作者: 破布 時(shí)間: 2025-3-21 20:29 作者: carbohydrate 時(shí)間: 2025-3-22 04:08 作者: Champion 時(shí)間: 2025-3-22 06:27 作者: idiopathic 時(shí)間: 2025-3-22 09:59
,Continual Learning for?Remote Physiological Measurement: Minimize Forgetting and?Simplify Inferencesolidate the past knowledge and an inference simplification strategy to convert potentially forgotten tasks into familiar ones for the model. To evaluate ADDP and enable fair comparisons, we create the first continual learning protocol for rPPG measurement. Comprehensive experiments demonstrate the 作者: indigenous 時(shí)間: 2025-3-22 15:06 作者: indigenous 時(shí)間: 2025-3-22 19:53 作者: expeditious 時(shí)間: 2025-3-23 00:57 作者: CHECK 時(shí)間: 2025-3-23 01:50
Efficient NeRF Optimization - Not All Samples Remain Equally Hard,strate the effectiveness of the proposed approach, we apply our method to Instant-NGP, resulting in significant improvements of the view-synthesis quality over the baseline (1 dB improvement on average per training time, or 2x speedup to reach the same PSNR level) along with .40% memory savings comi作者: 推延 時(shí)間: 2025-3-23 07:36
,Revisiting Calibration of?Wide-Angle Radially Symmetric Cameras,g onto it. The . is used in a subsequent robust non-linear optimization process to determine the camera parameters for any radially symmetric model provided as input. By disentangling the estimation of camera model parameters from the ., which is based only on the assumption of radial symmetry in th作者: 大約冬季 時(shí)間: 2025-3-23 12:34 作者: 相一致 時(shí)間: 2025-3-23 14:03
,Robust Incremental Structure-from-Motion with?Hybrid Features,dition to points, leverages lines and their structured geometric relations. Our technical contributions span the entire pipeline (mapping, triangulation, registration) and we integrate these into a comprehensive end-to-end SfM system that we share as an open-source software with the community. We al作者: 退出可食用 時(shí)間: 2025-3-23 20:44
,Revisiting Domain-Adaptive Object Detection in?Adverse Weather by?the?Generation and?Composition ofce performs poorly in cross-domain noisy image scenes. Moreover, relying exclusively on predictions from the teacher model could cause the student model to collapse. Accordingly, in the composition phase, we introduce the mean-teacher model with a joint-filtering and student-aware strategy combining作者: 消極詞匯 時(shí)間: 2025-3-23 23:43
,Prediction Exposes Your Face: Black-Box Model Inversion via?Prediction Alignment,ctor space can be well aligned with the more disentangled latent space, thus establishing a connection between prediction vectors and the semantic facial features. During the attack phase, we further design the Aligned Ensemble Attack scheme to integrate complementary facial attributes of target ide作者: 慢慢沖刷 時(shí)間: 2025-3-24 04:37
UniCal: Unified Neural Sensor Calibration,ducials. This “drive-and-calibrate” approach significantly reduces costs and operational overhead compared to existing calibration systems, enabling efficient calibration for large SDV fleets at scale. To ensure geometric consistency across observations from different sensors, we introduce a novel s作者: Urea508 時(shí)間: 2025-3-24 10:16
Allgemeine und Spezielle Pathologie-time planners. On the other hand, the proposed framework decouples the inference processes of the LLM and real-time planners. By capitalizing on the asynchronous nature of their inference frequencies, our approach have successfully reduced the computational cost introduced by LLM, while maintaining作者: conception 時(shí)間: 2025-3-24 14:05 作者: Nonporous 時(shí)間: 2025-3-24 17:43 作者: 社團(tuán) 時(shí)間: 2025-3-24 20:59 作者: 并置 時(shí)間: 2025-3-25 02:55
Extrakardiale Clicks bei Herzschrittmachersolidate the past knowledge and an inference simplification strategy to convert potentially forgotten tasks into familiar ones for the model. To evaluate ADDP and enable fair comparisons, we create the first continual learning protocol for rPPG measurement. Comprehensive experiments demonstrate the 作者: Gratuitous 時(shí)間: 2025-3-25 05:37
Extrakardiale Clicks bei Herzschrittmacher The fourth stage solves an MRF problem to associate each mesh face with a selected view. In particular, the third and fourth stages are iterated, with the cuts obtained in the fourth stage encouraging non-rigid alignment in the third stage to focus on regions close to the cuts. Experimental results作者: Anemia 時(shí)間: 2025-3-25 11:16 作者: 駕駛 時(shí)間: 2025-3-25 11:39
https://doi.org/10.1007/978-3-662-05663-9 keyframe selection methods. Experimental results demonstrate the effectiveness of our approach, showing incredibly fast speeds up to 107 FPS (for the entire system) and superior quality of the reconstructed map..The code is available at: .Video is: ..作者: happiness 時(shí)間: 2025-3-25 18:53
