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Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes

Neural Geometric Level of Detail: Real-time Rendering with I...

引用

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

作者： Takikawa, Towaki Litalien, Joey Yin, Kangxue Kreis, Karsten Loop, Charles Nowrouzezahrai, Derek Jacobson, Alec McGuire, Morgan Fidler, Sanja NVIDIA Santa Clara CA 95051 USA Univ Toronto Toronto ON Canada McGill Univ Montreal PQ Canada Vector Inst Toronto ON Canada

ISBN: (纸本)9781665445092

Neural signed distance functions (SDFs) are emerging as an effective representation for 3D shapes. State-of-the-art methods typically encode the SDF with a large, fixed-size neural network to approximate complex shapes with implicit surfaces. Rendering with these large networks is, however, computationally expensive since it requires many forward passes through the network for every pixel, making these representations impractical for real-time graphics. We introduce an efficient neural representation that, for the first time, enables real-time rendering of high-fidelity neural SDFs, while achieving state-of-the-art geometry reconstruction quality. We represent implicit surfaces using an octree-based feature volume which adaptively fits shapes with multiple discrete levels of detail (LODs), and enables continuous LOD with SDF interpolation. We further develop an efficient algorithm to directly render our novel neural SDF representation in real-time by querying only the necessary LODs with sparse octree traversal. We show that our representation is 2-3 orders of magnitude more efficient in terms of rendering speed compared to previous works. Furthermore, it produces state-of-the-art reconstruction quality for complex shapes under both 3D geometric and 2D image-space metrics.

关键词： Measurement Surface reconstruction Three-dimensional displays Shape Octrees Neural networks Rendering (computer graphics)

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A Unified Framework for Human-centric Point Cloud Video Understanding

A Unified Framework for Human-centric Point Cloud Video Unde...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yiteng Xu Kecheng Ye Xiao Han Yiming Ren Xinge Zhu Yuexin Ma ShanghaiTech University The Chinese University of Hong Kong

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Human-centric Point Cloud Video Understanding (PVU) is an emerging field focused on extracting and interpreting human-related features from sequences of human point clouds, further advancing downstream human-centric tasks and applications. Previous works usually focus on tack-ling one specific task and rely on huge labeled data, which has poor generalization capability. Considering that hu-man has specific characteristics, including the structural semantics of human body and the dynamics of human motions, we propose a unified framework to make full use of the prior knowledge and explore the inherent features in the data itself for generalized human-centric point cloud video understanding. Extensive experiments demonstrate that our method achieves state-of-the-art performance on various human-related tasks, including action recognition and 3D pose estimation. All datasets and code will be re-leased soon.

关键词： Point cloud compression computer vision Three-dimensional displays Codes Semantics Dynamics Pose estimation

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ParameterNet: Parameters are All You Need for Large-Scale Visual Pretraining of Mobile Networks

ParameterNet: Parameters are All You Need for Large-Scale Vi...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Kai Han Yunhe Wang Jianyuan Guo Enhua Wu Huawei Noah's Ark Lab State Key Lab of Computer Science ISCAS & UCAS The University of Sydney Faculty of Science and Technology University of Macau

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The large-scale visual pretraining has significantly improve the performance of large vision models. However, we observe the low FLOPs pitfall that the existing low-FLOPs models cannot benefit from large-scale pretraining. In this paper, we introduce a novel design principle, termed ParameterNet, aimed at augmenting the number of parameters in large-scale visual pretraining models while minimizing the increase in FLOPs. We leverage dynamic convolutions to incorporate additional parameters into the networks with only a marginal rise in FLOPs. The ParameterNet approach allows low-FLOPs networks to take advantage of large-scale visual pretraining. Furthermore, we extend the ParameterNet concept to the language domain to enhance inference results while preserving inference speed. Experiments on the large-scale ImageNet-22K have shown the superiority of our ParameterNet scheme. For example, ParameterNet-600M can achieve higher accuracy than the widely-used Swin Transformer (81.6% vs. 80.9%) and has much lower FLOPs (0.6G vs. 4.5G). The code will be released at https://***/.

关键词： Convolutional codes Visualization computer vision Accuracy Transformers pattern recognition

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Gaussian Context Transformer

Gaussian Context Transformer

引用

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

作者： Ruan, Dongsheng Wang, Daiyin Zheng, Yuan Zheng, Nenggan Zheng, Min Zhejiang Univ Qiushi Acad Adv Studies Hangzhou Zhejiang Peoples R China Zhejiang Univ Coll Opt Sci & Engn Hangzhou Zhejiang Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou Zhejiang Peoples R China Zhejiang Univ Sch Aeronaut & Astronaut Hangzhou Zhejiang Peoples R China Zhejiang Univ State Key Lab Diag & Treatment Infect Dis Affiliated Hosp 1 Coll Med Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665445092

