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SparseOcc: Rethinking Sparse Latent Representation for vision-Based Semantic Occupancy Prediction

SparseOcc: Rethinking Sparse Latent Representation for Visio...

引用

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

作者： Tang, Pin Wang, Zhongdao Wang, Gum-ling Zheng, Jilai Ren, Xiangxuan Feng, Bailan Ma, Chao Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9798350353006

vision-based perception for autonomous driving requires an explicit modeling of a 3D space, where 2D latent representations are mapped and subsequent 3D operators are applied. However, operating on dense latent spaces introduces a cubic time and space complexity, which limits scalability in terms of perception range or spatial resolution. Existing approaches compress the dense representation using projections like Bird's Eye View (BEV) or Tri-Perspective View (TPV). Although efficient, these projections result in information loss, especially for tasks like semantic occupancy prediction. To address this, we propose SparseOcc, an efficient occupancy network inspired by sparse point cloud processing. It utilizes a lossless sparse latent representation with three key innovations. Firstly, a 3D sparse diffuser performs latent completion using spatially decomposed 3D sparse convolutional kernels. Secondly, a feature pyramid and sparse interpolation enhance scales with information from others. Finally, the transformer head is redesigned as a sparse variant. SparseOcc achieves a remarkable 74.9% reduction on FLOPs over the dense baseline. Interestingly, it also improves accuracy, from 12.8% to 14.1% mIOU, which in part can be attributed to the sparse representation's ability to avoid hallucinations on empty voxels.

关键词： Autonomous Driving Occupancy Prediction Sparse Representation vision-Based Perception

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Fair Scratch Tickets: Finding Fair Sparse Networks without Weight Training

Fair Scratch Tickets: Finding Fair Sparse Networks without W...

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

作者： Tang, Pengwei Yao, Wei Li, Zhicong Liu, Yong Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China

ISBN: (纸本)9798350301298

Recent studies suggest that computer vision models come at the risk of compromising fairness. There are extensive works to alleviate unfairness in computer vision using pre-processing, in-processing, and post-processing methods. In this paper, we lead a novel fairness-aware learning paradigm for in-processing methods through the lens of the lottery ticket hypothesis (LTH) in the context of computer vision fairness. We randomly initialize a dense neural network and find appropriate binary masks for the weights to obtain fair sparse subnetworks without any weight training. Interestingly, to the best of our knowledge, we are the first to discover that such sparse subnetworks with inborn fairness exist in randomly initialized networks, achieving an accuracy-fairness trade-off comparable to that of dense neural networks trained with existing fairness-aware in-processing approaches. We term these fair subnetworks as Fair Scratch Tickets (FSTs). We also theoretically provide fairness and accuracy guarantees for them. In our experiments, we investigate the existence of FSTs on various datasets, target attributes, random initialization methods, sparsity patterns, and fairness surrogates. We also find that FSTs can transfer across datasets and investigate other properties of FSTs.

关键词： accountability ethics in vision fairness privacy Transparency

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Stereo-Based 3D Reconstruction of Dynamic Fluid Surfaces by Global Optimization 30

Stereo-Based 3D Reconstruction of Dynamic Fluid Surfaces by ...

引用

30th ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Qian, Yiming Gong, Minglun Yang, Yee-Hong Univ Alberta Edmonton AB Canada Mem Univ Newfoundland St John NF Canada

ISBN: (纸本)9781538604571

3D reconstruction of dynamic fluid surfaces is an open and challenging problem in computer vision. Unlike previous approaches that reconstruct each surface point independently and often return noisy depth maps, we propose a novel global optimization-based approach that recovers both depths and normals of all 3D points simultaneously. Using the traditional refraction stereo setup, we capture the wavy appearance of a pre-generated random pattern, and then estimate the correspondences between the captured images and the known background by tracking the pattern. Assuming that the light is refracted only once through the fluid interface, we minimize an objective function that incorporates both the cross-view normal consistency constraint and the single-view normal consistency constraints. The key idea is that the normals required for light refraction based on Snell's law from one view should agree with not only the ones from the second view, but also the ones estimated from local 3D geometry. Moreover, an effective reconstruction error metric is designed for estimating the refractive index of the fluid. We report experimental results on both synthetic and real data demonstrating that the proposed approach is accurate and shows superiority over the conventional stereo-based method.

