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共 23240 条记录，以下是211-220 订阅

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TIGER: Time-Varying Denoising Model for 3D Point Cloud Generation with Diffusion Process

TIGER: Time-Varying Denoising Model for 3D Point Cloud Gener...

引用

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

作者： Ren, Zhiyuan Kim, Minchul Liu, Feng Liu, Xiaoming Michigan State Univ E Lansing MI 48824 USA

ISBN: (纸本)9798350353006

Recently, diffusion models have emerged as a new powerful generative method for 3D point cloud generation tasks. However, few works study the effect of the architecture of the diffusion model in the 3D point cloud, resorting to the typical UNet model developed for 2D images. Inspired by the wide adoption of Transformers, we study the complementary role of convolution (from UNet) and attention (from Transformers). We discover that their respective importance change according to the timestep in the diffusion process. At early stage, attention has an out-sized influence because Transformers are found to generate the overall shape more quickly, and at later stages when adding fine detail, convolution starts having a larger impact on the generated point cloud's local surface quality. In light of this observation, we propose a time-varying two-stream denoising model combined with convolution layers and transformer blocks. We generate an optimizable mask from each timestep to reweigh global and local features, obtaining time-varying fused features. Experimentally, we demonstrate that our proposed method quantitatively outperforms other state-of-the-art methods regarding visual quality and diversity. Code is avaiable https://***/Zhiyuan-R/Tiger-Diffusion.

关键词： 3D vision Diffusion Model Generative Model Point Cloud ShapeNet

来源：评论

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Low-Resource vision Challenges for Foundation Models

Low-Resource Vision Challenges for Foundation Models

引用

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

作者： Zhang, Yunhua Doughty, Hazel Snoek, Cees G. M. Univ Amsterdam Amsterdam Netherlands Leiden Univ Leiden Netherlands

ISBN: (纸本)9798350353006

Low-resource settings are well-established in natural language processing, where many languages lack sufficient data for deep learning at scale. However, low-resource problems are under-explored in computer vision. In this paper, we address this gap and explore the challenges of low-resource image tasks with vision foundation models. We first collect a benchmark of genuinely low-resource image data, covering historic maps, circuit diagrams, and mechanical drawings. These low-resource settings all share three challenges: data scarcity, fine-grained differences, and the distribution shift from natural images to the specialized domain of interest. While existing foundation models have shown impressive generalizability, we find they cannot transfer well to our low-resource tasks. To begin to tackle the challenges of low-resource vision, we introduce one simple baseline per challenge. Specifically, we i) enlarge the data space by generative models, ii) adopt the best sub-kernels to encode local regions for fine-grained difference discovery and iii) learn attention for specialized domains. Experiments on our three low-resource tasks demonstrate our proposals already provide a better baseline than transfer learning, data augmentation, and fine-grained methods. This highlights the unique characteristics and challenges of low-resource vision for foundation models that warrant further investigation. Project page: https://***/Low-Resource-vision/.

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Robust Image Denoising through Adversarial Frequency Mixup

Robust Image Denoising through Adversarial Frequency Mixup

引用

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

作者： Ryou, Donghun Ha, Inju Yoo, Hyewon Kim, Dongwan Han, Bohyung Seoul Natl Univ ECE Comp Vis Lab Seoul South Korea Seoul Natl Univ IPAI Seoul South Korea

ISBN: (纸本)9798350353013;9798350353006

Image denoising approaches based on deep neural networks often struggle with overfitting to specific noise distributions present in training data. This challenge persists in existing real-world denoising networks, which are trained using a limited spectrum of real noise distributions, and thus, show poor robustness to out-of-distribution real noise types. To alleviate this issue, we develop a novel training framework called Adversarial Frequency Mixup (AFM). AFM leverages mixup in the frequency domain to generate noisy images with distinctive and challenging noise characteristics, all the while preserving the properties of authentic real-world noise. Subsequently, incorporating these noisy images into the training pipeline enhances the denoising network's robustness to variations in noise distributions. Extensive experiments and analyses, conducted on a wide range of real noise benchmarks demonstrate that denoising networks trained with our proposed framework exhibit significant improvements in robustness to unseen noise distributions. The code is available at https://***/dhryougit/AFM.