Recently, a large number of channel attention blocks are proposed to boost the representational power of deep convolutional neural networks (CNNs). These approaches commonly learn the relationship between global contexts and attention activations by fully-connected layers or linear transformations. However, we empirically find that though many parameters are introduced, these attention blocks may not learn the relationship well. In this paper, we hypothesize that the relationship is predetermined. Based on this hypothesis, we propose a simple yet extremely efficient channel attention block, called Gaussian Context Transformer (GCT), which achieves contextual feature excitation using a Gaussian function that satisfies the presupposed relationship. According to whether the standard deviation of the Gaussian function is learnable, we develop two versions of GCT: GCT-B0 and GCT-B1. GCT-B0 is a parameter-free channel attention block by fixing the standard deviation. It directly maps global contexts to attention activations without learning. In contrast, GCT-B1 is a parameterized version, which adaptively learns the standard deviation to enhance the mapping ability. Extensive experiments on ImageNet and MS COCO benchmarks demonstrate that our GCTs lead to consistent improvements across various deep CNNs and detectors. Compared with a bank of state-of-the-art channel attention blocks, such as SE [17] and ECA [42], our GCTs are superior in effectiveness and efficiency.

关键词： computer vision Transforms Detectors Benchmark testing Transformers Solids pattern recognition

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Improved Self-Training for Test-Time Adaptation

Improved Self-Training for Test-Time Adaptation

引用

conference on computer vision and pattern recognition (cvpr)

作者： Jing Ma Huazhong University of Science and Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Test-time adaptation (TTA) is a technique to improve the performance of a pretrained source model on a target distribution without using any labeled data. However, existing self-trained TTA methods often face the challenges of un-reliable pseudo-labels and unstable model optimization. In this paper, we propose an Improved Self-Training (IST) approach, which addresses these challenges by enhancing the pseudo-label quality and stabilizing the adaptation process. Specifically, we use a simple augmentation strategy to gen-erate multiple views of each test sample, and construct a graph structure to correct the pseudo-labels based on the similarity of the latent features. Moreover, we adopt a parameter moving average scheme to smooth the model up-dates and prevent catastrophic forgetting. Instead of using a model with fixed label space, we explore the adaptability of the foundation model CLIP to various downstream tasks at test time. Extensive experiments on various benchmarks show that IST can achieve significant and consistent improvements over the existing TTA methods in classification, detection, and segmentation tasks.

关键词： Adaptation models computer vision Semantic segmentation Face recognition Object detection Benchmark testing Data models

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Learning Unbiased Representations via Mutual Information Backpropagation

Learning Unbiased Representations via Mutual Information Bac...

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

作者： Ragonesi, Ruggero Volpi, Riccardo Cavazza, Jacopo Murino, Vittorio Ist Italiano Tecnol PAVIS Dept Genoa Italy Univ Genoa DITEN Dept Genoa Italy Naver Labs Europe Grenoble France Univ Verona Dept Comp Sci Verona Italy Huawei Technol Ltd Ireland Res Ctr Dublin Ireland

ISBN: (纸本)9781665448994

We are interested in learning data-driven representations that can generalize well, even when trained on inherently biased data. In particular, we face the case where some attributes (bias) of the data, if learned by the model, can severely compromise its generalization properties. We tackle this problem through the lens of information theory, leveraging recent findings for a differentiable estimation of mutual information. We propose a novel end-to-end optimization strategy, which simultaneously estimates and minimizes the mutual information between the learned representation and specific data attributes. When applied on standard benchmarks, our model shows comparable or superior classification performance with respect to state-of-the-art approaches. Moreover, our method is general enough to be applicable to the problem of "algorithmic fairness", with competitive results.

关键词： Training computer vision Face recognition Estimation Benchmark testing Mutual information Tuning

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Data Poisoning Based Backdoor Attacks to Contrastive Learning

Data Poisoning Based Backdoor Attacks to Contrastive Learnin...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Jinghuai Zhang Hongbin Liu Jinyuan Jia Neil Zhenqiang Gong University of California Los Angeles Duke University Penn State

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Contrastive learning (CL) pretrains general-purpose encoders using an unlabeled pretraining dataset, which consists of images or image-text pairs. CL is vulnera-ble to data poisoning based backdoor attacks (DPBAs), in which an attacker injects poisoned inputs into the pretraining dataset so the encoder is backdoored. However, existing DPBAs achieve limited effectiveness. In this work, we take the first step to analyze the limitations of existing backdoor attacks and propose new DPBAs called CorruptEncoder to CL. CorruptEncoder introduces a new attack strategy to create poisoned inputs and uses a theory-guided method to maximize attack effectiveness. Our experiments show that CorruptEncoder substantially outperforms existing DPBAs. In particular, CorruptEncoder is the first DPBA that achieves more than 90% attack success rates with only a few (3) reference images and a small poisoning ratio (0.5%). Moreover, we also propose a de-fense, called localized cropping, to defend against DPBAs. Our results show that our defense can reduce the effectiveness of DPBAs, but it sacrifices the utility of the encoder, highlighting the need for new defenses.

