作者: lactic 時間: 2025-3-21 22:06 作者: Acetabulum 時間: 2025-3-22 02:28
,CanonicalFusion: Generating Drivable 3D Human Avatars from?Multiple Images,tegrating individual reconstruction results into the canonical space. To be specific, we first predict Linear Blend Skinning (LBS) weight maps and depth maps using a shared-encoder-dual-decoder network, enabling direct canonicalization of the 3D mesh from the predicted depth maps. Here, instead of p作者: 單片眼鏡 時間: 2025-3-22 05:00
,Camera Height Doesn’t Change: Unsupervised Training for?Metric Monocular Road-Scene Depth Estimatioust from regular training data, .., driving videos. We refer to this training framework as FUMET.?The key idea is to leverage cars found on the road as sources of?scale supervision and to incorporate them in network training robustly. FUMET detects and estimates the sizes of cars in a frame?and aggr作者: conservative 時間: 2025-3-22 08:49 作者: fulmination 時間: 2025-3-22 16:43 作者: fulmination 時間: 2025-3-22 19:06 作者: atopic-rhinitis 時間: 2025-3-22 23:38
,GENIXER: Empowering Multimodal Large Language Model as?a?Powerful Data Generator,nerate visual instruction tuning data. This paper proposes to explore the potential of empowering MLLMs to generate data independently without relying on GPT-4. We introduce ., a comprehensive data generation pipeline consisting of four key steps: (i) instruction data collection, (ii) instruction te作者: 生來 時間: 2025-3-23 02:59 作者: committed 時間: 2025-3-23 08:52 作者: 有角 時間: 2025-3-23 12:37
,PreLAR: World Model Pre-training with?Learnable Action Representation,he world model learning requires extensive interactions with the real environment. Therefore, several innovative approaches such as APV proposed to unsupervised pre-train the world model from large-scale videos, allowing fewer interactions to fine-tune the world model. However, these methods only pr作者: Endemic 時間: 2025-3-23 13:59 作者: monopoly 時間: 2025-3-23 21:20 作者: BUOY 時間: 2025-3-23 22:56 作者: 性學(xué)院 時間: 2025-3-24 05:41 作者: 突襲 時間: 2025-3-24 09:39 作者: 無法解釋 時間: 2025-3-24 12:31 作者: enterprise 時間: 2025-3-24 15:05
,LaMI-DETR: Open-Vocabulary Detection with?Language Model Instruction,(VLMs), such as CLIP. However, two?main challenges emerge: (1) A deficiency in concept representation,?where the category names in CLIP’s text space lack textual and visual knowledge. (2) An overfitting tendency towards base categories,?with the open vocabulary knowledge biased towards base categori作者: Ischemia 時間: 2025-3-24 19:10 作者: 甜食 時間: 2025-3-25 00:26
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: eulogize 時間: 2025-3-25 04:21
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: 小爭吵 時間: 2025-3-25 11:17
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: micronutrients 時間: 2025-3-25 14:35 作者: Communal 時間: 2025-3-25 17:31 作者: interrogate 時間: 2025-3-25 21:16
Die Stellungnahme des Kranken zur Krankheit reduces the trade-off between the?two groundings. Our experiments demonstrate significant improvements from the original GLIGEN to the rewired version in the trade-off between textual grounding and spatial grounding. The project webpage is at ..作者: Comprise 時間: 2025-3-26 04:06 作者: 開始沒有 時間: 2025-3-26 05:05
,ReGround: Improving Textual and?Spatial Grounding at?No Cost, reduces the trade-off between the?two groundings. Our experiments demonstrate significant improvements from the original GLIGEN to the rewired version in the trade-off between textual grounding and spatial grounding. The project webpage is at ..作者: certitude 時間: 2025-3-26 08:39 作者: GRUEL 時間: 2025-3-26 14:56
https://doi.org/10.1007/978-3-662-11111-6r goal is to utilize the powerful feature extraction capability of segment anything model (SAM) and?make out-of-domain tuning to help SAM distinguish breast masses?from background. To this end, we propose a novel model called ., which inherits the model architecture of SAM but makes improvements to 作者: Statins 時間: 2025-3-26 18:55 作者: cataract 時間: 2025-3-27 00:19 作者: gait-cycle 時間: 2025-3-27 02:59 作者: 陳列 時間: 2025-3-27 09:06
Die Synthese der Krankheitsbilder,ation, which prompts the development of NIR-to-visible translation tasks. However, the performance of existing translation methods is limited by the neglected disparities between NIR and visible imaging and the lack of paired training data. To address these challenges, we propose a novel object-awar作者: 小卒 時間: 2025-3-27 11:31
Die Stellungnahme des Kranken zur Krankheitminate redundant data for faster processing without compromising accuracy. Previous methods are often architecture-specific or necessitate re-training, restricting their applicability with frequent model updates. To solve this, we first introduce a novel property of lightweight ConvNets: their abili作者: 滔滔不絕地說 時間: 2025-3-27 14:37
Die Stellungnahme des Kranken zur Krankheitnerate visual instruction tuning data. This paper proposes to explore the potential of empowering MLLMs to generate data independently without relying on GPT-4. We introduce ., a comprehensive data generation pipeline consisting of four key steps: (i) instruction data collection, (ii) instruction te作者: 楓樹 時間: 2025-3-27 20:38 作者: 撤退 時間: 2025-3-28 00:35
