作者: Indolent 時間: 2025-3-21 21:52 作者: 字形刻痕 時間: 2025-3-22 01:11
Dual Adversarial Network: Toward Real-World Noise Removal and Noise Generation,ates the research of noise generation, aiming at synthesizing more clean-noisy image pairs to facilitate the training of deep denoisers. In this work, we propose a novel unified framework to simultaneously deal with the noise removal and noise generation tasks. Instead of only inferring the posterio作者: 反對 時間: 2025-3-22 06:10
Linguistic Structure Guided Context Modeling for Referring Image Segmentation,ence is crucial to distinguish the referent from the background. Existing methods either insufficiently or redundantly model the multimodal context. To tackle this problem, we propose a “gather-propagate-distribute” scheme to model multimodal context by cross-modal interaction and implement this sch作者: incision 時間: 2025-3-22 12:35 作者: Self-Help-Group 時間: 2025-3-22 15:38
Robust Re-Identification by Multiple Views Knowledge Distillation,a large drop in performance for single image queries (e.g., Image-To-Video setting). Recent works address this severe degradation by transferring . from a Video-based network to an Image-based one. In this work, we devise a training strategy that allows the transfer of a superior knowledge, arising 作者: Self-Help-Group 時間: 2025-3-22 18:47 作者: 擁擠前 時間: 2025-3-23 01:02
,RhyRNN: Rhythmic RNN for?Recognizing Events in Long and?Complex Videos,ains a challenge. One particular reason is that events in long and complex videos can consist of multiple heterogeneous sub-activities (in terms of rhythms, activity variants, composition order, etc.) within quite a long period. This fact brings about two main difficulties: excessive/varying length 作者: SPALL 時間: 2025-3-23 04:42 作者: 過份好問 時間: 2025-3-23 07:20
Weighing Counts: Sequential Crowd Counting by Reinforcement Learning,o existing counting models that directly output count values, we divide one-step estimation into a sequence of much easier and more tractable sub-decision problems. Such sequential decision nature corresponds exactly to a physical process in reality—scale weighing. Inspired by scale weighing, we pro作者: glacial 時間: 2025-3-23 11:34
Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks,ctim model by injecting a backdoor pattern into a small proportion of the training data. At test time, the victim model behaves normally on clean test data, yet consistently predicts a specific (likely incorrect) target class whenever the backdoor pattern is present in a test example. While existing作者: BANAL 時間: 2025-3-23 16:09
Learning to Learn with Variational Information Bottleneck for Domain Generalization, we address both problems. We introduce a probabilistic meta-learning model for domain generalization, in which classifier parameters shared across domains are modeled as distributions. This enables better handling of prediction uncertainty on unseen domains. To deal with domain shift, we learn doma作者: Thyroiditis 時間: 2025-3-23 20:58
Deep Positional and Relational Feature Learning for Rotation-Invariant Point Cloud Analysis,ghly aligned 3D shapes based on point coordinates, but suffer from performance drops with shape rotations. Some geometric features, e.g., distances and angles of points as inputs of network, are rotation-invariant but lose positional information of points. In this work, we propose a novel deep netwo作者: Osteons 時間: 2025-3-24 01:24
Thanks for Nothing: Predicting Zero-Valued Activations with Lightweight Convolutional Neural Networe observation that spatial correlation exists in CNN output feature maps (ofms), we propose a method to dynamically predict whether ofm activations are zero-valued or not according to their neighboring activation values, thereby avoiding zero-valued activations and reducing the number of convolution作者: capsule 時間: 2025-3-24 02:55 作者: 箴言 時間: 2025-3-24 08:13
SCAN: Learning to Classify Images Without Labels,ification remains an important, and open challenge in computer vision. Several recent approaches have tried to tackle this problem in an end-to-end fashion. In this paper, we deviate from recent works, and advocate a two-step approach where feature learning and clustering are decoupled. First, a sel作者: Diatribe 時間: 2025-3-24 11:03
