作者: 百靈鳥 時間: 2025-3-21 23:59
,Efficient Training with?Denoised Neural Weights,alize parameters is challenging and?may require manual tuning, which can be time-consuming and prone?to human error. To overcome such limitations, this work takes a?novel step towards building a . to synthesize?the neural weights for initialization. We use the image-to-image translation task with ge作者: octogenarian 時間: 2025-3-22 00:31 作者: 暫停,間歇 時間: 2025-3-22 07:31 作者: myelography 時間: 2025-3-22 09:35
,Local and?Global Flatness for?Federated Domain Generalization,ings assume identical?data distributions for both training and testing sets, neglecting?the demand for the model’s cross-domain generalization ability when?such assumption does not hold. Federated domain generalization seeks?to develop a model that is capable of generalizing to unseen testing domain作者: 詞匯表 時間: 2025-3-22 16:28
,SRPose: Two-View Relative Pose Estimation with?Sparse Keypoints,from time-consuming robust estimators,?while deep learning-based pose regressors only cater to camera-to-world pose estimation, lacking generalizability to different image?sizes and camera intrinsics. In this paper, we propose SRPose, a sparse keypoint-based framework for two-view relative pose esti作者: 詞匯表 時間: 2025-3-22 20:34 作者: 先兆 時間: 2025-3-23 00:22 作者: 調(diào)味品 時間: 2025-3-23 04:20 作者: PLUMP 時間: 2025-3-23 08:15 作者: 鋸齒狀 時間: 2025-3-23 12:10
,Boost Your NeRF: A Model-Agnostic Mixture of?Experts Framework for?High Quality and?Efficient Rendef Fast-NeRFs models. Despite demonstrating impressive rendering speed and quality, the rapid convergence of such models poses challenges for further improving reconstruction quality. Common strategies to improve rendering quality involves augmenting model parameters or increasing the number of sampl作者: 愛了嗎 時間: 2025-3-23 15:41
,PFGS: High Fidelity Point Cloud Rendering via?Feature Splatting,ssing details, or expensive computations. In this paper, we propose a novel framework to render high-quality images from sparse points. This method first attempts to bridge the 3D Gaussian Splatting and point cloud rendering, which includes several cascaded modules. We first use a regressor to estim作者: Tailor 時間: 2025-3-23 20:38 作者: indignant 時間: 2025-3-23 23:35
,E3M: Zero-Shot Spatio-Temporal Video Grounding with?Expectation-Maximization Multimodal Modulation,n costs, we make a first exploration to tackle spatio-temporal video grounding in a zero-shot manner. Our method dispenses with the need for any training videos or annotations; instead, it localizes the target object by leveraging pre-trained vision-language models and optimizing within the video an作者: 個人長篇演說 時間: 2025-3-24 04:26 作者: EXTOL 時間: 2025-3-24 09:19 作者: Ligament 時間: 2025-3-24 13:05 作者: 伸展 時間: 2025-3-24 15:33 作者: BINGE 時間: 2025-3-24 21:52
,Generalized Coverage for?More Robust Low-Budget Active Learning,ribution with balls of a given radius at selected data points. We demonstrate, however, that the performance of this algorithm is extremely sensitive to the choice of this radius hyper-parameter, and that tuning it is quite difficult, with the original heuristic frequently failing. We thus introduce作者: G-spot 時間: 2025-3-24 23:22
Conference proceedings 2025uter Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024...The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal with topics such as computer vision; machine learning; deep neural networks; reinforceme作者: ABHOR 時間: 2025-3-25 07:19
0302-9743 ce on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024...The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal with topics such as computer vision; machine learning; deep neural networks; r作者: 現(xiàn)存 時間: 2025-3-25 08:49 作者: 鍵琴 時間: 2025-3-25 11:47 作者: invulnerable 時間: 2025-3-25 19:11
CMOS Image Sensors for Ambient Intelligenceoder. The whole pipeline experiences a two-stage training and is driven by our well-designed progressive and multiscale reconstruction loss. Experiments on different benchmarks show the superiority of our method in terms of rendering qualities and the necessities of our main components. (Project page: .).作者: Leisureliness 時間: 2025-3-25 20:05
