作者: 誤傳 時間: 2025-3-21 20:59
,Face-Adapter for?Pre-trained Diffusion Models with?Fine-Grained ID and?Attribute Control,perior generation capabilities. However, training these models is resource-intensive, and the results have not yet achieved satisfactory performance levels. To address this issue, we introduce ., an efficient and effective adapter designed for high-precision and high-fidelity face editing for pre-tr作者: projectile 時間: 2025-3-22 03:25
,WeConvene: Learned Image Compression with?Wavelet-Domain Convolution and?Entropy Model,sform (DWT). However, LIC mainly reduces spatial redundancy in the autoencoder networks and entropy coding, but has not fully removed the frequency-domain correlation explicitly as in DCT or DWT. To leverage the best of both worlds, we propose a surprisingly simple but efficient WeConvene framework,作者: 有助于 時間: 2025-3-22 07:26
,Grid-Attention: Enhancing Computational Efficiency of?Large Vision Models Without Fine-Tuning,he computer vision field. However, the quartic complexity within the transformer’s Multi-Head Attention (MHA) leads to substantial computational costs in these models whose inputs and outputs are high-resolution. Although several prior works attempted to alleviate this challenge, none have successfu作者: 缺乏 時間: 2025-3-22 12:35
,Mitigating Background Shift in?Class-Incremental Semantic Segmentation, achieve this, two popular strategies are employed: 1) pseudo-labeling and knowledge distillation to preserve prior knowledge; and 2) background weight transfer, which leverages the broad coverage of background in learning new classes by transferring background weight to the new class classifier. Ho作者: ENDOW 時間: 2025-3-22 13:30
,Relation DETR: Exploring Explicit Position Relation Prior for?Object Detection,e problem in transformers from a new perspective, suggesting that it arises from the self-attention that introduces no structural bias over inputs. To address this issue, we explore incorporating position relation prior as attention bias to augment object detection, following the verification of its作者: ENDOW 時間: 2025-3-22 20:22
,BKDSNN: Enhancing the?Performance of?Learning-Based Spiking Neural Networks Training with?Blurred Kls with excellent computing efficiency. By utilizing the surrogate gradient estimation for discrete spikes, learning-based SNN training methods that can achieve ultra-low inference latency (.., number of time-step) have emerged recently. Nevertheless, due to the difficulty of deriving precise gradie作者: arousal 時間: 2025-3-22 22:44
,Agent Attention: On the?Integration of?Softmax and?Linear Attention,l cost restricts its applicability in various scenarios. In this paper, we propose a novel attention paradigm, ., to strike a favorable balance between computational efficiency and representation power. Specifically, the Agent Attention, denoted as a quadruple (.,?.,?.,?.), introduces an additional 作者: Spina-Bifida 時間: 2025-3-23 01:42
,Learning by?Aligning 2D Skeleton Sequences and?Multi-modality Fusion,tions. In contrast with the state-of-the-art method of CASA, where sequences of 3D skeleton coordinates are taken directly as input, our key idea is to use sequences of 2D skeleton heatmaps as input. Unlike CASA which performs self-attention in the temporal domain only, we feed 2D skeleton heatmaps 作者: 適宜 時間: 2025-3-23 09:01
,Resolving Scale Ambiguity in?Multi-view 3D Reconstruction Using Dual-Pixel Sensors,iew 3D reconstruction suffers from unknown scale ambiguity unless a reference object of known size is recorded together with the scene, or the camera poses are pre-calibrated. In this paper, we show that multi-view images recorded by a dual-pixel (DP) sensor allow us to automatically resolve the sca作者: 錯 時間: 2025-3-23 12:14 作者: 驚惶 時間: 2025-3-23 16:02 作者: 嬉耍 時間: 2025-3-23 18:30 作者: 舉止粗野的人 時間: 2025-3-23 23:19 作者: 幾何學家 時間: 2025-3-24 04:41
,Dual-Camera Smooth Zoom on?Mobile Phones,er’s zoom experience. In this work, we introduce a new task, .., dual-camera smooth zoom (DCSZ) to achieve a smooth zoom preview. The frame interpolation (FI) technique is a potential solution but struggles with ground-truth collection. To address the issue, we suggest a data factory solution where 作者: Cpr951 時間: 2025-3-24 08:46
