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检索条件"任意字段=7th Chinese Conference on Pattern Recognition and Computer Vision"

共 2177 条记录，以下是51-60 订阅

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RefineStyle: Dynamic Convolution Refinement for StyleGAN 7th

RefineStyle: Dynamic Convolution Refinement for StyleGAN

引用

7th chinese conference on pattern recognition and computer vision

作者： Xia, Siwei Hu, Xueqi Sun, Li Li, Qingli East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China

ISBN: (纸本)9789819786916;9789819786923

In StyleGAN, convolution kernels are shaped by both static parameters shared across images and dynamic modulation factors w(+) is an element of W+ specific to each image. therefore, W+ space is often used for image inversion and editing. However, pre-trained model struggles with synthesizing out-of-domain images due to the limited capabilities of W+ and its resultant kernels, necessitating full fine-tuning or adaptation through a complex hypernetwork. this paper proposes an efficient refining strategy for dynamic kernels. the key idea is to modify kernels by low-rank residuals, learned from input image or domain guidance. these residuals are generated by matrix multiplication between two sets of tokens with the same number, which controls the complexity. We validate the refining scheme in image inversion and domain adaptation. In the former task, we design grouped transformer blocks to learn these token sets by one- or two-stage training. In the latter task, token sets are directly optimized to support synthesis in the target domain while preserving original content. Extensive experiments show that our method achieves low distortions for image inversion and high quality for out-of-domain editing.

关键词： computer vision Generative models GAN inversion Domain adaptation

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Discriminative-Guided Diffusion-Based Self-supervised Monocular Depth Estimation 7th

Discriminative-Guided Diffusion-Based Self-supervised Monocu...

引用

7th chinese conference on pattern recognition and computer vision

作者： Liu, Runze Zhang, Guanghui Zhu, Dongchen Wang, Lei Zhang, Xiaolin Li, Jiamao Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Bion Vis Syst Lab Shanghai 200050 Peoples R China ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Sci & Technol China Hefei 230027 Anhui Peoples R China

ISBN: (纸本)9789819785070;9789819785087

Self-supervised monocular depth estimation is a critical task in computer vision. Existing methods can be typically categorized into discriminative-based and generative-based methods according to different data modeling approaches. Discriminative-based methods are distinguished by high accuracy, while generative-based methods are notable for superior robustness. Given that images captured in real-world scenarios are inevitably influenced by various external factors, it is essential to develop a robust and accurate depth estimation algorithm. However, there is limited research on the balance of robustness and accuracy by exploring the interactions between discriminative and generative networks. We propose a generative diffusion-based self-supervised monocular depth estimation algorithm guided by discriminative networks and incorporate a depth interaction constraint. We utilize discriminative networks to optimize image-guided information for the denoising process within the diffusion model. this approach seamlessly combines great robustness with high accuracy. Additionally, to reduce the impact of low texture regions on the reprojection photometric loss, we design a texture-aware discriminatory mask module. this module strengthens the constraint capability of the photometric consistency. We conduct experiments on the KITTI and Make3D datasets. the results demonstrate that our method successfully balances accuracy and robustness.

关键词： Monocular depth estimation Self-supervised learning Diffusion model

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KA-Seg: Improving LiDAR Point Cloud 7th

KA-Seg: Improving LiDAR Point Cloud

引用

7th chinese conference on pattern recognition and computer vision

作者： Cu, Kaining Wang, Xiaoyang Wang, Lu Cheng, Jun Northeastern Univ Shenyang Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China