Melanie Walker,Elaine Unterhalterate that EMO is able to produce not only convincing speaking videos but also singing videos in various styles, significantly outperforming existing state-of-the-art methodologies in terms of expressiveness and realism.作者: 合群 時間: 2025-3-26 00:12
,Deep Reward Supervisions for?Tuning Text-to-Image Diffusion Models,ally, we fine-tune Stable Diffusion XL 1.0 (SDXL 1.0) model via DRTune to optimize Human Preference Score v2.1, resulting in the Favorable Diffusion XL 1.0 (FDXL 1.0) model. FDXL 1.0 significantly enhances image quality compared to SDXL 1.0?and reaches comparable quality compared with Midjourney v5.2.作者: 樹上結(jié)蜜糖 時間: 2025-3-26 04:24 作者: GULLY 時間: 2025-3-26 09:45
,EMO: Emote Portrait Alive Generating Expressive Portrait Videos with?Audio2Video Diffusion Model Unate that EMO is able to produce not only convincing speaking videos but also singing videos in various styles, significantly outperforming existing state-of-the-art methodologies in terms of expressiveness and realism.作者: 愛了嗎 時間: 2025-3-26 12:45
https://doi.org/10.1007/978-3-476-03606-3ns. Comprehensive experiments show that our model achieves SOTA performance in generating ultra-high-resolution images in both machine and human evaluation. Compared to commonly used UNet structures, our model can save more than . memory when generating . images. The project URL is ..作者: misanthrope 時間: 2025-3-26 18:13
,Inf-DiT: Upsampling Any-Resolution Image with?Memory-Efficient Diffusion Transformer,ns. Comprehensive experiments show that our model achieves SOTA performance in generating ultra-high-resolution images in both machine and human evaluation. Compared to commonly used UNet structures, our model can save more than . memory when generating . images. The project URL is ..作者: Ballerina 時間: 2025-3-27 00:12
Conference proceedings 2025nt learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation..作者: Substance 時間: 2025-3-27 04:37
https://doi.org/10.1007/978-3-0348-6370-4 dataset is limited. Current strategies aim to address this challenge through different domain generalization techniques, yet they have had limited success due to the risk of overfitting when solely relying on value labels for regression. Recent progress in pre-trained vision-language models has mot作者: Frisky 時間: 2025-3-27 08:02
https://doi.org/10.1007/978-3-0348-6370-4alize parameters is challenging and?may require manual tuning, which can be time-consuming and prone?to human error. To overcome such limitations, this work takes a?novel step towards building a . to synthesize?the neural weights for initialization. We use the image-to-image translation task with ge作者: 松緊帶 時間: 2025-3-27 13:18
Angelika D?rfler-Dierken,Gerhard Kümmelt hinders both unimodal and multimodal contrastive learning is feature suppression, a phenomenon where?the trained model captures only a limited portion of the information from the input data while overlooking other potentially valuable content. This issue often leads to indistinguishable representa作者: Increment 時間: 2025-3-27 15:26
Angelika D?rfler-Dierken,Gerhard Kümmelfashion VLP research?has proposed various pre-training tasks to account for fine-grained details in multimodal fusion. However, fashion VLP research has?not yet addressed the need to focus on (1) uni-modal embeddings?that reflect fine-grained features and (2) hard negative samples?to improve the per作者: 錯事 時間: 2025-3-27 20:23
Angelika D?rfler-Dierken,Gerhard Kümmelings assume identical?data distributions for both training and testing sets, neglecting?the demand for the model’s cross-domain generalization ability when?such assumption does not hold. Federated domain generalization seeks?to develop a model that is capable of generalizing to unseen testing domain作者: GLIDE 時間: 2025-3-27 22:48 作者: Modify 時間: 2025-3-28 05:50 作者: RUPT 時間: 2025-3-28 06:52 作者: 榮幸 時間: 2025-3-28 11:12
https://doi.org/10.1007/978-3-476-03606-3ng ultra-high-resolution images (e.g. .), the resolution of generated images is often limited to .. In this work. We propose a unidirectional block attention mechanism that can adaptively adjust the memory overhead during the inference process and handle global dependencies. Building on this module,作者: Barrister 時間: 2025-3-28 15:17 作者: 整潔 時間: 2025-3-28 22:14 作者: 可卡 時間: 2025-3-29 02:38
