NoisyQuant, a quantizer-agnostic enhancement for the post-training activation quantization performance of vision transformers. We make a surprising theoretical discovery that for a given quantizer, adding a fixed Uniform noisy bias to the values being quantized can significantly reduce the quantization error under provable conditions. Building on the theoretical insight, NoisyQuant achieves the first success on actively altering the heavy-tailed activation distribution with additive noisy bias to fit a given quantizer. Extensive experiments show NoisyQuant largely improves the post-training quantization performance of vision transformer with minimal computation overhead. For instance, on linear uniform 6-bit activation quantization, NoisyQuant improves SOTA top-1 accuracy on ImageNet by up to 1.7%, 1.1% and 0.5% for ViT, DeiT, and Swin Transformer respectively, achieving on-par or even higher performance than previous nonlinear, mixed-precision quantization.

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Privacy-Preserving Image Features via Adversarial Affine Subspace Embeddings

Privacy-Preserving Image Features via Adversarial Affine Sub...

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

作者： Dusmanu, Mihai Schoenberger, Johannes L. Sinha, Sudipta N. Pollefeys, Marc Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Microsoft Menlo Pk CA USA

ISBN: (纸本)9781665445092

Many computer vision systems require users to upload image features to the cloud for processing and storage. These features can be exploited to recover sensitive information about the scene or subjects, e.g., by reconstructing the appearance of the original image. To address this privacy concern, we propose a new privacy-preserving feature representation. The core idea of our work is to drop constraints from each feature descriptor by embedding it within an affine subspace containing the original feature as well as adversarial feature samples. Feature matching on the privacy-preserving representation is enabled based on the notion of subspace-to-subspace distance. We experimentally demonstrate the effectiveness of our method and its high practical relevance for the applications of visual localization and mapping as well as face authentication. Compared to the original features, our approach makes it significantly more difficult for an adversary to recover private information.

关键词： Location awareness Visualization computer vision Privacy Face recognition Authentication Rendering (computer graphics)

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Learning to Warp for Style Transfer

Learning to Warp for Style Transfer

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

作者： Liu, Xiao-Chang Yang, Yong-Liang Hall, Peter Univ Bath Bath Avon England

ISBN: (纸本)9781665445092

Since its inception in 2015, Style Transfer has focused on texturing a content image using an art exemplar. Recently, the geometric changes that artists make have been acknowledged as an important component of style [42, 55, 62, 63]. Our contribution is to propose a neural network that, uniquely, learns a mapping from a 4D array of inter-feature distances to a non parametric 2D warp field. The system is generic in not being limited by semantic class, a single learned model will suffice;all examples in this paper are output from one model. Our approach combines the benefits of the high speed of Liu et al. [42] with the non-parametric warping of Kim et al. [55]. Furthermore, our system extends the normal NST paradigm: although it can be used with a single exemplar, we also allow two style exemplars: one for texture and another for geometry. This supports far greater flexibility in use cases than single exemplars can provide.

关键词： Geometry computer vision Art Semantics Neural networks pattern recognition Strain

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Progressive Unsupervised Learning for Visual Object Tracking

Progressive Unsupervised Learning for Visual Object Tracking

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

作者： Wu, Qiangqiang Wan, Jia Chan, Antoni B. City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9781665445092

In this paper, we propose a progressive unsupervised learning (PUL) framework, which entirely removes the need for annotated training videos in visual tracking. Specifically, we first learn a background discrimination (BD) model that effectively distinguishes an object from background in a contrastive learning way. We then employ the BD model to progressively mine temporal corresponding patches (i.e., patches connected by a track) in sequential frames. As the BD model is imperfect and thus the mined patch pairs are noisy, we propose a noise-robust loss function to more effectively learn temporal correspondences from this noisy data. We use the proposed noise robust loss to train backbone networks of Siamese trackers. Without online fine-tuning or adaptation, our unsupervised real-time Siamese trackers can outperform state-of-the-art unsupervised deep trackers and achieve competitive results to the supervised baselines.