关键词： Image Denoising Low-level vision Robustness

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Instance-Aware Group Quantization for vision Transformers

Instance-Aware Group Quantization for Vision Transformers

引用

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

作者： Jaehyeon, Moon Kim, Dohyung Cheon, Junyong Ham, Bumsub Yonsei Univ Seoul South Korea Articron Bogota Colombia

ISBN: (纸本)9798350353006

Post-training quantization (PTQ) is an efficient model compression technique that quantizes a pretrained full-precision model using only a small calibration set of unlabeled samples without retraining. PTQ methods for convolutional neural networks (CNNs) provide quantization results comparable to full-precision counterparts. Directly applying them to vision transformers (ViTs), however, incurs severe performance degradation, mainly due to the differences in architectures between CNNs and ViTs. In particular, the distribution of activations for each channel vary drastically according to input instances, making PTQ methods for CNNs inappropriate for ViTs. To address this, we introduce instance-aware group quantization for ViTs (IGQ-ViT). To this end, we propose to split the channels of activation maps into multiple groups dynamically for each input instance, such that activations within each group share similar statistical properties. We also extend our scheme to quantize softmax attentions across tokens. In addition, the number of groups for each layer is adjusted to minimize the discrepancies between predictions from quantized and full-precision models, under a bit-operation (BOP) constraint. We show extensive experimental results on image classification, object detection, and instance segmentation, with various transformer architectures, demonstrating the effectiveness of our approach.

关键词： Post-training quantization ViT

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Towards Better vision-Inspired vision-Language Models

Towards Better Vision-Inspired Vision-Language Models

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

作者： Cao, Yun-Hao Ji, Kaixiang Huang, Ziyuan Zheng, Chuanyang Liu, Jiajia Wang, Jian Chen, Jingdong Yang, Ming Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China Ant Grp Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9798350353006

vision-language (VL) models have achieved unprecedented success recently, in which the connection module is the key to bridge the modality gap. Nevertheless, the abundant visual clues are not sufficiently exploited in most existing methods. On the vision side, most existing approaches only use the last feature of the vision tower, without using the low-level features. On the language side, most existing methods only introduce shallow vision-language interactions. In this paper, we present a vision-inspired vision-language connection module, dubbed as VIVL, which efficiently exploits the vision cue for VL models. To take advantage of the lower-level information from the vision tower, a feature pyramid extractor (FPE) is introduced to combine features from different intermediate layers, which enriches the visual cue with negligible parameters and computation overhead. To enhance VL interactions, we propose deep vision-conditioned prompts (DVCP) that allows deep interactions of vision and language features efficiently. Our VIVL exceeds the previous state-of- the-art method by 18.1 CIDEr when training from scratch on the COCO caption task, which greatly improves the data efficiency. When used as a plug-in module, VIVL consistently improves the performance for various backbones and VL frameworks, delivering new state-of-the-art results on multiple benchmarks, e.g., NoCaps and VQAv2.

关键词： deep learning vision-language models feature pyramid deep prompt

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Potential Risk Localization via Weak Labeling out of Blind Spot

Potential Risk Localization via Weak Labeling out of Blind S...

引用

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

作者： Shimomura, Kota Hirakawa, Tsubasa Yamashita, Takayoshi Fujiyoshi, Hironobu Chubu Univ Kasugai Aichi Japan

ISBN: (纸本)9798350365474

Achieving fully autonomous driving requires not only understanding the current surrounding conditions but also predicting how objects that could lead to potential risks may change in the future. Predicting potential risk regions, especially where pedestrians or vehicles might suddenly appear, is crucial for safe autonomous driving and accident avoidance. Constructing datasets annotated with potential risk regions is costly. Therefore, conventional methods have proposed blind spot estimation using depth maps or segmentation masks through automatic labeling. However, these methods are limited in applicability due to their reliance on camera parameters or point clouds. In this study, we propose a method to automatically generate labels from depth maps and segmentation masks and estimate potential risk regions in 2D. Our automatic labeling algorithm relies solely on images, making it applicable to all onboard camera datasets. To demonstrate the effectiveness of our approach, we define regions where pedestrians or vehicles might emerge from blind spots as potential risk regions and annotate them to create a new dataset extended with potential risk region annotations. Experiments using the Cityscapes Dataset show that weakly training with labels generated by our proposed method achieves equal or superior accuracy compared with supervised training with manually annotated ground truth (GT). Furthermore, experiments using the Mapillary Vistas Dataset and BDD100K Dataset demonstrate the versatility of our approach.

关键词： Advanced Driver-Assistance Systems Autonomous Driving computer vision Deep Learning

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Any-Shift Prompting for Generalization over Distributions

Any-Shift Prompting for Generalization over Distributions

引用

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

作者： Xiao, Zehao Shen, Jiayi Derakhshani, Mohammad Mandi Liao, Shengcai Snoek, Cees G. M. Univ Amsterdam Amsterdam Netherlands Core42 Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350353006

Image-language models with prompt learning have shown remarkable advances in numerous downstream vision tasks. Nevertheless, conventional prompt learning methods overfit their training distribution and lose the generalization ability on test distributions. To improve generalization across various distribution shifts, we propose any-shift prompting: a general probabilistic inference framework that considers the relationship between training and test distributions during prompt learning. We explicitly connect training and test distributions in the latent space by constructing training and test prompts in a hierarchical architecture. Within this frame-work, the test prompt exploits the distribution relationships to guide the generalization of the CLIP image-language model from training to any test distribution. To effectively encode the distribution information and their relationships, we further introduce a transformer inference network with a pseudo-shift training mechanism. The network generates the tailored test prompt with both training and test information in a feedforward pass, avoiding extra training costs at test time. Extensive experiments on twenty-three datasets demonstrate the effectiveness of any-shift prompting on the generalization over various distribution shifts.