Graph Convolutional Networks for Learning with Few Clean and Many Noisy Labels,d noisy data is modeled by a graph per class and Graph Convolutional Networks (GCN) are used to predict class relevance of noisy examples. For each class, the GCN is treated as a binary classifier, which learns to discriminate clean from noisy examples using a weighted binary cross-entropy loss func作者: avulsion 時間: 2025-3-24 14:54
Object-and-Action Aware Model for Visual Language Navigation,on the basis of visible environments. This requires to extract value from two very different types of natural-language information. The first is object description (e.g., ‘table’, ‘door’), each presenting as a tip for the agent to determine the next action by finding the item visible in the environm作者: AXIS 時間: 2025-3-24 20:39
A Comprehensive Study of Weight Sharing in Graph Networks for 3D Human Pose Estimation,rmance. One limitation of the vanilla graph convolution is that it models the relationships between neighboring nodes via a shared weight matrix. This is suboptimal for articulated body modeling as the relations between different body joints are different. The objective of this paper is to have a co作者: DUCE 時間: 2025-3-25 03:13
0302-9743 uter Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic..The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers dea作者: fluffy 時間: 2025-3-25 03:54 作者: farewell 時間: 2025-3-25 09:45
0302-9743 processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..?..?.978-3-030-58606-5978-3-030-58607-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: ERUPT 時間: 2025-3-25 13:51 作者: CANDY 時間: 2025-3-25 17:21 作者: extract 時間: 2025-3-25 23:18 作者: Hirsutism 時間: 2025-3-26 03:41
The Incidence of US Farm Programse rotation-invariant when processing point clouds as input. Experiments show state-of-the-art classification and segmentation performances on benchmark datasets, and ablation studies demonstrate effectiveness of the network design.作者: 含水層 時間: 2025-3-26 05:06 作者: 新星 時間: 2025-3-26 10:13 作者: 范圍廣 時間: 2025-3-26 16:40
Jesús Astigarraga,Javier Usoz,Juan Zabalzaree more variants can be derived by decoupling the self-connections with other edges. We conduct extensive ablation study on these weight sharing methods under controlled settings and obtain new conclusions that will benefit the community.作者: IVORY 時間: 2025-3-26 20:05
,Federated Visual Classification with?Real-World Data Distribution,ta splits that simulate real-world edge learning scenarios. We also develop two new algorithms (FedVC, FedIR) that intelligently resample and reweight over the client pool, bringing large improvements in accuracy and stability in training. The datasets are made available online.作者: hematuria 時間: 2025-3-26 23:10 作者: strdulate 時間: 2025-3-27 03:53 作者: 行乞 時間: 2025-3-27 06:30 作者: Hla461 時間: 2025-3-27 10:09 作者: Irrigate 時間: 2025-3-27 15:16 作者: 散布 時間: 2025-3-27 18:28
A Comprehensive Study of Weight Sharing in Graph Networks for 3D Human Pose Estimation,ree more variants can be derived by decoupling the self-connections with other edges. We conduct extensive ablation study on these weight sharing methods under controlled settings and obtain new conclusions that will benefit the community.作者: collagen 時間: 2025-3-27 22:34 作者: 調(diào)味品 時間: 2025-3-28 03:42 作者: 要素 時間: 2025-3-28 06:47
Linguistic Structure Guided Context Modeling for Referring Image Segmentation,-WG) which guides all the words to include valid multimodal context of the sentence while excluding disturbing ones through three steps over the multimodal feature, i.e., gathering, constrained propagation and distributing. Extensive experiments on four benchmarks demonstrate that our method outperforms all the previous state-of-the-arts.作者: ACTIN 時間: 2025-3-28 10:31
Thanks for Nothing: Predicting Zero-Valued Activations with Lightweight Convolutional Neural Networting models. ZAPs are trained by mimicking hidden layer ouputs; thereby, enabling a parallel and label-free training. Furthermore, without retraining, each ZAP can be tuned to a different operating point trading accuracy for MAC reduction.作者: 節(jié)約 時間: 2025-3-28 15:46
Conference proceedings 2020n, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic..The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with top作者: hedonic 時間: 2025-3-28 21:59 作者: invert 時間: 2025-3-29 00:47 作者: COM 時間: 2025-3-29 03:43 作者: 不知疲倦 時間: 2025-3-29 08:53
