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Neural Underwater Scene Representation

Neural Underwater Scene Representation

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yunkai Tang Chengxuan Zhu Renjie Wan Chao Xu Boxin Shi National Engineering Research Center of Visual Technology School of Computer Science Peking University National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Key Lab of General AI School of Intelligence Science and Technology Peking University Department of Computer Science Hong Kong Baptist University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Among the numerous efforts towards digitally recovering the physical world, Neural Radiance Fields (NeRFs) have proved effective in most cases. However, underwater scene introduces unique challenges due to the absorbing water medium, the local change in lighting and the dynamic contents in the scene. We aim at developing a neural under-water scene representation for these challenges, modeling the complex process of attenuation, unstable in-scattering and moving objects during light transport. The proposed method can reconstruct the scenes from both established datasets and in-the-wild videos with outstanding fidelity.

关键词： Solid modeling Visualization Computational modeling Urban areas Lighting Virtual reality Neural radiance field

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Dynamic Neural Network for Multi-Task Learning Searching across Diverse Network Topologies

Dynamic Neural Network for Multi-Task Learning Searching acr...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Wonhyeok Choi Sunghoon Im Department of Electrical Engineering & Computer Science DGIST Daegu Korea

In this paper, we present a new MTL framework that searches for structures optimized for multiple tasks with diverse graph topologies and shares features among tasks. We design a restricted DAG-based central network with read-in/read-out layers to build topologically diverse task-adaptive structures while limiting search space and time. We search for a single optimized network that serves as multiple task adaptive sub-networks using our three-stage training process. To make the network compact and discretized, we propose a flow-based reduction algorithm and a squeeze loss used in the training process. We evaluate our optimized network on various public MTL datasets and show ours achieves state-of-the-art performance. An extensive ablation study experimentally validates the effectiveness of the sub-module and schemes in our framework.

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Grounded Text-to-Image Synthesis with Attention Refocusing

Grounded Text-to-Image Synthesis with Attention Refocusing

引用

conference on computer vision and pattern recognition (cvpr)

作者： Quynh Phung Songwei Ge Jia-Bin Huang University of Maryland College Park

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Driven by the scalable diffusion models trained on large-scale datasets, text-to-image synthesis methods have shown compelling results. However, these models still fail to pre-cisely follow the text prompt involving multiple objects, attributes, or spatial compositions. In this paper, we reveal the potential causes in the diffusion model's cross-attention and self-attention layers. We propose two novel losses to refocus attention maps according to a given spatial layout during sampling. Creating the layouts manually requires additional effort and can be tedious. Therefore, we explore using large language models (LLM) to produce these lay-outs for our method. We conduct extensive experiments on the DrawBench, HRS, and TIFA benchmarks to evaluate our proposed method. We show that our proposed attention re-focusing effectively improves the controllability of existing approaches.

关键词： Visualization computer vision Large language models Computational modeling Layout Text to image Benchmark testing

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PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds

PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estim...

引用

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

作者： Wei, Yi Wang, Ziyi Rao, Yongming Lu, Jiwen Zhou, Jie Tsinghua Univ Dept Automat Beijing Peoples R China State Key Lab Intelligent Technol & Syst Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Beijing Peoples R China

ISBN: (纸本)9781665445092

In this paper, we propose a Point-Voxel Recurrent All-Pairs Field Transforms (PV-RAFT) method to estimate scene flow from point clouds. Since point clouds are irregular and unordered, it is challenging to efficiently extract features from all-pairs fields in the 3D space, where all-pairs correlations play important roles in scene flow estimation. To tackle this problem, we present point-voxel correlation fields, which capture both local and long-range dependencies of point pairs. To capture point-based correlations, we adopt the K-Nearest Neighbors search that preserves fine-grained information in the local region. By voxelizing point clouds in a multi-scale manner, we construct pyramid correlation voxels to model long-range correspondences. Integrating these two types of correlations, our PV-RAFT makes use of all-pairs relations to handle both small and large displacements. We evaluate the proposed method on the FlyingThings3D and KITTI Scene Flow 2015 datasets. Experimental results show that P V-RAFT outperforms state-of-the-art methods by remarkable margins.