关键词： Stereo-Based 3D Reconstruction Dynamic Fluid Surfaces by Global Optimization three-dimensional displays Global optimization Reconstructive Surgical Procedures Fluid images computer vision normal consistency Light refraction consistency constraint Reconstructions Normal

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Object Localization under Single Coarse Point Supervision

Object Localization under Single Coarse Point Supervision

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

作者： Yu, Xuehui Chen, Pengfei Wu, Di Hassan, Najmul Li, Guorong Yan, Junchi Shi, Humphrey Ye, Qixiang Han, Zhenjun Univ Chinese Acad Sci Beijing Peoples R China Univ Oregon Eugene OR 97403 USA Shanghai Jiao Tong Univ Shanghai Peoples R China Picsart AI Res PAIR Eugene OR USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Point-based object localization (POL), which pursues high-performance object sensing under low-cost data annotation, has attracted increased attention. However, the point annotation mode inevitably introduces semantic variance for the inconsistency of annotated points. Existing POL methods heavily reply on accurate key-point annotations which are difficult to define. In this study, we propose a POL method using coarse point annotations, relaxing the supervision signals from accurate key points to freely spotted points. To this end, we propose a coarse point refinement (CPR) approach, which to our best knowledge is the first attempt to alleviate semantic variance from the perspective of algorithm. CPR constructs point bags, selects semantic-correlated points, and produces semantic center points through multiple instance learning (MIL). In this way, CPR defines a weakly supervised evolution procedure, which ensures training high-performance object localizer under coarse point supervision. Experimental results on COCO, DOTA and our proposed SeaPerson dataset validate the effectiveness of the CPR approach. The dataset and code will be available at https://***/ucas- vg/PointTinyBenchmark/.

关键词： Location awareness Training computer vision Codes Annotations Semantics Sensors

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Dual Attention MobDenseNet(DAMDNet) for Robust 3D Face Alignment 17

Dual Attention MobDenseNet(DAMDNet) for Robust 3D Face Align...

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ieee/cvf International conference on computer vision (ICCV)

作者： Jiang, Lei Wu, Xiao-Jun Kittler, Josef Jiangnan Univ Sch IoT Engn Wuxi 214122 Jiangsu Peoples R China Univ Surry Ctr Vis Speech & Signal Proc CVSSP Guildford GU27XH Surrey England Jiangnan Univ Jiangsu Prov Engn Lab Pattern Recognit & Computat Wuxi 214122 Jiangsu Peoples R China

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150239

3D face alignment of monocular images is a crucial process in the recognition of faces with disguise.3D face reconstruction facilitated by alignment can restore the face structure which is helpful in detcting disguise *** paper proposes a dual attention mechanism and an efficient end-to-end 3D face alignment framework. We build a stable network model through Depthwise Separable Convolution, Densely Connected Convolutional and Lightweight Channel Attention Mechanism. In order to enhance the ability of the network model to extract the spatial features of the face region, we adopt Spatial Group-wise Feature enhancement module to improve the representation ability of the network. Different loss functions are applied jointly to constrain the 3D parameters of a 3D Morphable Model (3DMM) and its 3D vertices. We use a variety of data enhancement methods and generate large virtual pose face data sets to solve the data imbalance problem. The experiments on the challenging AFLW,AFLW2000-3D datasets show that our algorithm significantly improves the accuracy of 3D face alignment. Our experiments using the field DFW dataset show that DAMDNet exhibits excellent performance in the 3D alignment and reconstruction of challenging disguised *** model parameters and the complexity of the proposed method are also reduced significantly.

