作者: 合適 時(shí)間: 2025-3-21 23:20
H3DNet: 3D Object Detection Using Hybrid Geometric Primitives,作者: Decimate 時(shí)間: 2025-3-22 04:19
Generative Low-Bitwidth Data Free Quantization,ization (GDFQ) to remove the data dependence burden. Specifically, we propose a knowledge matching generator to produce meaningful fake data by exploiting classification boundary knowledge and distribution information in the pre-trained model. With the help of generated data, we can quantize a model作者: 整潔漂亮 時(shí)間: 2025-3-22 07:14 作者: Highbrow 時(shí)間: 2025-3-22 10:58 作者: 接觸 時(shí)間: 2025-3-22 13:29 作者: 接觸 時(shí)間: 2025-3-22 17:30 作者: lipoatrophy 時(shí)間: 2025-3-22 23:09
Thinking in Frequency: Face Forgery Detection by Mining Frequency-Aware Clues,he forgery patterns via our two-stream collaborative learning framework. We apply DCT as the applied frequency-domain transformation. Through comprehensive studies, we show that the proposed F.-Net significantly outperforms competing state-of-the-art methods on all compression qualities in the chall作者: heckle 時(shí)間: 2025-3-23 02:43
SeqHAND: RGB-Sequence-Based 3D Hand Pose and Shape Estimation,rent layer of the framework during domain finetuning from synthetic to real allows preservation of the visuo-temporal features learned from sequential synthetic hand images. Hand poses that are sequentially estimated consequently produce natural and smooth hand movements which lead to more robust es作者: 原始 時(shí)間: 2025-3-23 05:41
Rethinking the Distribution Gap of Person Re-identification with Camera-Based Batch Normalization, undervalued before due to the lack of cross-camera information, to achieve competitive ReID performance. Experiments on a wide range of ReID tasks demonstrate the effectiveness of our approach. The code is available at ..作者: 凈禮 時(shí)間: 2025-3-23 11:50
AMLN: Adversarial-Based Mutual Learning Network for Online Knowledge Distillation,tion. AMLN has been evaluated under a variety of network architectures over three widely used benchmark datasets. Extensive experiments show that AMLN achieves superior performance consistently against state-of-the-art knowledge transfer methods.作者: alabaster 時(shí)間: 2025-3-23 17:22 作者: Scintillations 時(shí)間: 2025-3-23 20:16 作者: Blood-Clot 時(shí)間: 2025-3-23 23:30
AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds,red to state-of-the-art attacks. We test AdvPC using four popular point cloud networks: PointNet, PointNet++ (MSG and SSG), and DGCNN. Our proposed attack increases the attack success rate by up?to 40% for those transferred to unseen networks (transferability), while maintaining a high success rate 作者: dandruff 時(shí)間: 2025-3-24 04:56 作者: Bombast 時(shí)間: 2025-3-24 07:57
Public Finances and the Financial Systemization (GDFQ) to remove the data dependence burden. Specifically, we propose a knowledge matching generator to produce meaningful fake data by exploiting classification boundary knowledge and distribution information in the pre-trained model. With the help of generated data, we can quantize a model作者: 神圣將軍 時(shí)間: 2025-3-24 12:19
Agriculture during Industrializationfusing class-irrelevant regions, which makes the local correlation knowledge more accurate and valuable. We conduct extensive experiments and ablation studies on challenging datasets, including CIFAR100 and ImageNet, to show our superiority over the state-of-the-art methods.作者: GLADE 時(shí)間: 2025-3-24 14:53
Foreign Direct Investments in the EAEU,ing with large objects (such as bicycles, motorcycles, and surfboards) and handheld objects (such as laptops, tennis rackets, and skateboards). We quantify the ability of our approach to recover human-object arrangements and outline remaining challenges in this relatively unexplored domain. The proj作者: 使?jié)M足 時(shí)間: 2025-3-24 20:54 作者: monochromatic 時(shí)間: 2025-3-25 02:09
Allen K. Lynch,Todd Clear,David W. Rasmussenh more than 10 sensors. . CelebA-Spoof contains 10 spoof type annotations, as well as the 40 attribute annotations inherited from the original CelebA dataset. Equipped with CelebA-Spoof, we carefully benchmark existing methods in a unified multi-task framework, ., and reveal several valuable observa作者: 交響樂(lè) 時(shí)間: 2025-3-25 07:18 作者: 運(yùn)氣 時(shí)間: 2025-3-25 07:30 作者: Homocystinuria 時(shí)間: 2025-3-25 12:52