CMOS Image Sensors for Ambient Intelligencessing details, or expensive computations. In this paper, we propose a novel framework to render high-quality images from sparse points. This method first attempts to bridge the 3D Gaussian Splatting and point cloud rendering, which includes several cascaded modules. We first use a regressor to estim作者: Ballerina 時間: 2025-3-29 04:00
The Physical Basis of Ambient Intelligence methods address this issue by synthesizing anomalies with noise or external data. However, there is always a large semantic gap between synthetic and real-world anomalies, resulting in weak performance in anomaly detection. To solve the problem, we propose a few-shot Anomaly-driven Generation (AnoG作者: sultry 時間: 2025-3-29 09:50 作者: Semblance 時間: 2025-3-29 15:21
Melanie Walker,Elaine Unterhalterced relationship between audio cues and facial movements. We identify the limitations of traditional techniques that often fail to capture the full spectrum of human expressions and the uniqueness of individual facial styles. To address these issues, we propose EMO, a novel framework that utilizes a作者: 繁榮中國 時間: 2025-3-29 16:20 作者: 得罪 時間: 2025-3-29 21:21
Luisa S. Deprez,Sandra S. Butlered on the model training phase. However, these approaches become impractical when dealing with?the outsourcing of sensitive data. Furthermore, they have encountered significant challenges in balancing the utility-privacy trade-off. How can we generate privacy-preserving surrogate data suitable?for u作者: 忍受 時間: 2025-3-30 01:09 作者: Agronomy 時間: 2025-3-30 04:30
Building a High-Contrast Planetary Newtonianribution with balls of a given radius at selected data points. We demonstrate, however, that the performance of this algorithm is extremely sensitive to the choice of this radius hyper-parameter, and that tuning it is quite difficult, with the original heuristic frequently failing. We thus introduce作者: Constituent 時間: 2025-3-30 08:51
Computer Vision – ECCV 2024978-3-031-73010-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 僵硬 時間: 2025-3-30 15:28
https://doi.org/10.1007/978-3-031-73010-8artificial intelligence; computer networks; computer systems; computer vision; education; Human-Computer 作者: GULF 時間: 2025-3-30 16:33
978-3-031-73009-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: fabricate 時間: 2025-3-30 23:54 作者: 愛好 時間: 2025-3-31 00:57
https://doi.org/10.1007/978-3-0348-6370-4nuous linguistic features through our proposed multimodal contrastive regression loss, which customizes adaptive weights for different negative samples. Furthermore, to better adapt to the labels for gaze estimation task, we propose a geometry-aware interpolation method to obtain more precise gaze e作者: 全等 時間: 2025-3-31 07:33 作者: Essential 時間: 2025-3-31 11:17 作者: intertwine 時間: 2025-3-31 16:49
Angelika D?rfler-Dierken,Gerhard Kümmeldal alignment using the integrated representations, focusing on hard negatives to boost the learning of fine-grained cross-modal alignment. Third, comprehensive cross-modal alignment (C-CmA) extracts low- and high-level fashion information from?the text and learns the semantic alignment to encourage作者: CRAMP 時間: 2025-3-31 18:03 作者: Flat-Feet 時間: 2025-3-31 23:56
Angelika D?rfler-Dierken,Gerhard Kümmelrt methods?in terms of accuracy and speed, showing generalizability to?both scenarios. It is robust to different image sizes and camera intrinsics, and can be deployed with low computing resources. Project page: ..作者: 聲音刺耳 時間: 2025-4-1 03:29 作者: KEGEL 時間: 2025-4-1 07:57 作者: 顛簸下上 時間: 2025-4-1 11:54
The Physical Basis of Ambient Intelligencenovel gate formulation designed to maximize expert capabilities and propose a resolution-based routing technique to effectively induce sparsity and decompose scenes. Our work significantly improves reconstruction quality while maintaining competitive performance.作者: recede 時間: 2025-4-1 17:21
The Physical Basis of Ambient Intelligencediffusion model to generate realistic and diverse anomalies on specific objects (or textures). In the final stage, we propose a weakly-supervised anomaly detection method to train a more powerful model with generated anomalies. Our method builds upon DRAEM and DesTSeg as the foundation model and con作者: COUCH 時間: 2025-4-1 21:01
