作者: 誰(shuí)在削木頭 時(shí)間: 2025-3-21 21:29
Context-Enriched Personal Health Monitoringomplex video games such as Grand Theft Auto (GTA) and Minecraft. To train Octopus, we leverage GPT-4 to control an explorative agent that generates training data, i.e., action blueprints and corresponding executable code. We also collect feedback that enables an enhanced training scheme called .. Th作者: handle 時(shí)間: 2025-3-22 01:51 作者: STALE 時(shí)間: 2025-3-22 05:49
https://doi.org/10.1007/978-3-642-37988-8from camera views. Extensive comparative and ablation studies across 11 heterogeneous LiDAR datasets validate our effectiveness and superiority. Additionally, we observe several interesting emerging properties by scaling up the 2D and 3D backbones during pretraining, shedding light on the future res作者: 配置 時(shí)間: 2025-3-22 09:51 作者: magnanimity 時(shí)間: 2025-3-22 14:45 作者: magnanimity 時(shí)間: 2025-3-22 18:38 作者: 小故事 時(shí)間: 2025-3-22 22:01 作者: bronchiole 時(shí)間: 2025-3-23 04:48 作者: Neutral-Spine 時(shí)間: 2025-3-23 07:47
Lecture Notes in Electrical Engineeringt using any 3D labels, our method achieves favorable performance against state-of-the-art approaches and is competitive with the method that uses 500-frame 3D annotations. Code and models will be made publicly available.作者: Fortuitous 時(shí)間: 2025-3-23 11:14
Nadia Mana,Ornella Mich,Michela Ferronrred events, generated under high-speed or low-light conditions. The core component of this work is a physically-accurate pixel bandwidth model that accounts for event motion blur. We also introduce a threshold-normalized total variation loss to better regularize large textureless patches. Experimen作者: placebo-effect 時(shí)間: 2025-3-23 16:14
Claudia Porfirione,Francesco Burlando are both informative to the current model and persistently challenging throughout active learning. During adaptation, we learn from features of actively selected anchors obtained from previous intermediate models, so that the . can facilitate feature distribution alignment and active sample exploit作者: 冥界三河 時(shí)間: 2025-3-23 20:33 作者: RENIN 時(shí)間: 2025-3-23 23:59 作者: palliate 時(shí)間: 2025-3-24 05:06 作者: BAIT 時(shí)間: 2025-3-24 07:44 作者: 別名 時(shí)間: 2025-3-24 10:44 作者: 反對(duì) 時(shí)間: 2025-3-24 16:50 作者: Complement 時(shí)間: 2025-3-24 21:58 作者: MOAN 時(shí)間: 2025-3-25 01:38 作者: Debark 時(shí)間: 2025-3-25 03:26 作者: 極小 時(shí)間: 2025-3-25 10:05 作者: 發(fā)微光 時(shí)間: 2025-3-25 13:40 作者: fructose 時(shí)間: 2025-3-25 16:07 作者: 元音 時(shí)間: 2025-3-25 23:53 作者: Metamorphosis 時(shí)間: 2025-3-26 01:31 作者: DEVIL 時(shí)間: 2025-3-26 06:55
,Deblur ,-NeRF: NeRF from?Motion-Blurred Events under High-speed or?Low-light Conditions,rred events, generated under high-speed or low-light conditions. The core component of this work is a physically-accurate pixel bandwidth model that accounts for event motion blur. We also introduce a threshold-normalized total variation loss to better regularize large textureless patches. Experimen作者: NAG 時(shí)間: 2025-3-26 11:06
,Learn from?the?Learnt: Source-Free Active Domain Adaptation via?Contrastive Sampling and?Visual Per are both informative to the current model and persistently challenging throughout active learning. During adaptation, we learn from features of actively selected anchors obtained from previous intermediate models, so that the . can facilitate feature distribution alignment and active sample exploit作者: 明智的人 時(shí)間: 2025-3-26 16:33
,Motion Mamba: Efficient and?Long Sequence Motion Generation,ween frames. We also design a . (.) block to bidirectionally process latent poses, to enhance accurate motion generation within a temporal frame. Our proposed method achieves up to . FID improvement and up to . times faster on the HumanML3D and KIT-ML datasets compared to the previous best diffusion作者: 臆斷 時(shí)間: 2025-3-26 16:57 作者: 小溪 時(shí)間: 2025-3-27 00:35
