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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

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Multi-Centroid Task Descriptor for Dynamic Class Incremental Inference

Multi-Centroid Task Descriptor for Dynamic Class Incremental...

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conference on computer vision and pattern recognition (CVPR)

作者： Tenghao Cai Zhizhong Zhang Xin Tan Yanyun Qu Guannan Jiang Chengjie Wang Yuan Xie School of Computer Science and Technology East China Normal University Shanghai China School of Informatics Xiamen University Fujian China CATL China Tencent Youtu Lab

Incremental learning could be roughly divided into two categories, i.e., class- and task-incremental learning. The main difference is whether the task ID is given during evaluation. In this paper, we show this task information is indeed a strong prior knowledge, which will bring significant improvement over class-incremental learning baseline, e.g., DER [39]. Based on this observation, we propose a gate network to predict the task ID for class incremental inference. This is challenging as there is no explicit semantic relationship between categories in the concept of task. Therefore, we propose a multi-centroid task descriptor by assuming the data within a task can form multiple clusters. The cluster centers are optimized by pulling relevant sample-centroid pairs while pushing others away, which ensures that there is at least one centroid close to a given sample. To select relevant pairs, we use class prototypes as proxies and solve a bipartite matching problem, making the task descriptor representative yet not degenerate to uni-modal. As a result, our dynamic inference network is trained independently of baseline and provides a flexible, efficient solution to distinguish between tasks. Extensive experiments show our approach achieves state-of-the-art results, e.g., we achieve 72.41% average accuracy on CIFAR100-BOS50, outperforming DER by 3.40%.

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DINN360: Deformable Invertible Neural Network for Latitude-aware 360° Image Rescaling

DINN360: Deformable Invertible Neural Network for Latitude-a...

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conference on computer vision and pattern recognition (CVPR)

作者： Yichen Guo Mai Xu Lai Jiang Leonid Sigal Yunjin Chen School of Electronic and Information Engineering Beihang University Beijing China Department of Computer Science University of British Columbia Vancouver Canada

With the rapid development of virtual reality, 360° images have gained increasing popularity. Their wide field of view necessitates high resolution to ensure image quality. This, however, makes it harder to acquire, store and even process such 360° images. To alleviate this issue, we propose the first attempt at 360° image rescaling, which refers to downscaling a 360° image to a visually valid lowresolution (LR) counterpart and then upscaling to a highresolution (HR) 360° image given the LR variant. Specifically, we first analyze two 360° image datasets and observe several findings that characterize how 360° images typically change along their latitudes. Inspired by these findings, we propose a novel deformable invertible neural network (INN), named DINN360, for latitude-aware 360° image rescaling. In DINN360, a deformable INN is designed to downscale the LR image, and project the high-frequency (HF) component to the latent space by adaptively handling various deformations occurring at different latitude regions. Given the downscaled LR image, the high-quality HR image is then reconstructed in a conditional latitude-aware manner by recovering the structure-related HF component from the latent space. Extensive experiments over four public datasets show that our DINN360 method performs considerably better than other state-of-the-art methods for 2 x, 4 x and 8 x 360° image rescaling.

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Towards efficient feature sharing in MIMO architectures

Towards efficient feature sharing in MIMO architectures

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Ré my Sun Alexandre Ramé Clé ment Masson Nicolas Thome Matthieu Cord MLIAI SIR Sorbonne Universit&#x00E9Thales Land and Air Systems France MLIAI SIR Sorbonne Universit&#x00E9 Thales Land and Air Systems France Vertigo CEDRIC Conservatoire National des Arts et M&#x00E9 tiers MLIAI SIR Sorbonne Universit&#x00E9Valeo.ai

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Multi-input multi-output architectures propose to train multiple subnetworks within one base network and then average the subnetwork predictions to benefit from ensembling for free. Despite some relative success, these architectures are wasteful in their use of parameters. Indeed, we highlight in this paper that the learned subnetwork fail to share even generic features which limits their applicability on smaller mobile and AR/VR devices. We posit this behavior stems from an ill-posed part of the multi-input multi-output framework. To solve this issue, we propose a novel unmixing step in MIMO architectures that allows subnetworks to properly share features. Preliminary experiments on CIFAR 100 show our adjustments allow feature sharing and improve model performance for small architectures.