,ProtoComp: Diverse Point Cloud Completion with?Controllable Prototype,el is carried out on synthetic datasets, which have limited categories and deviate significantly from real-world scenarios. This disparity often leads existing methods to struggle with unfamiliar categories and severe incompleteness in real-world situations. In this paper, we propose ., a novel prot作者: 因無茶而冷淡 時間: 2025-3-24 14:40 作者: LAIR 時間: 2025-3-24 17:37 作者: 陰謀 時間: 2025-3-24 19:02 作者: Customary 時間: 2025-3-25 00:31
Conference proceedings 2025orcement 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: 上腭 時間: 2025-3-25 06:14 作者: Banister 時間: 2025-3-25 07:29 作者: 疾馳 時間: 2025-3-25 15:04 作者: 啟發(fā) 時間: 2025-3-25 16:44
https://doi.org/10.1007/978-3-031-12797-7bservation, we develop a simple yet effective linear solution method to determine the absolute scale in multi-view 3D reconstruction. Experiments demonstrate the effectiveness of the proposed method with diverse scenes recorded with different cameras/lenses. Code and data are available at ..作者: CLAMP 時間: 2025-3-25 23:45 作者: 遺傳 時間: 2025-3-26 00:56
https://doi.org/10.1007/978-94-017-6654-8mited data scale. High-quality data with simultaneously captured human and 3D environments is hard to acquire, resulting in limited data diversity and complexity. In this work, we argue that interaction with a scene is essentially interacting with the space occupancy of the scene from an ., leading 作者: Detoxification 時間: 2025-3-26 08:14
https://doi.org/10.1007/978-94-017-6654-8perior generation capabilities. However, training these models is resource-intensive, and the results have not yet achieved satisfactory performance levels. To address this issue, we introduce ., an efficient and effective adapter designed for high-precision and high-fidelity face editing for pre-tr作者: cipher 時間: 2025-3-26 10:24 作者: demote 時間: 2025-3-26 14:09 作者: BRINK 時間: 2025-3-26 20:03 作者: frenzy 時間: 2025-3-26 21:23
https://doi.org/10.1007/978-94-017-6654-8e problem in transformers from a new perspective, suggesting that it arises from the self-attention that introduces no structural bias over inputs. To address this issue, we explore incorporating position relation prior as attention bias to augment object detection, following the verification of its作者: 消音器 時間: 2025-3-27 03:48 作者: 羅盤 時間: 2025-3-27 07:51 作者: 后退 時間: 2025-3-27 13:30
Alva Myrdal and Disarmament in a Man’s Worldtions. In contrast with the state-of-the-art method of CASA, where sequences of 3D skeleton coordinates are taken directly as input, our key idea is to use sequences of 2D skeleton heatmaps as input. Unlike CASA which performs self-attention in the temporal domain only, we feed 2D skeleton heatmaps 作者: nocturia 時間: 2025-3-27 13:36 作者: 維持 時間: 2025-3-27 19:06
Plantinga’s Theory of Proper Namesetworks and pre-training tasks. Single-stream networks can effectively leverage self-attention mechanisms to facilitate modality interactions but suffer from high computational complexity and limited applicability to downstream retrieval tasks. In contrast, dual-stream networks address these issues 作者: DOTE 時間: 2025-3-28 00:50
Plantinga on Trans-World Identitycessed by existing metrics such as mAP and MOTA, and consequently is less explored by the community. To bridge this gap, this work proposes Stability Index (SI), a new metric that can comprehensively evaluate the stability of 3D detectors in terms of confidence, box localization, extent, and heading作者: Mobile 時間: 2025-3-28 03:17 作者: 審問,審訊 時間: 2025-3-28 09:14
The Built-In Doctor: Antivirus Programstions with the same training and testing label space. However, in the real world, unknown classes not encountered during training may appear during testing, making it difficult to apply existing methodologies. In this paper, we propose a novel . method for LiDAR semantic segmentation, aiming to clas作者: 傳染 時間: 2025-3-28 12:53