ISBN: (纸本)9789819787913;9789819787920

In the field of autonomous driving, profound scene understanding is crucial, and semantic segmentation of LiDAR point clouds plays a key role in this context. A prevalent issue in point cloud datasets is the imbalance in class distribution. To address this, we introduce the InstanceAug data augmentation pipeline, which balances the class distribution by duplicating instances within scenes. this approach significantly enhances the robustness of our model. Deep learning models for point cloud processing often use sparse convolution for efficiency, but this limits feature transmission and the receptive field. Building on the strengthened dataset, we present KA-Seg, an innovative attention-based framework. KA-Seg refines sparse voxel features to further enhance robustness. Its core feature is an attention mechanism with super voxel partitioning and key point subsampling, which greatly improves the model's ability to identify complex spatial patterns and focus on important voxel regions. Inspired by Transformer architecture, KA-Seg utilizes learnable key point sampling for global feature querying, expanding the model's spatial understanding. this method augments spatial information processing across the point cloud and achieves a 1.3% higher mean intersection over union (mIoU) on the test set compared to the baseline model. Our code is publicly available at https://***/cvkdnk/kaseg.

关键词： Global attention sampling Sparse convolution Point clouds Semantic segmentation

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Empathizing Before Generation: A Double-Layered Framework for Emotional Support LLM 7th

Empathizing Before Generation: A Double-Layered Framework fo...

引用

7th chinese conference on pattern recognition and computer vision

作者： Zhu, Jiahao Jiang, Zijian Zhou, Boyu Su, Jionglong Zhang, Naming Li, Zhihao Shantou Univ Shantou Peoples R China Chinese Univ Hong Kong Ma Liu Shui Hong Kong Peoples R China Mind Win Heart Robot Co Ltd Shenzhen Peoples R China Xian Jiaotong Liverpool Univ Suzhou Peoples R China

ISBN: (纸本)9789819784899;9789819784905

Large Language Models (LLMs) have found extensive use across different applications due to its diverse capabilities and proficiency in executing instructions. In the case of chatbots, they are frequently required to show empathy when used in the context of emotional support. However, to date their performance is still not satisfactory due to the lack of deep understanding of user related issues. Hence, we introduce the Empathizing Before Generation (EBG), a two-step learning framework that allows LLMs to analyze the chain of thought (COT) prior to generating a response. this model also enables the inference of the 24 emotions conveyed in the user's text as well as facilitates the generation of empathetic, high-quality and appropriate responses. We create a COT version of the dataset for sentiment inference by utilizing a publicly accessible sentiment dialogue. this dataset is then used as support for the training of two layers of EBG. Experiments indicate that models integrated with the EBG outperform other models in demonstrating empathy, with 98.2% and 92.8% accuracy in emotional attributes and labels respectively. Additionally, there is a notable enhancement in the model's capacity to comprehend COT instructions, infer emotions, and generate answers that are more satisfactory than other models.

关键词： Large language model Emotional support Chain of thought

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Balancing Complementarity and Consistency via Delayed Activation in Incomplete Multi-view Clustering 7th

Balancing Complementarity and Consistency via Delayed Activa...

引用

7th chinese conference on pattern recognition and computer vision

作者： Li, Bo Xu, Zhiwei Yun, Jing Wang, Jiatai Inner Mongolia Univ Technol Coll Data Sci & Applicat Hohhot Peoples R China Haihe Lab ITAI Tianjin Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9789819787913;9789819787920

this paper study one challenging issue in incomplete multi-view clustering, where valuable complementary information from other views is always ignored. To be specific, we propose a framework that effectively balances Complementarity and Consistency information in Incomplete Multi-view Clustering (CoCo-IMC). Specifically, we design a dual network of delayed activation, which achieves a balance of complementarity and consistency among different views. the delayed activation could enriches the complementarity information that was ignored during consistency learning. then, we recover the incomplete information and enhance the consistency learning by minimizing the conditional entropy and maximizing the mutual information across different views. this could be the first theoretical attempt to incorporate delayed activation into incomplete data recovery and the balance of complementarity and consistency. We have proved the effectiveness of CoCo-IMC in extensive comparative experiments with 12 state-of-the-art baselines on four publicly available datasets.

关键词： Incomplete Multi-view Clustering Contrastive Learning Complementarity Consistency Balance

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Accelerating Domain Adaptation with Cascaded Adaptive vision Transformer 7th

Accelerating Domain Adaptation with Cascaded Adaptive Vision...