关键词： Training Visualization Real-time systems Data models Noise robustness pattern recognition Noise measurement

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CompletionFormer: Depth Completion with Convolutions and vision Transformers

CompletionFormer: Depth Completion with Convolutions and Vis...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Youmin Zhang Xianda Guo Matteo Poggi Zheng Zhu Guan Huang Stefano Mattoccia University of Bologna PhiGent Robotics

Given sparse depths and the corresponding RGB images, depth completion aims at spatially propagating the sparse measurements throughout the whole image to get a dense depth prediction. Despite the tremendous progress of deep-learning-based depth completion methods, the locality of the convolutional layer or graph model makes it hard for the network to model the long-range relationship between pixels. While recent fully Transformer-based architecture has reported encouraging results with the global receptive field, the performance and efficiency gaps to the well-developed CNN models still exist because of its deteriorative local feature details. This paper proposes a Joint Convolutional Attention and Transformer block (JCAT), which deeply couples the convolutional attention layer and vision Transformer into one block, as the basic unit to construct our depth completion model in a pyramidal structure. This hybrid architecture naturally benefits both the local connec-tivity of convolutions and the global context of the Trans-former in one single model. As a result, our Completion-Former outperforms state-of-the-art CNNs-based methods on the outdoor KITTI Depth Completion benchmark and indoor NYUv2 dataset, achieving significantly higher effi-ciency (nearly 1/3 FLOPs) compared to pure Transformer-based methods. Code is available at https://***/youmi-zym/CompletionFormer.

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GIVL: Improving Geographical Inclusivity of vision-Language Models with Pre-Training Methods

GIVL: Improving Geographical Inclusivity of Vision-Language ...

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conference on computer vision and pattern recognition (cvpr)

作者： Da Yin Feng Gao Govind Thattai Michael Johnston Kai-Wei Chang University of California Los Angeles Amazon Alexa AI

A key goal for the advancement of AI is to develop technologies that serve the needs not just of one group but of all communities regardless of their geographical re-gion. In fact, a significant proportion of knowledge is locally shared by people from certain regions but may not apply equally in other regions because of cultural dif-ferences. If a model is unaware of regional character-istics, it may lead to performance disparity across re-gions and result in bias against underrepresented groups. We propose GIVL, a Geographically Inclusive vision-and-Language Pre-trained model. There are two attributes of geo-diverse visual concepts which can help to learn geo-diverse knowledge: 1) concepts under similar categories have unique knowledge and visual characteristics, 2) concepts with similar visual features may fall in completely different categories. Motivated by the attributes, we de-sign new pre-training objectives Image-Knowledge Matching (IKM) and Image Edit Checking (IEC) to pre-train GIVL. Compared with similar-size models pre-trained with similar scale of data, GIVL achieves state-of-the-art (SOTA) and more balanced performance on geo-diverse V &L tasks. Code and data are released at https://***/WadeYin9712/GIVL.

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Edges to Shapes to Concepts: Adversarial Augmentation for Robust vision

Edges to Shapes to Concepts: Adversarial Augmentation for Ro...

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conference on computer vision and pattern recognition (cvpr)

作者： Aditay Tripathi Rishubh Singh Anirban Chakraborty Pradeep Shenoy CDS Indian Institute of Science Google Research India

Recent work has shown that deep vision models tend to be overly dependent on low-level or “texture” features, leading to poor generalization. Various data augmentation strategies have been proposed to overcome this so-called texture bias in DNNs. We propose a simple, lightweight adversarial augmentation technique that explicitly incentivizes the network to learn holistic shapes for accurate prediction in an object classification setting. Our augmentations superpose edgemaps from one image onto another image with shuffled patches, using a randomly determined mixing proportion, with the image label of the edgemap image. To classify these augmented images, the model needs to not only detect and focus on edges but distinguish between relevant and spurious edges. We show that our augmentations significantly improve classification accuracy and robustness measures on a range of datasets and neural architectures. As an example, for ViT-S, We obtain absolute gains on classification accuracy gains up to 6%. We also obtain gains of up to 28% and 8.5% on natural adversarial and out-of-distribution datasets like ImageNet-A (for ViT-B) and ImageNet-R (for ViT-S), respectively. Analysis using a range of probe datasets shows substantially increased shape sensitivity in our trained models, explaining the observed improvement in robustness and classification accuracy.