Alan Clarke,Nigel G. Fielding,Robert Witt undervalued before due to the lack of cross-camera information, to achieve competitive ReID performance. Experiments on a wide range of ReID tasks demonstrate the effectiveness of our approach. The code is available at ..作者: 斜坡 時(shí)間: 2025-3-25 19:23 作者: 木訥 時(shí)間: 2025-3-25 20:14 作者: 背景 時(shí)間: 2025-3-26 03:50 作者: Malfunction 時(shí)間: 2025-3-26 05:07 作者: 帽子 時(shí)間: 2025-3-26 09:50
https://doi.org/10.1007/978-3-031-61864-2gate the pixel-wise features onto vertices, which emphasizes on the features around edges for fine segmentation along edges. The finally learned graph representation is projected back to pixel grids for parsing. Experiments demonstrate that our model outperforms state-of-the-art methods on the widel作者: 托運(yùn) 時(shí)間: 2025-3-26 16:32
The Economic Dimensions of Crime of cell positions in the outputs of co-detection CNN. Experiments demonstrated that the proposed method can associate cells by analyzing co-detection CNN. Even though the method uses only weak supervision, the performance of our method was almost the same as the state-of-the-art supervised method. Code is publicly available in ..作者: Stricture 時(shí)間: 2025-3-26 19:22 作者: 修飾語(yǔ) 時(shí)間: 2025-3-26 22:45
The Optimal Replacement Policy,o excavate informative parts of depth cues from the channel and spatial views. This fuses RGB and depth modalities in a complementary way. Our simple yet efficient architecture, dubbed .ifurcated .ackbone .trategy .work (.), is backbone independent and outperforms 18 SOTAs on seven challenging datasets using four metrics.作者: Fecundity 時(shí)間: 2025-3-27 03:52
Weakly-Supervised Cell Tracking via Backward-and-Forward Propagation, of cell positions in the outputs of co-detection CNN. Experiments demonstrated that the proposed method can associate cells by analyzing co-detection CNN. Even though the method uses only weak supervision, the performance of our method was almost the same as the state-of-the-art supervised method. Code is publicly available in ..作者: Musket 時(shí)間: 2025-3-27 08:59 作者: 漂白 時(shí)間: 2025-3-27 10:17 作者: Ordnance 時(shí)間: 2025-3-27 16:55 作者: Lucubrate 時(shí)間: 2025-3-27 18:35
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: Slit-Lamp 時(shí)間: 2025-3-27 23:23
Conference proceedings 2020g; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..?..?.作者: 確定的事 時(shí)間: 2025-3-28 05:03 作者: 采納 時(shí)間: 2025-3-28 06:40 作者: 賭博 時(shí)間: 2025-3-28 14:11
Single Image Super-Resolution via a Holistic Attention Network,ons of each channel to selectively capture more informative features. Extensive experiments demonstrate that the proposed HAN performs favorably against the state-of-the-art single image super-resolution approaches.作者: MODE 時(shí)間: 2025-3-28 18:28
Generative Low-Bitwidth Data Free Quantization, deployed on mobile or embedded devices. Existing quantization methods require original data for calibration or fine-tuning to get better performance. However, in many real-world scenarios, the data may not be available due to confidential or private issues, thereby making existing quantization meth作者: Nebulous 時(shí)間: 2025-3-28 21:59
Local Correlation Consistency for Knowledge Distillation,udent network. Existing methods mainly focus on the consistency of instance-level features and their relationships, but neglect the local features and their correlation, which also contain many details and discriminative patterns. In this paper, we propose the local correlation exploration framework作者: 人工制品 時(shí)間: 2025-3-29 01:48
Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild,e-wild captured in an uncontrolled environment. Notably, our method runs on datasets without any scene- or object-level 3D supervision. Our key insight is that considering humans and objects jointly gives rise to “3D common sense” constraints that can be used to resolve ambiguity. In particular, we 作者: 打擊 時(shí)間: 2025-3-29 05:02