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7th chinese conference on pattern recognition and computer vision

作者： Jiang, Qilin Cui, Chaoran Zhang, Chunyun Zhen, Yongrui Gong, Shuai Liu, Ziyi Meng, Fan'an Zhao, Hongyan Shandong Univ Finance & Econ Jinan Peoples R China

ISBN: (纸本)9789819784868;9789819784875

Domain adaptation (DA) aims to transfer knowledge from labeled source domains to unlabeled target domains, addressing the challenge of model generalization when there is a distribution mismatch between training and testing data. While many vision Transformer (ViT)-based methods have been developed for DA, they focus primarily on improving accuracy, with less emphasis on accelerating inference on unlabeled target domains. In this paper, we propose a novel method named Cascaded Adaptive vision Transformer (CAViT), which dynamically adjusts token counts for each input image by cascading multiple transformers with increasing tokens. During testing, "easier" images exit early, while "harder" images are processed further until confident predictions are achieved. We further enhance domain adversarial learning by incorporating a token-level domain discriminator in the attention layer, which assigns distinct weights to different patch tokens. this enables the network to learn features with cross-domain transferability and discriminative capabilities, achieving effective feature alignment. Experimental results demonstrate that our method not only improves accuracy but also significantly reduces computational costs, as evidenced by results on three benchmark datasets.

关键词： Domain adaptation Transfer learning Adaptive inference Resource efficiency

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End-To-End High-Quality Transformer Object Detection Model Applied to Human Head Detection 7th

End-To-End High-Quality Transformer Object Detection Model A...

引用

7th chinese conference on pattern recognition and computer vision

作者： Zhou, Zhen Li, Rongchun Qiao, Peng Jiang, Jingfei Natl Univ Def Technol Sch Comp Natl Key Lab Parallel & Distributed Comp Changsha 410073 Peoples R China

ISBN: (纸本)9789819788576;9789819788583

Head detection is a challenging and widely applied object detection task. Although previous CNN-based head detectors have made good progress, the inherent locality of CNN restricts the extraction of global contextual information, which leads to low precision and recall rates in head detection. In this article, we propose an end-to-end high-quality head detector based on Transformer, which effectively models the contextual relationships between heads, other objects and the background. To extract and generate discriminative feature maps suitable for detecting small head targets, we incorporate specific CNN-based auxiliary detector heads for joint training. the GIoU-aware classification loss function is improved to generate bounding boxes with high localization quality and high classification confidence, and a feature fusion module is introduced to enhance the feature representation capabilities of the model. We conduct experiments on COCO 2017 dataset and Brainwash head dataset, and the results demonstrate that our method outperforms in both COCO generalized object detection and Brainwash head detection tasks compared to previous CNN-based detectors as well as other current mainstream Transformer-based object detection models.

关键词： Head detection vision transformer Auxiliary detector heads GIoU-aware Feature fusion

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Adapt and Refine: A Few-Shot Class-Incremental Learner via Pre-Trained Models 7th

Adapt and Refine: A Few-Shot Class-Incremental Learner via P...

引用

7th chinese conference on pattern recognition and computer vision

作者： Qiang, Sunyuan Xiong, Zhu Liang, Yanyan Wan, Jun Zhang, Du Macau Univ Sci & Technol Macau Peoples R China Chinese Acad Sci Inst Automat MAIS Beijing Peoples R China Univ Chinese Acad Sci SAI Beijing Peoples R China

ISBN: (纸本)9789819784868;9789819784875

the intricate and ever-changing nature of the real world imposes greater demands on neural networks, necessitating the rapid assimilation of fleeting new concepts as they arise. Consequently, a novel learning paradigm has emerged, namely, few-shot class-incremental learning (FSCIL), which aims to continuously update knowledge of novel categories with insufficient instances while avoiding catastrophic forgetting of previous knowledge. However, recent FSCIL methods encountered significant performance limitations due to the low-quality latent representation spaces obtained from base session. To this end, this paper introduces a novel FSCIL method, Adapt and REfine (ARE). Specifically, ARE initially strengthens the latent space through the powerful representational capabilities of pre-trained models (PTMs). Subsequently, we further adapt and refine the feature space and prototypes to promote the enhancement of FSCIL performance. Extensive experiments on benchmarks such as CIFAR100, mini-ImageNet, and CUB200 validate the effectiveness of the proposed method.