关键词： distribution shifts out-of-distribution generalization prompt learning vision-language

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ShapeWalk: Compositional Shape Editing through Language-Guided Chains

ShapeWalk: Compositional Shape Editing through Language-Guid...

引用

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

作者： Slim, Habib Elhoseiny, Mohamed KAUST Thuwal Saudi Arabia

ISBN: (纸本)9798350353006

Editing 3D shapes through natural language instructions is a challenging task that requires the comprehension of both language semantics and fine-grained geometric details. To bridge this gap, we introduce ShapeWalk, a carefully designed synthetic dataset designed to advance the field of language-guided shape editing. The dataset consists of 158K unique shapes connected through 26K edit chains, with an average length of 14 chained shapes. Each consecutive pair of shapes is associated with precise language instructions describing the applied edits. We synthesize edit chains by reconstructing and interpolating shapes sampled from a realistic CAD-designed 3D dataset in the parameter space of the GeoCode shape program. We leverage rule-based methods and language models to generate accurate and realistic natural language prompts corresponding to each edit. To illustrate the practicality of our contribution, we train neural editor modules in the latent space of shape autoencoders, and demonstrate the ability of our dataset to enable a variety of language-guided shape edits. Finally, we introduce multi-step editing metrics to benchmark the capacity of our models to perform recursive shape edits. We hope that our work will enable further study of compositional language-guided shape editing, and finds application in 3D CAD design and interactive modeling.

关键词： 3D language editing 3D vision compositionality

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The Audio-Visual Conversational Graph: From an Egocentric-Exocentric Perspective

The Audio-Visual Conversational Graph: From an Egocentric-Ex...

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

作者： Jia, Wenqi Liu, Miao Jiang, Hao Ananthabhotla, Ishwarya Rehg, James M. Ithapu, Vamsi Krishna Gao, Ruohan Georgia Tech Atlanta GA 30332 USA Meta Real Labs Menlo Pk CA 94025 USA UIUC Champaign IL USA Meta GenAI Menlo Pk CA USA

ISBN: (纸本)9798350353006

In recent years, the thriving development of research related to egocentric videos has provided a unique perspective for the study of conversational interactions, where both visual and audio signals play a crucial role. While most prior work focus on learning about behaviors that directly involve the camera wearer, we introduce the Ego-Exocentric Conversational Graph Prediction problem, marking the first attempt to infer exocentric conversational interactions from egocentric videos. We propose a unified multi-modal framework-Audio-Visual Conversational Attention (AV-CONV), for the joint prediction of conversation behaviors-speaking and listening-for both the camera wearer as well as all other social partners present in the egocentric video. Specifically, we adopt the self-attention mechanism to model the representations across-time, across-subjects, and across-modalities. To validate our method, we conduct experiments on a challenging egocentric video dataset that includes multi-speaker and multi-conversation scenarios. Our results demonstrate the superior performance of our method compared to a series of baselines. We also present detailed ablation studies to assess the contribution of each component in our model. Check our Project Page.

关键词： egocentric vision Multi-modal learning social ai

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Cross-view and Cross-pose Completion for 3D Human Understanding

Cross-view and Cross-pose Completion for 3D Human Understand...

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

作者： Armando, Matthieu Galaaoui, Salma Baradel, Fabien Lucas, Thomas Leroy, Vincent Bregier, Romain Weinzaepfel, Philippe Rogez, Gregory NAVER LABS Europe Meylan France

ISBN: (纸本)9798350353013;9798350353006

Human perception and understanding is a major domain of computer vision which, like many other vision subdomains recently, stands to gain from the use of large models pre-trained on large datasets. We hypothesize that the most common pre-training strategy of relying on general purpose, object-centric image datasets such as ImageNet, is limited by an important domain shift. On the other hand, collecting domain-specific ground truth such as 2D or 3D labels does not scale well. Therefore, we propose a pre-training approach based on self-supervised learning that works on human-centric data using only images. Our method uses pairs of images of humans: the first is partially masked and the model is trained to reconstruct the masked parts given the visible ones and a second image. It relies on both stereoscopic (cross-view) pairs, and temporal (cross-pose) pairs taken from videos, in order to learn priors about 3D as well as human motion. We pre-train a model for body-centric tasks and one for hand-centric tasks. With a generic transformer architecture, these models outperform existing self-supervised pre-training methods on a wide set of human-centric downstream tasks, and obtain state-of-the-art performance for instance when fine-tuning for model-based and model-free human mesh recovery.

关键词： hand-centric human-centric pretraining representation learning vision tranformer

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