关键词： Face Three-dimensional displays Solid modeling Shape Two dimensional displays Feature extraction Image reconstruction

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MDL-NAS: A Joint Multi-domain Learning Framework for vision Transformer

MDL-NAS: A Joint Multi-domain Learning Framework for Vision ...

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

作者： Wang, Shiguang Xie, Tao Cheng, Jian Zhang, Xingcheng Liu, Haijun Univ Elect Sci & Technol China Chengdu Peoples R China Harbin Inst Technol Harbin Peoples R China SenseTime Res Hong Kong Peoples R China Chongqing Univ Chongqing Peoples R China

ISBN: (纸本)9798350301298

In this work, we introduce MDL-NAS, a unified framework that integrates multiple vision tasks into a manageable supernet and optimizes these tasks collectively under diverse dataset domains. MDL-NAS is storage-efficient since multiple models with a majority of shared parameters can be deposited into a single one. Technically, MDL-NAS constructs a coarse-to-fine search space, where the coarse search space offers various optimal architectures for different tasks while the fine search space provides fine-grained parameter sharing to tackle the inherent obstacles of multi-domain learning. In the fine search space, we suggest two parameter sharing policies, i.e., sequential sharing policy and mask sharing policy. Compared with previous works, such two sharing policies allow for the partial sharing and non-sharing of parameters at each layer of the network, hence attaining real fine-grained parameter sharing. Finally, we present a joint-subnet search algorithm that finds the optimal architecture and sharing parameters for each task within total resource constraints, challenging the traditional practice that downstream vision tasks are typically equipped with backbone networks designed for image classification. Experimentally, we demonstrate that MDL-NAS families fitted with non-hierarchical or hierarchical transformers deliver competitive performance for all tasks compared with state-of-the-art methods while maintaining efficient storage deployment and computation. We also demonstrate that MDL-NAS allows incremental learning and evades catastrophic forgetting when generalizing to a new task.

关键词： Deep learning architectures and techniques

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Learning Neural Light Fields with Ray-Space Embedding

Learning Neural Light Fields with Ray-Space Embedding

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

作者： Attal, Benjamin Huang, Jia-Bin Zollhofer, Michael Kopf, Johannes Kim, Changil Carnegie Mellon Univ Pittsburgh PA 15213 USA Meta Menlo Pk CA USA Real Labs Res Pittsburgh PA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Neural radiance fields (NeRFs) produce state-of-the-art view synthesis results, but are slow to render, requiring hundreds of network evaluations per pixel to approximate a volume rendering integral. Baking NeRFs into explicit data structures enables efficient rendering, but results in large memory footprints and, in some cases, quality reduction. Additionally, volumetric representations for view synthesis often struggle to represent challenging view dependent effects such as distorted reflections and refractions. We present a novel neural light field representation that, in contrast to prior work, is fast, memory efficient, and excels at modeling complicated view dependence. Our method supports rendering with a single network evaluation per pixel for small baseline light fields and with only a few evaluations per pixel for light fields with larger baselines. At the core of our approach is a ray-space embedding network that maps 4D ray-space into an intermediate, interpolable latent space. Our method achieves state-of-the-art quality on dense forward-facing datasets such as the Stanford Light Field dataset. In addition, for forward-facing scenes with sparser inputs we achieve results that are competitive with NeRF-based approaches while providing a better speed/quality/memory trade-off with far fewer network evaluations.

关键词： Photography Interpolation Three-dimensional displays Memory management Rendering (computer graphics) Light fields Reflection

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End-to-End Semi-Supervised Learning for Video Action Detection

End-to-End Semi-Supervised Learning for Video Action Detecti...