关键词： Visualization computer vision Correlation Three-dimensional displays Estimation Lattices Transforms

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Florence-2: Advancing a Unified Representation for a Variety of vision Tasks

Florence-2: Advancing a Unified Representation for a Variety...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Bin Xiao Haiping Wu Weijian Xu Xiyang Dai Houdong Hu Yumao Lu Michael Zeng Ce Liu Lu Yuan Microsoft

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We introduce Florence-2, a novel vision foundation model with a unified, prompt-based representation for various computer vision and vision-language tasks. While existing large vision models excel in transfer learning, they struggle to perform diverse tasks with simple instructions, a capability that implies handling the complexity of various spatial hierarchy and semantic granularity. Florence-2 was designed to take text-prompt as task instructions and generate desirable results in text forms, whether it be captioning, object detection, grounding or segmentation. This multi-task learning setup demands large-scale, high-quality annotated data. To this end, we co-developed FLD-5B that consists of 5.4 billion comprehensive visual annotations on 126 million images, using an iterative strategy of automated image annotation and model refinement. We adopted a sequence-to-sequence structure to train Florence-2 to perform versatile and comprehensive vision tasks. Extensive evaluations on numerous tasks demonstrated Florence-2 to be a strong vision foundation model contender with un-precedented zero-shot and fine-tuning capabilities.

关键词： Visualization computer vision Grounding Annotations Semantics Transfer learning Object detection

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Fingerprint Liveliness Detection using Stacked Ensemble and Transfer Learning Technique

Fingerprint Liveliness Detection using Stacked Ensemble and ...

引用

2024 ieee International conference on computer vision and Machine Intelligence, CVMI 2024

作者： Kumari, Vidya Shekar, B.H. Mangalore University Department of Computer Science Mangaluru India

ISBN: (纸本)9798350376876

The effectiveness and dependability of fingerprint recognition systems make them popular for biometric authentication. This research addresses the challenge of distinguishing between real and spoof fingerprint images using a comprehensive approach that integrates data preprocessing, data augmentation, feature extraction with pretrained models and stacked classifiers. The study begins with rigorous data preprocessing techniques to enhance the quality and consistency of the fingerprint images. Subsequently, data augmentation is employed to increase the diversity of the training dataset, enabling better generalization and robustness of the classification model. Feature extraction is then performed using pretrained convolutional neural network models, namely ResNet50, VGG16, and DenseNet201, to capture discriminative features from the fingerprint *** classifiers leverage the complementary strengths of multiple base classifiers, such as Random Forest, Extra Trees, and Support Vector Classifier, to improve classification performance. The proposed ensemble learning approach achieves an impressive accuracy of 9 9. 3 0 % in distinguishing between real and spoof fingerprint images, demonstrating its effectiveness in enhancing biometric security systems. © 2024 ieee.

关键词： Convolutional neural networks

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PointGuard: Provably Robust 3D Point Cloud Classification

PointGuard: Provably Robust 3D Point Cloud Classification

引用

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

作者： Liu, Hongbin Jia, Jinyuan Gong, Neil Zhenqiang Duke Univ Durham NC 27708 USA

ISBN: (纸本)9781665445092

3D point cloud classification has many safety-critical applications such as autonomous driving and robotic grasping. However, several studies showed that it is vulnerable to adversarial attacks. In particular, an attacker can make a classifier predict an incorrect label for a 3D point cloud via carefully modifying, adding, and/or deleting a small number of its points. Randomized smoothing is state-of-the-art technique to build certifiably robust 2D image classifiers. However, when applied to 3D point cloud classification, randomized smoothing can only certify robustness against adversarially modified points. In this work, we propose PointGuard, the first defense that has provable robustness guarantees against adversarially modified, added, and/or deleted points. Specifically, given a 3D point cloud and an arbitrary point cloud classifier, our PointGuard first creates multiple subsampled point clouds, each of which contains a random subset of the points in the original point cloud;then our PointGuard predicts the label of the original point cloud as the majority vote among the labels of the subsampled point clouds predicted by the point cloud classifier. Our first major theoretical contribution is that we show PointGuard provably predicts the same label for a 3D point cloud when the number of adversarially modified, added, and/or deleted points is bounded. Our second major theoretical contribution is that we prove the tightness of our derived bound when no assumptions on the point cloud classifier are made. Moreover;we design an efficient algorithm to compute our certified robustness guarantees. We also empirically evaluate PointGuard on ModelNet40 and ScanNet benchmark datasets.