关键词： Performance evaluation computer vision Impedance matching conferences Neural networks computer architecture pattern recognition

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Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon Prediction

Open-World Multi-Task Control Through Goal-Aware Representat...

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conference on computer vision and pattern recognition (CVPR)

作者： Shaofei Cai Zihao Wang Xiaojian Ma Anji Liu Yitao Liang Institute for Artificial Intelligence Peking University School of Intelligence Science and Technology Peking University Computer Science Department University of California Los Angeles Beijing Institute for General Artificial Intelligence (BIGAI)

We study the problem of learning goal-conditioned policies in Minecraft, a popular, widely accessible yet challenging open-ended environment for developing human-level multi-task agents. We first identify two main challenges of learning such policies: 1) the indistinguishability of tasks from the state distribution, due to the vast scene diversity, and 2) the non-stationary nature of environment dynamics caused by partial observability. To tackle the first challenge, we propose Goal-Sensitive Backbone (GSB) for the policy to encourage the emergence of goal-relevant visual state representations. To tackle the second challenge, the policy is further fueled by an adaptive horizon prediction module that helps alleviate the learning uncertainty brought by the non-stationary dynamics. Experiments on 20 Minecraft tasks show that our method significantly outperforms the best baseline so far; in many of them, we double the performance. Our ablation and exploratory studies then explain how our approach beat the counterparts and also unveil the surprising bonus of zero-shot generalization to new scenes (biomes). We hope our agent could help shed some light on learning goal-conditioned, multi-task agents in challenging, open-ended environments like Minecraft. The code is released at https://***/CraftJarvis/MC-Controller.

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Dynamic Generative Targeted Attacks with pattern Injection

Dynamic Generative Targeted Attacks with Pattern Injection

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conference on computer vision and pattern recognition (CVPR)

作者： Weiwei Feng Nanqing Xu Tianzhu Zhang Yongdong Zhang University of Science and Technology of China Deep Space Exploration Lab

Adversarial attacks can evaluate model robustness and have been of great concern in recent years. Among various attacks, targeted attacks aim at misleading victim models to output adversary-desired predictions, which are more challenging and threatening than untargeted ones. Existing targeted attacks can be roughly divided into instance-specific and instance-agnostic attacks. Instance-specific attacks craft adversarial examples via iterative gradient updating on the specific instance. In contrast, instance-agnostic attacks learn a universal perturbation or a generative model on the global dataset to perform attacks. However, they rely too much on the classification boundary of substitute models, ignoring the realistic distribution of the target class, which may result in limited targeted attack performance. And there is no attempt to simultaneously combine the information of the specific instance and the global dataset. To deal with these limitations, we first conduct an analysis via a causal graph and propose to craft transferable targeted adversarial examples by injecting target patterns. Based on this analysis, we introduce a generative attack model composed of a cross-attention guided convolution module and a pattern injection module. Concretely, the former adopts a dynamic convolution kernel and a static convolution kernel for the specific instance and the global dataset, respectively, which can inherit the advantages of both instance-specific and instance-agnostic attacks. And the pattern injection module utilizes a pattern prototype to encode target patterns, which can guide the generation of targeted adversa rial examples. Besides, we also provide rigorous theoretical analysis to guarantee the effectiveness of our method. Extensive experiments demonstrate that our method shows superior performance than 10 existing adversarial attacks against 13 models.