Chirurgie des praktischen Arztesstrate the effectiveness of the proposed approach, we apply our method to Instant-NGP, resulting in significant improvements of the view-synthesis quality over the baseline (1 dB improvement on average per training time, or 2x speedup to reach the same PSNR level) along with .40% memory savings comi作者: ornithology 時(shí)間: 2025-3-25 22:55 作者: 功多汁水 時(shí)間: 2025-3-26 03:32
M. Rossetti,E. Gr?del,E. C. Yasargil, an unsupervised Transformer-based encoder-decoder method for raw-to-raw translation. It accurately maps raw images captured by a certain camera to the target camera, facilitating the generalization of learnable ISPs to new unseen cameras. Our method demonstrates superior performance on real camera作者: 替代品 時(shí)間: 2025-3-26 07:17 作者: gait-cycle 時(shí)間: 2025-3-26 10:45
Chirurgie des praktischen Arztesce performs poorly in cross-domain noisy image scenes. Moreover, relying exclusively on predictions from the teacher model could cause the student model to collapse. Accordingly, in the composition phase, we introduce the mean-teacher model with a joint-filtering and student-aware strategy combining作者: 摘要記錄 時(shí)間: 2025-3-26 16:06 作者: 拱形面包 時(shí)間: 2025-3-26 19:10 作者: HARD 時(shí)間: 2025-3-26 22:00 作者: 通便 時(shí)間: 2025-3-27 01:27
0302-9743 reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..978-3-031-72763-4978-3-031-72764-1Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 遍及 時(shí)間: 2025-3-27 06:48
Ursus-Nikolaus Riede,Martin Wernerimage enhancement. InstructIR improves +1dB over previous all-in-one restoration methods. Moreover, our dataset and results represent a novel benchmark for new research on text-guided image restoration and enhancement.作者: rheumatology 時(shí)間: 2025-3-27 11:48
,Pharmakologie des lymphocyt?ren Systems,ned to stabilize model optimization for diminishing cross-domain variance during training. Collectively, the whole RES method can significantly enhance model generalization. We evaluate RES on five benchmark datasets and the results show that it outperforms multiple advanced DG methods. Our code will be available at ..作者: Longitude 時(shí)間: 2025-3-27 13:44
M. Rossetti,M. Allg?wer,R. Berchtold noise optimization strategy, referred to as .. By refining the initial random noise through a few iterations, the content of original video can?be largely preserved, and the enhancement effect demonstrates a notable improvement. Extensive experiments have demonstrated?the effectiveness of the proposed method.作者: 確定的事 時(shí)間: 2025-3-27 19:05
InstructIR: High-Quality Image Restoration Following Human Instructions,image enhancement. InstructIR improves +1dB over previous all-in-one restoration methods. Moreover, our dataset and results represent a novel benchmark for new research on text-guided image restoration and enhancement.作者: Congestion 時(shí)間: 2025-3-28 01:13 作者: 玉米 時(shí)間: 2025-3-28 04:18
Noise Calibration: Plug-and-Play Content-Preserving Video Enhancement Using Pre-trained Video Diffu noise optimization strategy, referred to as .. By refining the initial random noise through a few iterations, the content of original video can?be largely preserved, and the enhancement effect demonstrates a notable improvement. Extensive experiments have demonstrated?the effectiveness of the proposed method.作者: 捐助 時(shí)間: 2025-3-28 08:22 作者: 招致 時(shí)間: 2025-3-28 11:50 作者: 清真寺 時(shí)間: 2025-3-28 16:23
Ursus-Nikolaus Riede,Martin Wernere that allows us to handle various generation tasks with varying degrees of conditioning with a single model. Empirically, LayoutFlow performs on par with state-of-the-art models while being significantly faster. The project page, including our code, can be found at ..作者: 失誤 時(shí)間: 2025-3-28 19:05 作者: 哭得清醒了 時(shí)間: 2025-3-29 01:25 作者: MILL 時(shí)間: 2025-3-29 05:50
,Asynchronous Large Language Model Enhanced Planner for?Autonomous Driving, avenues for enhancing the interpretability and controllability of motion planning. Nevertheless, LLM-based planners continue to encounter significant challenges, including elevated resource consumption and extended inference times, which pose substantial obstacles to practical deployment. In light 作者: ingrate 時(shí)間: 2025-3-29 09:47
,Make a?Cheap Scaling: A Self-Cascade Diffusion Model for?Higher-Resolution Adaptation,jects when generating images of varying sizes due to single-scale training data. Adapting large pre-trained diffusion models to higher resolution demands substantial computational and optimization resources, yet achieving generation capabilities comparable to low-resolution models remains challengin作者: Basal-Ganglia 時(shí)間: 2025-3-29 13:07 作者: 刪除 時(shí)間: 2025-3-29 17:13