über Sinn und Wert der Theorienteraction, and the time to contact from?the observation of egocentric video. This ability is fundamental?for wearable assistants or human-robot interaction to understand?the user’s goals, but there is still room for improvement to perform?STA in a precise and reliable way. In this work, we improve?t作者: 過份艷麗 時間: 2025-3-28 05:12
https://doi.org/10.1007/978-3-642-52895-8he world model learning requires extensive interactions with the real environment. Therefore, several innovative approaches such as APV proposed to unsupervised pre-train the world model from large-scale videos, allowing fewer interactions to fine-tune the world model. However, these methods only pr作者: 疼死我了 時間: 2025-3-28 10:05 作者: 表狀態(tài) 時間: 2025-3-28 13:31 作者: oxidant 時間: 2025-3-28 17:24
https://doi.org/10.1007/978-3-642-49689-9ask. Starting with images?that facilitate depth prediction due to the absence of unfavorable factors, we systematically generate new, user-defined scenes with?a comprehensive set of challenges and associated depth information. This is achieved by leveraging cutting-edge text-to-image diffusion model作者: 懦夫 時間: 2025-3-28 22:02
https://doi.org/10.1007/978-3-642-49689-9 through various query styles. However, current retrieval tasks predominantly focus on text-query retrieval exploration, leading to limited retrieval query options and potential ambiguity or bias in user intention. In this paper,?we propose the Style-Diversified Query-Based Image Retrieval task, whi作者: CRASS 時間: 2025-3-28 23:55
Die Stellungnahme des Kranken zur Krankheitone dominate the other? Our analysis of a pretrained image diffusion model that integrates gated self-attention into the U-Net reveals that spatial grounding often outweighs textual grounding due to?the . flow from gated self-attention to cross-attention. We demonstrate that such bias can be signifi作者: POWER 時間: 2025-3-29 04:47 作者: Camouflage 時間: 2025-3-29 11:17 作者: 在前面 時間: 2025-3-29 11:46 作者: Receive 時間: 2025-3-29 18:35
Computer Vision – ECCV 2024978-3-031-73337-6Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: parsimony 時間: 2025-3-29 20:04 作者: 集合 時間: 2025-3-30 02:05
978-3-031-73336-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 推崇 時間: 2025-3-30 07:56 作者: blithe 時間: 2025-3-30 11:56
Die sinnhaften objektiven Tatbest?ndeess we?call “Weak-to-Strong Compositional Learning” (WSCL). To achieve this, we propose a new compositional contrastive learning formulation?that discovers semantics and structures in complex descriptions?from synthetic triplets. As a result, VL models trained with?our synthetic data generation exhi作者: Congeal 時間: 2025-3-30 14:48 作者: 赦免 時間: 2025-3-30 19:05 作者: 熒光 時間: 2025-3-30 21:10
über Sinn und Wert der Theoriens datasets?show the effectiveness of FUMET, which achieves state-of-the-art accuracy. We also show that FUMET enables training on mixed datasets of different camera heights, which leads to larger-scale training and better generalization. Metric depth reconstruction is essential in any road-scene vis作者: 引水渠 時間: 2025-3-31 01:27
https://doi.org/10.1007/978-3-662-11111-6n, visual grounding, 3D captioning, and text-3D cross-modal retrieval.?It demonstrates performance on par with or surpassing state-of-the-art (SOTA) task-specific models. We hope our benchmark and Uni3DL?model will serve as a solid step to ease future research in unified models in the realm of 3D vi作者: 燒瓶 時間: 2025-3-31 05:39
Die Synthese der Krankheitsbilder,gned NIR-Visible Image Dataset, a large-scale dataset comprising fully matched pairs of NIR and visible images captured with a multi-sensor coaxial camera. Empirical evaluations demonstrate our method’s superiority over existing methods, producing visually compelling results on mainstream datasets. 作者: Hypopnea 時間: 2025-3-31 10:41
Die Stellungnahme des Kranken zur Krankheitghtweight ConvNets across a variety of deep learning architectures, including ViTs, ConvNets, and hybrid transformers, without any re-training. Moreover, the simple early-stage one-step patch pruning with PaPr enhances existing patch reduction methods. Through extensive testing on diverse architectu作者: helper-T-cells 時間: 2025-3-31 15:14
Die Stellungnahme des Kranken zur KrankheitREC datasets. Through experiments and synthetic data analysis, our findings are: (1) current MLLMs can serve as robust data generators without assistance from GPT-4V; (2) MLLMs trained with task-specific datasets can surpass GPT-4V in generating complex instruction tuning data; (3) synthetic dataset作者: 不足的東西 時間: 2025-3-31 21:18
Die Stellungnahme des Kranken zur Krankheit have not “emerged” yet in recent multimodal LLMs. Our analysis also highlights that specialist CV models could solve these problems much better, suggesting potential pathways for future improvements. We believe . will stimulate the community to help multimodal LLMs catch up with human-level visual
