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Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds

Back-tracing Representative Points for Voting-based 3D Objec...

引用

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

作者： Cheng, Bowen Sheng, Lu Shi, Shaoshuai Yang, Ming Xu, Dong Beihang Univ Coll Software Beijing Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (纸本)9781665445092

3D object detection in point clouds is a challenging vision task that benefits various applications for understanding the 3D visual world. Lots of recent research focuses on how to exploit end-to-end trainable Hough voting for generating object proposals. However, the current voting strategy can only receive partial votes from the surfaces of potential objects together with severe outlier votes from the cluttered backgrounds, which hampers full utilization of the information from the input point clouds. Inspired by the back-tracing strategy in the conventional Hough voting methods, in this work, we introduce a new 3D object detection method, named as Back-tracing Representative Points Network (BRNet), which generatively back-traces the representative points from the vote centers and also revisits complementary seed points around these generated points, so as to better capture the fine local structural features surrounding the potential objects from the raw point clouds. Therefore, this bottom-up and then top-down strategy in our BRNet enforces mutual consistency between the predicted vote centers and the raw surface points and thus achieves more reliable and flexible object localization and class prediction results. Our BRNet is simple but effective, which significantly outperforms the state-of-the-art methods on two large-scale point cloud datasets, ScanNet V2 (+7.5% in terms of mAP@0.50) and SUN RGB-D (+4.7% in terms of mAP@0.50), while it is still lightweight and efficient.

关键词： Location awareness Visualization Three-dimensional displays Object detection Feature extraction pattern recognition Proposals

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Driving Everywhere with Large Language Model Policy Adaptation

Driving Everywhere with Large Language Model Policy Adaptati...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Boyi Li Yue Wang Jiageng Mao Boris Ivanovic Sushant Veer Karen Leung Marco Pavone NVIDIA University of Southern California University of Washington Stanford University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Adapting driving behavior to new environments, customs, and laws is a longstanding problem in autonomous driving, precluding the widespread deployment of autonomous vehicles (AVs). In this paper, we present LLaDA, a simple yet powerful tool that enables human drivers and autonomous vehicles alike to drive everywhere by adapting their tasks and motion plans to traffic rules in new locations. LLaDA achieves this by leveraging the impressive zero-shot generalizability of large language models (LLMs) in interpreting the traffic rules in the local driver handbook. Through an extensive user study, we show that LLaDA's instructions are useful in disambiguating in-the-wild unexpected situations. We also demonstrate LLaDA's ability to adapt AV motion planning policies in real-world datasets; LLaDA outperforms baseline planning approaches on all our metrics. Please check our website for more details: LLaDA.

关键词： Measurement computer vision Video on demand Accuracy Large language models Planning pattern recognition

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Filter Distribution Templates in Convolutional Networks for Image Classification Tasks

Filter Distribution Templates in Convolutional Networks for ...

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

作者： Izquierdo-Cordova, Ramon Mayol-Cuevas, Walterio Univ Bristol Dept Comp Sci Bristol Avon England

ISBN: (纸本)9781665448994

Neural network designers have reached progressive accuracy by increasing models depth, introducing new layer types and discovering new combinations of layers. A common element in many architectures is the distribution of the number of filters in each layer. Neural network models keep a pattern design of increasing filters in deeper layers such as those in LeNet, VGG, ResNet, MobileNet and even in automatic discovered architectures such as NASNet. It remains unknown if this pyramidal distribution of filters is the best for different tasks and constrains. In this work we present a series of modifications in the distribution of filters in three popular neural network models and their effects in accuracy and resource consumption. Results show that by applying this approach, some models improve up to 8.9% in accuracy showing reductions in parameters up to 54%.

关键词： Computational modeling Neural networks Memory management Manuals Data models pattern recognition Convolutional neural networks

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Selectively Informative Description can Reduce Undesired Embedding Entanglements in Text-to-Image Personalization

Selectively Informative Description can Reduce Undesired Emb...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Jimyeong Kim Jungwon Park Wonjong Rhee Department of Intelligence and Information Seoul National University IPAI Seoul National University RICS Seoul National University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In text-to-image personalization, a timely and crucial challenge is the tendency of generated images overfitting to the biases present in the reference images. We initiate our study with a comprehensive categorization of the biases into background, nearby-object, tied-object, substance (in style re-contextualization), and pose biases. These biases manifest in the generated images due to their entanglement into the subject embedding. This undesired embedding entanglement not only results in the reflection of biases from the reference images into the generated images but also notably diminishes the alignment of the generated images with the given generation prompt. To address this challenge, we propose SID (Selectively Informative Description), a text description strategy that deviates from the prevalent approach of only characterizing the subject's class identification. SID is generated utilizing multimodal GPT-4 and can be seamlessly integrated into optimization-based models. We present comprehensive experimental results along with analyses of cross-attention maps, subject-alignment, non-subject-disentanglement, and text-alignment.