关键词： computer vision Three-dimensional displays Smoothing methods Computational modeling Grasping Benchmark testing Prediction algorithms

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Geometry Transfer for Stylizing Radiance Fields

Geometry Transfer for Stylizing Radiance Fields

引用

conference on computer vision and pattern recognition (cvpr)

作者： Hyunyoung Jung Seonghyeon Nam Nikolaos Sarafianos Sungjoo Yoo Alexander Sorkine-Hornung Rakesh Ranjan Seoul National University Meta Reality Labs

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Shape and geometric patterns are essential in defining stylistic identity. However, current 3D style transfer methods predominantly focus on transferring colors and textures, often overlooking geometric aspects. In this paper, we introduce Geometry Transfer, a novel method that leverages geometric deformation for 3D style transfer. This technique employs depth maps to extract a style guide, subsequently applied to stylize the geometry of radiance fields. Moreover, we propose new techniques that utilize geometric cues from the 3D scene, thereby enhancing aesthetic expressiveness and more accurately reflecting intended styles. Our extensive experiments show that Geometry Transfer enables a broader and more expressive range of stylizations, thereby significantly expanding the scope of 3D style transfer.

关键词： Geometry computer vision Three-dimensional displays Shape Deformation Buildings Color

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Interventional Video Grounding with Dual Contrastive Learning

Interventional Video Grounding with Dual Contrastive Learnin...

引用

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

作者： Nan, Guoshun Qiao, Rui Xiao, Yao Liu, Jun Leng, Sicong Zhang, Hao Lu, Wei Singapore Univ Technol & Design StatNLP Res Grp Singapore Singapore Shanghai Jiao Tong Univ Shanghai Peoples R China Singapore Univ Technol & Design Informat Syst Technol & Design Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore ASTAR Inst High Performance Comp Singapore Singapore

ISBN: (纸本)9781665445092

Video grounding aims to localize a moment from an untrimmed video for a given textual query. Existing approaches focus more on the alignment of visual and language stimuli with various likelihood-based matching or regression strategies, i.e., P(Y vertical bar X). Consequently, these models may suffer from spurious correlations between the language and video features due to the selection bias of the dataset. 1) To uncover the causality behind the model and data, we first propose a novel paradigm from the perspective of the causal inference, i.e., interventional video grounding (IVG) that leverages backdoor adjustment to deconfound the selection bias based on structured causal model (SCM) and do-calculus P(Y vertical bar do(X)). Then, we present a simple yet effective method to approximate the unobserved confounder as it cannot be directly sampled from the dataset. 2) Meanwhile, we introduce a dual contrastive learning approach (DCL) to better align the text and video by maximizing the mutual information (MI) between query and video clips, and the MI between start/end frames of a target moment and the others within a video to learn more informative visual representations. Experiments on three standard benchmarks show the effectiveness of our approaches.

关键词： Visualization Correlation Grounding Benchmark testing Knowledge discovery Data models pattern recognition

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Rethinking Inductive Biases for Surface Normal Estimation

Rethinking Inductive Biases for Surface Normal Estimation

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conference on computer vision and pattern recognition (cvpr)

作者： Gwangbin Bae Andrew J. Davison Dyson Robotics Lab Imperial College London

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Despite the growing demand for accurate surface nor- mal estimation models, existing methods use general- purpose dense prediction models, adopting the same induc- tive biases as other tasks. In this paper, we discuss the inductive biases needed for surface normal estimation and propose to (1) utilize the per-pixel ray direction and (2) en- code the relationship between neighboring surface normals by learning their relative rotation. The proposed method can generate crisp - yet, piecewise smooth - predictions for challenging in-the-wild images of arbitrary resolution and aspect ratio. Compared to a recent ViT-based state- of-the-art model, our method shows a stronger generalization ability, despite being trained on an orders of magni- tude smaller dataset. The code is available at https://***/baegwangbin/DSINE.

关键词： Convolutional codes computer vision Three-dimensional displays Image resolution Image coding Computational modeling Estimation

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