Tracking Meets LoRA: Faster Training, Larger Model, Stronger Performance,dels. To overcome these limitations, we first decouple the position embeddings in transformer-based trackers into shared spatial ones and independent type ones. The shared embeddings, which describe the absolute coordinates of multi-resolution images (namely, the template and search images), are inh作者: fleeting 時(shí)間: 2025-3-27 03:31
,CoR-GS: Sparse-View 3D Gaussian Splatting via?Co-regularization,: (i) Co-pruning considers Gaussians that exhibit high point disagreement in inaccurate positions and prunes them. (ii) Pseudo-view co-regularization considers pixels that exhibit high rendering disagreement are inaccurate and suppress the disagreement. Results on LLFF, Mip-NeRF360, DTU, and Blender作者: indices 時(shí)間: 2025-3-27 08:10 作者: arthroplasty 時(shí)間: 2025-3-27 09:50 作者: 絆住 時(shí)間: 2025-3-27 13:46
0302-9743 reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation..978-3-031-73231-7978-3-031-73232-4Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 使混合 時(shí)間: 2025-3-27 17:53 作者: 情愛(ài) 時(shí)間: 2025-3-28 01:16
Lecture Notes in Electrical Engineeringaset with two proposed data augmentation strategies to learn diverse requirements, thus enabling it to fast adapt to new requirements. Experiments indicate that the PromptIQA outperforms SOTA methods with higher performance and better generalization. The code is available at the ..作者: 等待 時(shí)間: 2025-3-28 03:48
Advanced Technologies and Societal Changeits usefulness in understanding real structure from motion graphs; we propose an algorithm for testing infinitesimal solvability?and extracting components of unsolvable cases, that is more efficient than previous work; we set up an open question on the connection between infinitesimal solvability?and solvability.作者: coddle 時(shí)間: 2025-3-28 07:42 作者: Host142 時(shí)間: 2025-3-28 11:30 作者: 無(wú)孔 時(shí)間: 2025-3-28 17:12
,A Direct Approach to?Viewing Graph Solvability,its usefulness in understanding real structure from motion graphs; we propose an algorithm for testing infinitesimal solvability?and extracting components of unsolvable cases, that is more efficient than previous work; we set up an open question on the connection between infinitesimal solvability?and solvability.作者: Canopy 時(shí)間: 2025-3-28 21:51 作者: 關(guān)節(jié)炎 時(shí)間: 2025-3-29 02:12 作者: Uncultured 時(shí)間: 2025-3-29 07:09 作者: 新手 時(shí)間: 2025-3-29 10:05
, FunQA: Towards Surprising Video Comprehension,a response to visual stimuli; rather, it hinges on the human capacity to understand (and appreciate) commonsense violations depicted in these videos. We introduce ., a challenging video question answering (QA) dataset specifically designed to evaluate and enhance the depth of video reasoning based o作者: Aspirin 時(shí)間: 2025-3-29 12:22
4D Contrastive Superflows are Dense 3D Representation Learners,a process that is both costly and labor-intensive. To address this challenge from a data representation learning perspective, we introduce ., a novel framework designed to harness consecutive LiDAR-camera pairs for establishing spatiotemporal pretraining objectives. SuperFlow stands out by integrati作者: 滑動(dòng) 時(shí)間: 2025-3-29 19:26
,ItTakesTwo: Leveraging Peer Representations for?Semi-supervised LiDAR Semantic Segmentation,velopment of semi-supervised learning (SSL) methods. However, such SSL approaches often concentrate on employing consistency learning only for individual LiDAR representations. This narrow focus results in limited perturbations that generally fail to enable effective consistency learning. Additional作者: 楓樹(shù) 時(shí)間: 2025-3-29 23:48 作者: pulse-pressure 時(shí)間: 2025-3-30 01:53