,Making Large Language Models Better Planners with?Reasoning-Decision Alignment,lity. Inspired by the knowledge-driven nature of human driving, recent approaches explore the potential of large language models (LLMs) to improve understanding and decision-making in traffic scenarios. They find that the pretrain-finetune paradigm of LLMs on downstream data with the Chain-of-Though作者: Truculent 時(shí)間: 2025-3-29 22:22 作者: CRACY 時(shí)間: 2025-3-30 02:19
,Representation Enhancement-Stabilization: Reducing Bias-Variance of?Domain Generalization,t domains. This paper explores DG through the lens of bias-variance decomposition, uncovering that test errors in DG predominantly arise from cross-domain bias and variance. Inspired by this insight, we introduce a Representation Enhancement-Stabilization (RES) framework, comprising a Representation作者: 草率女 時(shí)間: 2025-3-30 05:43 作者: Eructation 時(shí)間: 2025-3-30 11:59
,An Optimization Framework to?Enforce Multi-view Consistency for?Texturing 3D Meshes,aches typically use diffusion models to aggregate multi-view inputs, where common issues are the blurriness caused by the averaging operation in the aggregation step or inconsistencies in local features. This paper introduces an optimization framework that proceeds in four stages to achieve multi-vi作者: 表主動(dòng) 時(shí)間: 2025-3-30 12:54
STAG4D: Spatial-Temporal Anchored Generative 4D Gaussians,neration with spatial-temporal consistency remains a challenge. In this work, we propose STAG4D, a novel framework that combines pre-trained diffusion models with dynamic 3D Gaussian splatting for high-fidelity 4D generation. Drawing inspiration from 3D generation techniques, we utilize a multi-view作者: Osmosis 時(shí)間: 2025-3-30 19:58 作者: ADORE 時(shí)間: 2025-3-31 00:01
Efficient NeRF Optimization - Not All Samples Remain Equally Hard,uality for many 3D reconstruction and rendering tasks but require substantial computational resources. The encoding of the scene information within the NeRF network parameters necessitates stochastic sampling. We observe that during the training, a major part of the compute time and memory usage is 作者: licence 時(shí)間: 2025-3-31 01:03
,Revisiting Calibration of?Wide-Angle Radially Symmetric Cameras,se end-to-end approaches are typically tethered to one fixed camera model, leading to issues: . lack of flexibility, necessitating network architectural changes and retraining when changing camera models; . reduced accuracy, as a single model limits the diversity of cameras represented in the traini作者: Carcinoma 時(shí)間: 2025-3-31 05:14
,Rawformer: Unpaired Raw-to-Raw Translation for?Learnable Camera ISPs,es to produce final output images encoded in a standard color space (e.g., sRGB). Neural-based end-to-end learnable ISPs offer promising advancements, potentially replacing traditional ISPs with their ability to adapt without requiring extensive tuning for each new camera model, as is often the case作者: 農(nóng)學(xué) 時(shí)間: 2025-3-31 13:13 作者: Memorial 時(shí)間: 2025-3-31 17:14
,Revisiting Domain-Adaptive Object Detection in?Adverse Weather by?the?Generation and?Composition of in adverse weather conditions. Despite significant progress, existing methods are still plagued by low-quality pseudo-labels in degraded images. This paper proposes a generation-composition paradigm training framework that includes the tiny-object-friendly loss, i.e., IAoU loss with a joint-filteri作者: 殘廢的火焰 時(shí)間: 2025-3-31 18:04 作者: 聲音刺耳 時(shí)間: 2025-3-31 21:44
Noise Calibration: Plug-and-Play Content-Preserving Video Enhancement Using Pre-trained Video Diffuenting a noising-denoising process for refinement. Despite the significant training costs, maintaining consistency of content between the original and enhanced videos remains a major challenge. To tackle this challenge, we propose?a novel formulation that considers both visual quality and consistenc作者: 厭煩 時(shí)間: 2025-4-1 05:22
UniCal: Unified Neural Sensor Calibration,ethods typically leverage fiducials captured in a controlled and structured scene and compute correspondences to optimize over. These approaches are costly?and require substantial infrastructure and operations, making?it challenging to scale for vehicle fleets. In this work, we propose UniCal, a uni作者: Thyroid-Gland 時(shí)間: 2025-4-1 08:30
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/242321.jpg作者: 小步走路 時(shí)間: 2025-4-1 13:04
Ursus-Nikolaus Riede,Martin Wernertion models can effectively restore images from various types and levels of degradation using degradation-specific information as prompts to guide the restoration model. In this work, we present the first approach that uses human-written instructions to guide the image restoration model. Given natur作者: 江湖郎中 時(shí)間: 2025-4-1 16:08