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Renderable Neural Radiance Map for Visual Navigation

Renderable Neural Radiance Map for Visual Navigation

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conference on computer vision and pattern recognition (CVPR)

作者： Obin Kwon Jeongho Park Songhwai Oh Department of Electrical and Computer Engineering ASRI Seoul National University

We propose a novel type of map for visual navigation, a renderable neural radiance map (RNR-Map), which is designed to contain the overall visual information of a 3D environment. The RNR-Map has a grid form and consists of latent codes at each pixel. These latent codes are embedded from image observations, and can be converted to the neural radiance field which enables image rendering given a camera pose. The recorded latent codes implicitly contain visual information about the environment, which makes the RNR-Map visually descriptive. This visual information in RNR-Map can be a useful guideline for visual localization and navigation. We develop localization and navigation frameworks that can effectively utilize the RNR-Map. We evaluate the proposed frameworks on camera tracking, visual localization, and image-goal navigation. Experimental results show that the RNR-Map-based localization framework can find the target location based on a single query image with fast speed and competitive accuracy compared to other baselines. Also, this localization framework is robust to environmental changes, and even finds the most visually similar places when a query image from a different environment is given. The proposed navigation framework outperforms the existing image-goal navigation methods in difficult scenarios, under odometry and actuation noises. The navigation framework shows 65.7% success rate in curved scenarios of the NRNS [21] dataset, which is an improvement of 18.6% over the current state-of-the-art. Project page: https://***/projects/RNR-Map/

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NTIRE 2024 Quality Assessment of AI-Generated Content Challenge

NTIRE 2024 Quality Assessment of AI-Generated Content Challe...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Xiaohong Liu Xiongkuo Min Guangtao Zhai Chunyi Li Tengchuan Kou Wei Sun Haoning Wu Yixuan Gao Yuqin Cao Zicheng Zhang Xiele Wu Radu Timofte Fei Peng Huiyuan Fu Anlong Ming Chuanming Wang Huadong Ma Shuai He Zifei Dou Shu Chen Huacong Zhang Haiyi Xie Chengwei Wang Baoying Chen Jishen Zeng Jianquan Yang Weigang Wang Xi Fang Xiaoxin Lv Jun Yan Tianwu Zhi Yabin Zhang Yaohui Li Yang Li Jingwen Xu Jianzhao Liu Yiting Liao Junlin Li Zihao Yu Fengbin Guan Yiting Lu Xin Li Hossein Motamednia S. Farhad Hosseini-Benvidi Ahmad Mahmoudi-Aznaveh Azadeh Mansouri Ganzorig Gankhuyag Kihwan Yoon Yifang Xu Haotian Fan Fangyuan Kong Shiling Zhao Weifeng Dong Haibing Yin Li Zhu Zhiling Wang Bingchen Huang Avinab Saha Sandeep Mishra Shashank Gupta Rajesh Sureddi Oindrila Saha Luigi Celona Simone Bianco Paolo Napoletano Raimondo Schettini Junfeng Yang Jing Fu Wei Zhang Wenzhi Cao Limei Liu Han Peng Weijun Yuan Zhan Li Yihang Cheng Yifan Deng Haohui Li Bowen Qu Yao Li Shuqing Luo Shunzhou Wang Wei Gao Zihao Lu Marcos V. Conde Xinrui Wang Zhibo Chen Ruling Liao Yan Ye Qiulin Wang Bing Li Zhaokun Zhou Miao Geng Rui Chen Xin Tao Xiaoyu Liang Shangkun Sun Xingyuan Ma Jiaze Li Mengduo Yang Haoran Xu Jie Zhou Shiding Zhu Bohan Yu Pengfei Chen Xinrui Xu Jiabin Shen Zhichao Duan Erfan Asadi Jiahe Liu Qi Yan Youran Qu Xiaohui Zeng Lele Wang Renjie Liao

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact *** video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC.

关键词： Image quality computer vision conferences Computational modeling Market research Quality assessment pattern recognition

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Learned Image Compression with Mixed Transformer-CNN Architectures

Learned Image Compression with Mixed Transformer-CNN Archite...