关键词： computer vision Text to image Reflection pattern recognition

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Federated Online Adaptation for Deep Stereo

Federated Online Adaptation for Deep Stereo

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conference on computer vision and pattern recognition (cvpr)

作者： Matteo Poggi Fabio Tosi Department of Computer Science and Engineering (DISI) Advanced Research Center on Electronic System (ARCES) University of Bologna Italy

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We introduce a novel approach for adapting deep stereo networks in a collaborative manner. By building over principles of federated learning, we develop a distributed framework allowing for demanding the optimization process to a number of clients deployed in different environments. This makes it possible, for a deep stereo network running on resourced-constrained devices, to capitalize on the adaptation process carried out by other instances of the same architecture, and thus improve its accuracy in challenging environments even when it cannot carry out adaptation on its own. Experimental results show how federated adaptation performs equivalently to on-device adaptation, and even better when dealing with challenging environments.

关键词： Performance evaluation Adaptation models Accuracy Costs Federated learning Computational modeling Hardware

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Concept Weaver: Enabling Multi-Concept Fusion in Text-to-Image Models

Concept Weaver: Enabling Multi-Concept Fusion in Text-to-Ima...

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conference on computer vision and pattern recognition (cvpr)

作者： Gihyun Kwon Simon Jenni Dingzeyu Li Joon-Young Lee Jong Chul Ye Fabian Caba Heilbron KAIST Adobe

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

While there has been significant progress in customizing text-to-image generation models, generating images that combine multiple personalized concepts remains challenging. In this work, we introduce Concept Weaver, a method for composing customized text-to-image diffusion models at inference time. Specifically, the method breaks the process into two steps: creating a template image aligned with the semantics of input prompts, and then personalizing the template using a concept fusion strategy. The fusion strategy incorporates the appearance of the target concepts into the template image while retaining its structural details. The results indicate that our method can generate multiple custom concepts with higher identity fidelity compared to alternative approaches. Furthermore, the method is shown to seamlessly handle more than two concepts and closely follow the semantic meaning of the input prompt without blending appearances across different subjects.

关键词： computer vision Fuses Semantics Text to image Diffusion models pattern recognition Optimization

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CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching

CFNet: Cascade and Fused Cost Volume for Robust Stereo Match...

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

作者： Shen, Zhelun Dai, Yuchao Rao, Zhibo Northwestern Polytech Univ Xian Peoples R China Peking Univ Beijing Peoples R China

ISBN: (纸本)9781665445092

Recently, the ever-increasing capacity of large-scale annotated datasets has led to profound progress in stereo matching. However, most of these successes are limited to a specific dataset and cannot generalize well to other datasets. The main difficulties lie in the large domain differences and unbalanced disparity distribution across a variety of datasets, which greatly limit the real-world applicability of current deep stereo matching models. In this paper, we propose CFNet, a Cascade and Fused cost volume based network to improve the robustness of the stereo matching network. First, we propose a fused cost volume representation to deal with the large domain difference. By fusing multiple low-resolution dense cost volumes to enlarge the receptive field, we can extract robust structural representations for initial disparity estimation. Second, we propose a cascade cost volume representation to alleviate the unbalanced disparity distribution. Specifically, we employ a variance-based uncertainty estimation to adaptively adjust the next stage disparity search space, in this way driving the network progressively prune out the space of unlikely correspondences. By iteratively narrowing down the disparity search space and improving the cost volume resolution, the disparity estimation is gradually refined in a coarse-to-fine manner. When trained on the same training images and evaluated on KITTI, ETH3D, and Middlebury datasets with the fixed model parameters and hyperparameters, our proposed method achieves the state-of-the-art overall performance and obtains the 1st place on the stereo task of Robust vision Challenge 2020.