,Robust Fitting on?a?Gate Quantum Computer,ial time. Computer vision researchers have long been attracted to the power of quantum computers. Robust fitting, which is fundamentally important to many computer vision pipelines, has recently been shown to be amenable to gate quantum computing. The previous proposed solution was to compute Boolea作者: PLE 時(shí)間: 2025-3-30 06:42
,H-V2X: A Large Scale Highway Dataset for?BEV Perception,ts. However, these datasets primarily focus on urban intersections and lack data on highway scenarios. Additionally, the perception tasks in the datasets are mainly MONO 3D due to limited synchronized data across multiple sensors. To bridge this gap, we propose Highway-V2X (H-V2X), the first large-s作者: Tinea-Capitis 時(shí)間: 2025-3-30 09:49
,Learning Camouflaged Object Detection from?Noisy Pseudo Label,-intensive. Although weakly supervised methods offer higher annotation efficiency, their performance is far behind due to the unclear visual demarcations between foreground and background in camouflaged images. In this paper, we explore the potential of using boxes as prompts in camouflaged scenes a作者: Missile 時(shí)間: 2025-3-30 14:38
,Weakly Supervised 3D Object Detection via?Multi-level Visual Guidance, few accurate 3D annotations, we propose a framework to study how to leverage constraints between 2D and 3D domains without requiring any 3D labels. Specifically, we employ visual data from three perspectives to establish connections between 2D and 3D domains. First, we design a feature-level constr作者: 美食家 時(shí)間: 2025-3-30 20:00
,Deblur ,-NeRF: NeRF from?Motion-Blurred Events under High-speed or?Low-light Conditions,s underperform. However, event cameras also suffer from motion blur, especially under these challenging conditions, contrary to what most think. This is due to the limited bandwidth of the event sensor pixel, which is mostly proportional to the light intensity. Thus, to ensure event cameras can trul作者: Insatiable 時(shí)間: 2025-3-31 00:10 作者: Abutment 時(shí)間: 2025-3-31 02:08 作者: 子女 時(shí)間: 2025-3-31 05:23 作者: 半球 時(shí)間: 2025-3-31 09:16
,Motion Mamba: Efficient and?Long Sequence Motion Generation,emains challenging. Recent advancements in state space models (SSMs), notably Mamba, have showcased considerable promise in long sequence modeling with an efficient hardware-aware design, which appears to be a significant direction upon building motion generation model. Nevertheless, adapting SSMs t作者: Range-Of-Motion 時(shí)間: 2025-3-31 17:22 作者: nauseate 時(shí)間: 2025-3-31 19:27 作者: Odyssey 時(shí)間: 2025-4-1 01:15 作者: Bumptious 時(shí)間: 2025-4-1 05:30 作者: HARP 時(shí)間: 2025-4-1 08:46
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/242354.jpg作者: 吞噬 時(shí)間: 2025-4-1 14:08 作者: Charade 時(shí)間: 2025-4-1 15:27 作者: Intersect 時(shí)間: 2025-4-1 19:53
Tom Zentek,Alexander Marinc,Asarnusch Rashida response to visual stimuli; rather, it hinges on the human capacity to understand (and appreciate) commonsense violations depicted in these videos. We introduce ., a challenging video question answering (QA) dataset specifically designed to evaluate and enhance the depth of video reasoning based o作者: 干涉 時(shí)間: 2025-4-2 01:37
https://doi.org/10.1007/978-3-642-37988-8a process that is both costly and labor-intensive. To address this challenge from a data representation learning perspective, we introduce ., a novel framework designed to harness consecutive LiDAR-camera pairs for establishing spatiotemporal pretraining objectives. SuperFlow stands out by integrati