https://doi.org/10.1007/978-3-031-65304-9e. Most existing methods focus only on parts of the body. A few recent approaches reconstruct full expressive 3D humans from images using 3D body models that include the face and hands. These methods are optimization-based and thus slow, prone to local optima, and require 2D keypoints as input. We a作者: Ceremony 時間: 2025-3-29 12:48 作者: 爭論 時間: 2025-3-29 18:01 作者: 切碎 時間: 2025-3-29 20:07
https://doi.org/10.1007/978-3-319-29178-9ges in terms of data diversity and quality. Whilst typical models in the datacenter are trained using data that are independent and identically distributed (IID), data at source are typically far from IID. Furthermore, differing quantities of data are typically available at each device (imbalance). 作者: Ingratiate 時間: 2025-3-30 00:57
David G. Mayes,Clive S. Nicholasa large drop in performance for single image queries (e.g., Image-To-Video setting). Recent works address this severe degradation by transferring . from a Video-based network to an Image-based one. In this work, we devise a training strategy that allows the transfer of a superior knowledge, arising 作者: 阻塞 時間: 2025-3-30 07:08
Leasing and the Incentive to Invest,g because the blur is spatially varying and difficult to estimate. We propose an effective defocus deblurring method that exploits data available on dual-pixel (DP) sensors found on most modern cameras. DP sensors are used to assist a camera’s auto-focus by capturing two sub-aperture views of the sc作者: 錢財(cái) 時間: 2025-3-30 08:40
Studies in Productivity and Efficiencyains a challenge. One particular reason is that events in long and complex videos can consist of multiple heterogeneous sub-activities (in terms of rhythms, activity variants, composition order, etc.) within quite a long period. This fact brings about two main difficulties: excessive/varying length 作者: insincerity 時間: 2025-3-30 12:41 作者: 證實(shí) 時間: 2025-3-30 20:33
May Peters,Richard Stillman,Agapi Somwaruo existing counting models that directly output count values, we divide one-step estimation into a sequence of much easier and more tractable sub-decision problems. Such sequential decision nature corresponds exactly to a physical process in reality—scale weighing. Inspired by scale weighing, we pro作者: 龍蝦 時間: 2025-3-30 21:05
The Incidence of US Farm Programsctim model by injecting a backdoor pattern into a small proportion of the training data. At test time, the victim model behaves normally on clean test data, yet consistently predicts a specific (likely incorrect) target class whenever the backdoor pattern is present in a test example. While existing作者: PTCA635 時間: 2025-3-31 04:28
Studies in Productivity and Efficiency we address both problems. We introduce a probabilistic meta-learning model for domain generalization, in which classifier parameters shared across domains are modeled as distributions. This enables better handling of prediction uncertainty on unseen domains. To deal with domain shift, we learn doma作者: 根除 時間: 2025-3-31 07:42
The Incidence of US Farm Programsghly aligned 3D shapes based on point coordinates, but suffer from performance drops with shape rotations. Some geometric features, e.g., distances and angles of points as inputs of network, are rotation-invariant but lose positional information of points. In this work, we propose a novel deep netwo作者: Excise 時間: 2025-3-31 10:32 作者: 投票 時間: 2025-3-31 17:12 作者: 注意 時間: 2025-3-31 19:17 作者: 無可爭辯 時間: 2025-4-1 00:27
The Economic Importance of Insectsd noisy data is modeled by a graph per class and Graph Convolutional Networks (GCN) are used to predict class relevance of noisy examples. For each class, the GCN is treated as a binary classifier, which learns to discriminate clean from noisy examples using a weighted binary cross-entropy loss func作者: 喧鬧 時間: 2025-4-1 03:18 作者: opinionated 時間: 2025-4-1 09:03
Jesús Astigarraga,Javier Usoz,Juan Zabalzarmance. One limitation of the vanilla graph convolution is that it models the relationships between neighboring nodes via a shared weight matrix. This is suboptimal for articulated body modeling as the relations between different body joints are different. The objective of this paper is to have a co作者: 純樸 時間: 2025-4-1 12:33 作者: 無目標(biāo) 時間: 2025-4-1 14:44 作者: 散步 時間: 2025-4-1 19:45 作者: Expurgate 時間: 2025-4-1 23:33