Sep-Stereo: Visually Guided Stereophonic Audio Generation by Associating Source Separation,s guidance to generate binaural or ambisonic audio from mono ones with stereo supervision. However, this fully supervised paradigm suffers from an inherent drawback: the recording of stereophonic audio usually requires delicate devices that are expensive for wide accessibility. To overcome this chal作者: Amenable 時(shí)間: 2025-3-29 07:59
CelebA-Spoof: Large-Scale Face Anti-spoofing Dataset with Rich Annotations,fforts devoted. Among them, face anti-spoofing emerges as an important area, whose objective is to identify whether a presented face is live or spoof. Though promising progress has been achieved, existing works still have difficulty in handling complex spoof attacks and generalizing to real-world sc作者: Nonthreatening 時(shí)間: 2025-3-29 14:45 作者: RENIN 時(shí)間: 2025-3-29 19:30 作者: MAOIS 時(shí)間: 2025-3-29 19:59
SeqHAND: RGB-Sequence-Based 3D Hand Pose and Shape Estimation,ased on independent static images. In this paper, we attempt to not only consider the appearance of a hand but incorporate the temporal movement information of a hand in motion into the learning framework, which leads to the necessity of a large-scale dataset with sequential RGB hand images. We prop作者: Soliloquy 時(shí)間: 2025-3-30 03:50 作者: 假設(shè) 時(shí)間: 2025-3-30 07:44
AMLN: Adversarial-Based Mutual Learning Network for Online Knowledge Distillation,dels simultaneously and collaboratively. On the other hand, existing works focus more on outcome-driven learning according to knowledge like classification probabilities whereas the distilling processes which capture rich and useful intermediate features and information are largely neglected. In thi作者: Epidural-Space 時(shí)間: 2025-3-30 08:51 作者: 生氣的邊緣 時(shí)間: 2025-3-30 13:43
Single Image Super-Resolution via a Holistic Attention Network,ving information-rich features in each layer. However, channel attention treats each convolution layer as a separate process that misses the correlation among different layers. To address this problem, we propose a new holistic attention network (HAN), which consists of a layer attention module (LAM作者: 東西 時(shí)間: 2025-3-30 19:24
Can You Read Me Now? Content Aware Rectification Using Angle Supervision,phed documents are often folded and crumpled, resulting in large local variance in text structure. The problem of document rectification is fundamental to the Optical Character Recognition (OCR) process on documents, and its ability to overcome geometric distortions significantly affects recognition作者: 暗諷 時(shí)間: 2025-3-30 22:28
Momentum Batch Normalization for Deep Learning with Small Batch Size,s shown its effectiveness in accelerating the model training speed and improving model generalization capability. The success of BN has been explained from different views, such as reducing internal covariate shift, allowing the use of large learning rate, smoothing optimization landscape, etc. To m作者: Ordnance 時(shí)間: 2025-3-31 01:44 作者: 向下五度才偏 時(shí)間: 2025-3-31 05:17
Edge-Aware Graph Representation Learning and Reasoning for Face Parsing, in face parsing, which however overlook the correlation among different face regions. The correlation is a critical clue about the facial appearance, pose, expression, ., and should be taken into account for face parsing. To this end, we propose to model and reason the region-wise relations by lear作者: Asparagus 時(shí)間: 2025-3-31 11:11 作者: 盡忠 時(shí)間: 2025-3-31 14:43
Computer Vision – ECCV 2020978-3-030-58610-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 一罵死割除 時(shí)間: 2025-3-31 19:37
https://doi.org/10.1007/978-3-030-58610-2computer networks; computer vision; education; face recognition; image analysis; image coding; image proce作者: slow-wave-sleep 時(shí)間: 2025-4-1 01:20 作者: 鑲嵌細(xì)工 時(shí)間: 2025-4-1 04:46 作者: Epithelium 時(shí)間: 2025-4-1 08:02 作者: JAMB 時(shí)間: 2025-4-1 13:34 作者: 禁止 時(shí)間: 2025-4-1 15:11
Foreign Direct Investments in the EAEU,e-wild captured in an uncontrolled environment. Notably, our method runs on datasets without any scene- or object-level 3D supervision. Our key insight is that considering humans and objects jointly gives rise to “3D common sense” constraints that can be used to resolve ambiguity. In particular, we 作者: eczema 時(shí)間: 2025-4-1 22:16