关键词： computer vision Contrastive learning pattern recognition

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Efficient and Effective Weakly-Supervised Action Segmentation via Action-Transition-Aware Boundary Alignment

Efficient and Effective Weakly-Supervised Action Segmentatio...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Angchi Xu Wei-Shi Zheng School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangdong Province Key Laboratory of Information Security Technology Guangzhou China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Weakly-supervised action segmentation is a task of learning to partition a long video into several action segments, where training videos are only accompanied by transcripts (ordered list of actions). Most of existing methods need to infer pseudo segmentation for training by serial alignment between all frames and the transcript, which is time-consuming and hard to be parallelized while training. In this work, we aim to escape from this inefficient alignment with massive but redundant frames, and instead to directly localize a few action transitions for pseudo segmentation generation, where a transition refers to the change from an action segment to its next adjacent one in the transcript. As the true transitions are submerged in noisy boundaries due to intra-segment visual variation, we propose a novel Action-Transition-Aware Boundary Alignment (ATBA) framework to efficiently and effectively filter out noisy boundaries and detect transitions. In addition, to boost the semantic learning in the case that noise is inevitably present in the pseudo segmentation, we also introduce video-level losses to utilize the trusted video-level supervision. Extensive experiments show the effectiveness of our approach on both performance and training speed. 1 1 Code is available at https://***/iSEE-Laboratory/cvpr24_ATBA.

关键词： Training Visualization computer vision Semantics Noise Robustness pattern recognition

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ScanFormer: Referring Expression Comprehension by Iteratively Scanning

ScanFormer: Referring Expression Comprehension by Iterativel...

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conference on computer vision and pattern recognition (cvpr)

作者： Wei Su Peihan Miao Huanzhang Dou Xi Li College of Computer Science and Technology Zhejiang University School of Software Technology Zhejiang University Zhejiang-Singapore Innovation and Al Joint Research Lab

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Referring Expression Comprehension (REC) aims to localize the target objects specified by free-form natural language descriptions in images. While state-of-the-art methods achieve impressive performance, they perform a dense perception of images, which incorporates redundant visual regions unrelated to linguistic queries, leading to additional computational overhead. This inspires us to explore a question: can we eliminate linguistic-irrelevant redundant visual regions to improve the efficiency of the model? Existing relevant methods primarily focus on fundamental visual tasks, with limited exploration in vision-language fields. To address this, we propose a coarse-to-fine iterative perception framework, called ScanFormer. It can iteratively exploit the image scale pyramid to extract linguistic-relevant visual patches from top to bottom. In each iteration, irrelevant patches are discarded by our designed informativeness prediction. Furthermore, we propose a patch selection strategy for discarded patches to accelerate inference. Experiments on widely used datasets, namely Ref COCO, Ref COCO+, Ref COCO g, and ReferItGame, verify the effectiveness of our method, which can strike a balance between accuracy and efficiency.

关键词： Visualization computer vision Accuracy Computational modeling Linguistics pattern recognition Iterative methods

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Thinking Fast and Slow: Efficient Text-to-Visual Retrieval with Transformers

Thinking Fast and Slow: Efficient Text-to-Visual Retrieval w...

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

作者： Miech, Antoine Alayrac, Jean-Baptiste Laptev, Ivan Sivic, Josef Zisserman, Andrew DeepMind London England ENS Inria Lyon France CIIRC CTU Prague Czech Republic VGG Oxford Oxford England PSL Res Univ CNRS Ecole Normale Super Dept InformatENS F-75005 Paris France Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague Czech Republic Univ Oxford Dept Engn Sci VGG Oxford England

ISBN: (纸本)9781665445092

Our objective is language-based search of large-scale image and video datasets. For this task, the approach that consists of independently mapping text and vision to a joint embedding space, a.k.a. dual encoders, is attractive as retrieval scales and is efficient for billions of images using approximate nearest neighbour search. An alternative approach of using vision-text transformers with cross-attention gives considerable improvements in accuracy over the joint embeddings, but is often inapplicable in practice for large-scale retrieval given the cost of the cross-attention mechanisms required for each sample at test time. This work combines the best of both worlds. We make the following three contributions. First, we equip transformerbased models with a new fine-grained cross-attention architecture, providing significant improvements in retrieval accuracy whilst preserving scalability. Second, we introduce a generic approach for combining a Fast dual encoder model with our Slow but accurate transformer-based model via distillation and re-ranking. Finally, we validate our approach on the Flickr30K image dataset where we show an increase in inference speed by several orders of magnitude while having results competitive to the state of the art. We also extend our method to the video domain, improving the state of the art on the VATEX dataset.

关键词： computer vision Costs Computational modeling Scalability computer architecture Transformers Search problems

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