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Model Barrier: A Compact Un-Transferable Isolation Domain for Model Intellectual Property Protection

Model Barrier: A Compact Un-Transferable Isolation Domain fo...

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conference on computer vision and pattern recognition (cvpr)

作者： Lianyu Wang Meng Wang Daoqiang Zhang Huazhu Fu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) Singapore

As scientific and technological advancements result from human intellectual labor and computational costs, protecting model intellectual property (IP) has become increasingly important to encourage model creators and owners. Model IP protection involves preventing the use of well-trained models on unauthorized domains. To address this issue, we propose a novel approach called Compact Un-Transferable Isolation Domain (CUTI-domain), which acts as a barrier to block illegal transfers from authorized to unauthorized domains. Specifically, CUTI-domain blocks crossdomain transfers by highlighting the private style features of the authorized domain, leading to recognition failure on unauthorized domains with irrelevant private style features. Moreover, we provide two solutions for using CUTI-domain depending on whether the unauthorized domain is known or not: target-specified CUTI-domain and target-free CUTI-domain. Our comprehensive experimental results on four digit datasets, CIFAR10 & STL10, and VisDA-2017 dataset demonstrate that CUTI-domain can be easily implemented as a plug-and-play module with different backbones, providing an efficient solution for model IP protection.

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Texts as Images in Prompt Tuning for Multi-Label Image recognition

Texts as Images in Prompt Tuning for Multi-Label Image Recog...

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conference on computer vision and pattern recognition (cvpr)

作者： Zixian Guo Bowen Dong Zhilong Ji Jinfeng Bai Yiwen Guo Wangmeng Zuo Harbin Institute of Technology Tomorrow Advancing Life Independent Researcher Pazhou Lab Guangzhou

Prompt tuning has been employed as an efficient way to adapt large vision-language pre-trained models (e.g. CLIP) to various downstream tasks in data-limited or label-limited settings. Nonetheless, visual data (e.g., images) is by default prerequisite for learning prompts in existing methods. In this work, we advocate that the effectiveness of image-text contrastive learning in aligning the two modalities (for training CLIP) further makes it feasible to treat texts as images for prompt tuning and introduce TaI prompting. In contrast to the visual data, text descriptions are easy to collect, and their class labels can be directly derived. Particularly, we apply TaI prompting to multi-label image recognition, where sentences in the wild serve as alternatives to images for prompt tuning. Moreover, with TaI, double-grained prompt tuning (TaI-DPT) is further presented to extract both coarse-grained and fine-grained embeddings for enhancing the multi-label recognition performance. Experimental results show that our proposed TaI-DPT outperforms zero-shot CLIP by a large margin on multiple benchmarks, e.g., MS-COCO, VOC2007, and NUS-WIDE, while it can be combined with existing methods of prompting from images to improve recognition performance further. The code is released at https://***/guozix/TaI-DPT.

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PREDATOR: Registration of 3D Point Clouds with Low Overlap

PREDATOR: Registration of 3D Point Clouds with Low Overlap

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

作者： Huang, Shengyu Gojcic, Zan Usvyatsov, Mikhail Wieser, Andreas Schindler, Konrad Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781665445092

We introduce PREDATOR, a model for pairwise point-cloud registration with deep attention to the overlap region. Different from previous work, our model is specifically designed to handle (also) point-cloud pairs with low overlap. Its key novelty is an overlap-attention block for early information exchange between the latent encodings of the two point clouds. In this way the subsequent decoding of the latent representations into per-point features is conditioned on the respective other point cloud, and thus can predict which points are not only salient, but also lie in the overlap region between the two point clouds. The ability to focus on points that are relevant for matching greatly improves performance: PREDATOR raises the rate of successful registrations by more than 20% in the low-overlap scenario, and also sets a new state of the art for the 3DMatch benchmark with 89% registration recall.

关键词： Convolutional codes Solid modeling computer vision Three-dimensional displays Image matching computer architecture Encoding

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