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conference on computer vision and pattern recognition (CVPR)

作者： Jinming Liu Heming Sun Jiro Katto Department of Computer Science and Communication Engineering Waseda University Tokyo Japan Shanghai Jiao Tong University Shanghai China Waseda Research Institute for Science and Engineering Waseda University Tokyo Japan JST PRESTO Kawaguchi Saitama Japan

Learned image compression (LIC) methods have exhibited promising progress and superior rate-distortion performance compared with classical image compression standards. Most existing LIC methods are Convolutional Neural Networks-based (CNN-based) or Transformer-based, which have different advantages. Exploiting both advantages is a point worth exploring, which has two challenges: 1) how to effectively fuse the two methods? 2) how to achieve higher performance with a suitable complexity? In this paper, we propose an efficient parallel Transformer-CNN Mixture (TCM) block with a controllable complexity to incorporate the local modeling ability of CNN and the non-local modeling ability of transformers to improve the overall architecture of image compression models. Besides, inspired by the recent progress of entropy estimation models and attention modules, we propose a channel-wise entropy model with parameter-efficient swin-transformer-based attention (SWAtten) modules by using channel squeezing. Experimental results demonstrate our proposed method achieves state-of-the-art rate-distortion performances on three different resolution datasets (i.e., Kodak, Tecnick, CLIC Professional Validation) compared to existing LIC methods. The code is at https://***/jmliu206/LIC_TCM.

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Improving Graph Representation for Point Cloud Segmentation via Attentive Filtering

Improving Graph Representation for Point Cloud Segmentation ...

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conference on computer vision and pattern recognition (CVPR)

作者： Nan Zhang Zhiyi Pan Thomas H. Li Wei Gao Ge Li School of Electronic and Computer Engineering Shenzhen Graduate School Peking University

Recently, self-attention networks achieve impressive performance in point cloud segmentation due to their superiority in modeling long-range dependencies. However, compared to self-attention mechanism, we find graph convolutions show a stronger ability in capturing local geometry information with less computational cost. In this paper, we employ a hybrid architecture design to construct our Graph Convolution Network with Attentive Filtering (AF-GCN), which takes advantage of both graph convolution and selfattention mechanism. We adopt graph convolutions to aggregate local features in the shallow encoder stages, while in the deeper stages, we propose a self-attention-like module named Graph Attentive Filter (GAF) to better model long-range contexts from distant neighbors. Besides, to further improve graph representation for point cloud segmentation, we employ a Spatial Feature Projection (SFP) module for graph convolutions which helps to handle spatial variations of unstructured point clouds. Finally, a graphshared down-sampling and up-sampling strategy is introduced to make full use of the graph structures in point cloud processing. We conduct extensive experiments on multiple datasets including S3DIS, ScanNetV2, Toronto-3D, and ShapeNetPart. Experimental results show our AF-GCN obtains competitive performance.

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DaFKD: Domain-aware Federated Knowledge Distillation

DaFKD: Domain-aware Federated Knowledge Distillation

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conference on computer vision and pattern recognition (CVPR)

作者： Haozhao Wang Yichen Li Wenchao Xu Ruixuan Li Yufeng Zhan Zhigang Zeng School of Computer Science and Technology The Hong Kong Polytechnic University Hong Kong Beijing Institute of Technology Beijing China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Soochow University Suzhou China

Federated Distillation (FD) has recently attracted increasing attention for its efficiency in aggregating multiple diverse local models trained from statistically heterogeneous data of distributed clients. Existing FD methods generally treat these models equally by merely computing the average of their output soft predictions for some given input distillation sample, which does not take the diversity across all local models into account, thus leading to degraded performance of the aggregated model, especially when some local models learn little knowledge about the sample. In this paper, we propose a new perspective that treats the local data in each client as a specific domain and design a novel domain knowledge aware federated distillation method, dubbed DaFKD, that can discern the importance of each model to the distillation sample, and thus is able to optimize the ensemble of soft predictions from diverse models. Specifically, we employ a domain discriminator for each client, which is trained to identify the correlation factor between the sample and the corresponding domain. Then, to facilitate the training of the domain discriminator while saving communication costs, we propose sharing its partial parameters with the classification model. Extensive experiments on various datasets and settings show that the proposed method can improve the model accuracy by up to 6.02% compared to state-of-the-art baselines.

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