关键词： Training computer vision Costs Uncertainty Image resolution Codes Estimation

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FADES: Fair Disentanglement with Sensitive Relevance

FADES: Fair Disentanglement with Sensitive Relevance

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conference on computer vision and pattern recognition (cvpr)

作者： Taeuk Jang Xiaoqian Wang Purdue University West Lafayette IN USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Learning fair representation in deep learning is essential to mitigate discriminatory outcomes and enhance trustworthiness. However, previous research has been commonly established on inappropriate assumptions prone to unrealistic counterfactuals and performance degradation. Although some proposed alternative approaches, such as employing correlation-aware causal graphs or proxies for mutual information, these methods are less practical and not applicable in general. In this work, we propose FAir DisEntanglement with Sensitive relevance (FADES), a novel approach that leverages conditional mutual information from the information theory perspective to address these challenges. We employ sensitive relevant code to direct correlated information between target labels and sensitive attributes by imposing conditional independence, allowing better separation of the features of interest in the latent space. Utilizing an intuitive disentangling approach, FADES consistently achieves superior performance and fairness both quantitatively and qualitatively with its straightforward structure. Specifically, the proposed method outperforms existing works in downstream classification and counterfactual generations on various benchmarks.

关键词： Training Degradation Deep learning computer vision Codes Benchmark testing pattern recognition

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Learning to Aggregate and Personalize 3D Face from In-the-Wild Photo Collection

Learning to Aggregate and Personalize 3D Face from In-the-Wi...

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

作者： Zhang, Zhenyu Ge, Yanhao Chen, Renwang Tai, Ying Yan, Yan Yang, Jian Wang, Chengjie Li, Jilin Huang, Feiyue Tencent Youtu Lab Shanghai Peoples R China Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781665445092

Non-parametric face modeling aims to reconstruct 3D face only from images without shape assumptions. While plausible facial details are predicted, the models tend to over-depend on local color appearance and suffer from ambiguous noise. To address such problem, this paper presents a novel Learning to Aggregate and Personalize (LAP) framework for unsupervised robust 3D face modeling. Instead of using controlled environment, the proposed method implicitly disentangles ID-consistent and scenespecific face from unconstrained photo set. Specifically, to learn ID-consistent face, LAP adaptively aggregates intrinsic face factors of an identity based on a novel curriculum learning approach with relaxed consistency loss. To adapt the face for a personalized scene, we propose a novel attribute-refining network to modify ID-consistent face with target attribute and details. Based on the proposed method, we make unsupervised 3D face modeling benefit from meaningful image facial structure and possibly higher resolutions. Extensive experiments on benchmarks show LAP recovers superior or competitive face shape and texture, compared with state-of-the-art (SOTA) methods with or without prior and supervision.

关键词： Solid modeling Adaptation models Three-dimensional displays Image resolution Shape Image color analysis Face recognition

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Unsupervised Multi-source Domain Adaptation Without Access to Source Data

Unsupervised Multi-source Domain Adaptation Without Access t...

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

作者： Ahmed, Sk Miraj Raychaudhuri, Dripta S. Paul, Sujoy Oymak, Samet Roy-Chowdhury, Amit K. Univ Calif Riverside Riverside CA 92521 USA Google Res Mountain View CA USA UC Riverside Riverside CA USA

ISBN: (纸本)9781665445092

Unsupervised Domain Adaptation (UDA) aims to learn a predictor model for an unlabeled domain by transferring knowledge from a separate labeled source domain. However, most of these conventional UDA approaches make the strong assumption of having access to the source data during training, which may not be very practical due to privacy, security and storage concerns. A recent line of work addressed this problem and proposed an algorithm that transfers knowledge to the unlabeled target domain from a single source model without requiring access to the source data. However, for adaptation purposes, if there are multiple trained source models available to choose from, this method has to go through adapting each and every model individually, to check for the best source. Thus, we ask the question: can we find the optimal combination of source models, with no source data and without target labels, whose performance is no worse than the single best source? To answer this, we propose a novel and efficient algorithm which automatically combines the source models with suitable weights in such a way that it performs at least as good as the best source model. We provide intuitive theoretical insights to justify our claim. Furthermore, extensive experiments are conducted on several benchmark datasets to show the effectiveness of our algorithm, where in most cases, our method not only reaches best source accuracy but also outperforms it.

关键词： Training Adaptation models Data privacy computer vision Predictive models Benchmark testing Data models

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