关键词： Few-shot class-incremental learning (FSCIL) center dot Catastrophic forgetting Pre-trained models (PTMs)

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A Robust and Real-Time RGB-D SLAM Method with Dynamic Point recognition and Depth Segmentation Optimization 7th

A Robust and Real-Time RGB-D SLAM Method with Dynamic Point ...

引用

7th chinese conference on pattern recognition and computer vision

作者： Chen, Shuaixin Gan, Baolin Zhang, Congxuan Chen, Zhen Lu, Ke Lu, Feng Nanchang Hangkong Univ Nanchang 330063 Jiangxi Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9789819786916;9789819786923

Simultaneous localization and mapping (SLAM), as one of the core prerequisite technologies for intelligent mobile robots, has attracted much attention in recent years. However, the applicability of SLAM algorithms in practical scenarios is limited due to the strict assumptions of static environments. Although many recent SLAM systems have been devoted to introducing semantic segmentation or object detection schemes to identify dynamic regions, these methods fail to detect regions with unknown semantics and are highly time-consuming. To address the abovementioned problems, we propose a robust and real-time RGB-D SLAM method with dynamic point recognition and depth segmentation optimization. Specifically, we first plan a self-adaptive feature point tracking scheme based on sparse optical flows, which accelerates the feature point tracking process and avoids local optima. then, we design a dynamic feature point recognition model that uses motion information and spatial distribution patterns to distinguish between dynamic and static point clusters. Finally, we exploit a depth segmentation optimization scheme to recover the misclassified feature points, which further improves the SLAM performance of the model. the experimental results obtained from a comparison between our method and several state-of-the-art (SOTA) models demonstrate that the proposed method achieves the best performance among the geometry-based methods and performs competitively relative to the deep learning-based models, especially in highly dynamic environments.

关键词： SLAM dynamic environments optical flow spatial distribution pattern depth segmentation

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DRC-NET: Density Reweighted Convolution Network for Edge Curve Extraction 7th

DRC-NET: Density Reweighted Convolution Network for Edge Cur...

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7th chinese conference on pattern recognition and computer vision

作者： Ning, Xiaojuan Shi, Qishuai Liu, Ynexuan Jin, Haiyan Wang, Yinghui Zhang, Xiaopeng Guo, Jianwei Xian Univ Technol Inst Comp Sci & Engn Xian Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi Jiangsu Peoples R China Chinese Acad Sci Inst Automat MAIS Beijing Peoples R China

ISBN: (纸本)9789819784899;9789819784905

Parametric edge curve extraction from 3D point clouds is a challenging task in computer vision due to the diverse, complex, sparse, and noisy characteristics of the data. In this paper, we propose a novel edge curve extraction network, named DRC-Net, which leverages Density Reweighted Convolution to address the challenge of incomplete edge curve structure encountered in prior research. Inspired by PIE-Net, we employ density-reweighted convolution instead of the conventional feature extraction module and deconvolution to supplant the feature propagation module, thereby preserving both translation and displacement invariance. Our DRC-Net is architected to simultaneously execute feature point classification and edge curve extraction, utilizing a standard encoder-decoder network structure. We evaluate the performance of DRC-Net on the ABC dataset, the largest publicly available dataset of CAD models, through robustness testing and comparison with EC-Net and PIE-Net. Experimental results demonstrate that the proposed method can effectively improve the accuracy in feature points classification and edge extraction while producing relatively complete and smooth edge curves.

关键词： D point cloud Edge curve extraction Feature point classification Density reweighted convolution

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