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

作者： Kumar, Akash Rawat, Yogesh Singh Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this work, we focus on semi-supervised learning for video action detection which utilizes both labeled as well as unlabeled data. We propose a simple end-to-end consistency based approach which effectively utilizes the unlabeled data. Video action detection requires both, action class prediction as well as a spatio-temporal localization of actions. Therefore, we investigate two types of constraints, classification consistency, and spatio-temporal consistency. The presence of predominant background and static regions in a video makes it challenging to utilize spatio-temporal consistency for action detection. To address this, we propose two novel regularization constraints for spatio-temporal consistency;1) temporal coherency, and 2) gradient smoothness. Both these aspects exploit the temporal continuity of action in videos and are found to be effective for utilizing unlabeled videos for action detection. We demonstrate the effectiveness of the proposed approach on two different action detection benchmark datasets, UCF101-24 and JHMDB-21. In addition, we also show the effectiveness of the proposed approach for video object segmentation on the Youtube-VOS which demonstrates its generalization capability The proposed approach achieves competitive performance by using merely 20% of annotations on UCF101-24 when compared with recent fully supervised methods. On UCF101-24, it improves the score by +8.9% and +11% at 0.5 f-mAP and v-mAP respectively, compared to supervised approach. The code and models will be made publicly available at: https://***/AKASH2907/End-to-End-Semi-Supervised-Learning-for-Video-Action-Detection.

关键词： Location awareness computer vision Codes Annotations Object segmentation Semisupervised learning Benchmark testing

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Structure Preserving Video Prediction 31

Structure Preserving Video Prediction

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31st ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Xu, Jingwei Ni, Bingbing Li, Zefan Cheng, Shuo Yang, Xiaokang Shanghai Jiao Tong Univ Shanghai Key Lab Digital Media Proc & Transmiss Shanghai Inst Adv Commun & Data Sci Shanghai 200240 Peoples R China

ISBN: (纸本)9781538664209

Despite recent emergence of adversarial based methods for video prediction, existing algorithms often produce unsatisfied results in image regions with rich structural information (i.e., object boundary) and detailed motion (i.e., articulated body movement). To this end, we present a structure preserving video prediction framework to explicitly address above issues and enhance video prediction quality. On one hand, our framework contains a two-stream generation architecture which deals with high frequency video content (i.e., detailed object or articulated motion structure) and low frequency video content (i.e., location or moving directions) in two separate streams. On the other hand, we propose a RNN structure for video prediction, which employs temporal-adaptive convolutional kernels to capture time-varying motion patterns as well as tiny objects within a scene. Extensive experiments on diverse scenes, ranging from human motion to semantic layout prediction, demonstrate the effectiveness of the proposed video prediction approach.

关键词： Kernel Task analysis computer architecture Dynamics Decoding Predictive models

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DegAE: A New Pretraining Paradigm for Low-level vision

DegAE: A New Pretraining Paradigm for Low-level Vision

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

作者： Liu, Yihao He, Jingwen Gu, Jinjin Kong, Xiangtao Qiao, Yu Dong, Chao Shanghai Artificial Intelligence Lab Shanghai Peoples R China Chinese Acad Sci ShenZhen Key Lab Comp Vis & Pattern Recognit Shenzhen Inst Adv Technol Shenzhen Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (纸本)9798350301298

Self-supervised pretraining has achieved remarkable success in high-level vision, but its application in low-level vision remains ambiguous and not well-established. What is the primitive intention of pretraining? What is the core problem of pretraining in low-level vision? In this paper, we aim to answer these essential questions and establish a new pretraining scheme for low-level vision. Specifically, we examine previous pretraining methods in both high-level and low-level vision, and categorize current low-level vision tasks into two groups based on the difficulty of data acquisition: low-cost and high-cost tasks. Existing literature has mainly focused on pretraining for low-cost tasks, where the observed performance improvement is often limited. However, we argue that pretraining is more significant for highcost tasks, where data acquisition is more challenging. To learn a general low-level vision representation that can improve the performance of various tasks, we propose a new pretraining paradigm called degradation autoencoder (DegAE). DegAE follows the philosophy of designing pretext task for self-supervised pretraining and is elaborately tailored to low-level vision. With DegAE pretraining, SwinIR achieves a 6.88dB performance gain on image dehaze task, while Uformer obtains 3.22dB and 0.54dB improvement on dehaze and derain tasks, respectively.

关键词： Low-level vision

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