Guardians at the Gate: Firewallser’s zoom experience. In this work, we introduce a new task, .., dual-camera smooth zoom (DCSZ) to achieve a smooth zoom preview. The frame interpolation (FI) technique is a potential solution but struggles with ground-truth collection. To address the issue, we suggest a data factory solution where 作者: 禁止,切斷 時間: 2025-3-28 18:28 作者: municipality 時間: 2025-3-28 19:15 作者: 先兆 時間: 2025-3-28 23:20
Morphologische und funktionelle Bildgebung tasks. However, current learnable prompt tokens are primarily used for the single phase of adapting to tasks (i.e., adapting prompt), easily leading to overfitting risks. In this work, we propose a novel .cade .rompt .earning (.) framework to enable prompt learning to serve both generic and specifi作者: NIP 時間: 2025-3-29 05:57
Behandlung nicht-kognitiver St?rungenthis paper, we aim to reconstruct the floorplan with a vectorized representation from point clouds. Despite significant advancements achieved in recent years, current methods still encounter several challenges, such as missing corners or edges, inaccuracies in corner positions or angles, self-inters作者: 神刊 時間: 2025-3-29 07:47
Computer Vision – ECCV 2024978-3-031-72973-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Subjugate 時間: 2025-3-29 12:34
https://doi.org/10.1007/978-3-031-72973-7artificial intelligence; computer networks; computer systems; computer vision; education; Human-Computer 作者: 憤慨一下 時間: 2025-3-29 15:33
978-3-031-72972-0The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 腐敗 時間: 2025-3-29 21:39 作者: 乞討 時間: 2025-3-30 03:09
,Revisit Human-Scene Interaction via?Space Occupancy,nteraction, a single motion controller is proposed to reach the target state given the surrounding occupancy. Once trained on MOB with complex occupancy layout, which is stringent to human movements, the controller could handle cramped scenes and generalize well to general scenes with limited comple作者: 不足的東西 時間: 2025-3-30 06:52
,Face-Adapter for?Pre-trained Diffusion Models with?Fine-Grained ID and?Attribute Control,a transformer decoder. 3) An Attribute Controller that integrates spatial conditions and detailed attributes. Face-Adapter achieves comparable or even superior performance in terms of motion control precision, ID retention capability, and generation quality compared to fully fine-tuned face reenactm作者: strdulate 時間: 2025-3-30 08:39
,WeConvene: Learned Image Compression with?Wavelet-Domain Convolution and?Entropy Model,rser in DWT domain. We also propose a .av.let-domain .annel-wise .uto-.egressive entropy .odel (WeChARM), where the output latent representations from the encoder network are first transformed by the DWT, before applying quantization and entropy coding, as in the traditional paradigm. Moreover, the 作者: 停止償付 時間: 2025-3-30 13:49
,Grid-Attention: Enhancing Computational Efficiency of?Large Vision Models Without Fine-Tuning,MHA to enhance the large vision models’ computational efficiency and preserve their performance without the need for re-training or fine-tuning their parameters. We conduct extensive experiments on recent high-resolution tasks, including zero-shot instance segmentation (SAM, Expedit-SAM), text-to-im作者: Outspoken 時間: 2025-3-30 19:27 作者: notion 時間: 2025-3-30 20:43 作者: subordinate 時間: 2025-3-31 02:24 作者: 疏忽 時間: 2025-3-31 06:30 作者: 陳腐的人 時間: 2025-3-31 10:33
,Learning by?Aligning 2D Skeleton Sequences and?Multi-modality Fusion,nsive evaluations on three public datasets, i.e., Penn Action, IKEA ASM, and H2O, demonstrate that our approach outperforms previous methods in different fine-grained human activity understanding tasks. Finally, fusing 2D skeleton heatmaps with RGB videos yields the state-of-the-art on all metrics a作者: bronchodilator 時間: 2025-3-31 13:23
,Object-Oriented Anchoring and?Modal Alignment in?Multimodal Learning,ile also preserving explicit semantics for modality interactions. Additionally, we design fine-grained token-level asymmetry alignment between modalities and multiview mining to promote modality alignment. To the best of our knowledge, we are the first to apply object-oriented tokens in multimodal p作者: 光滑 時間: 2025-3-31 19:45 作者: 指耕作 時間: 2025-4-1 01:44
,FYI: Flip Your Images for?Dataset Distillation,ue for dataset distillation, dubbed FYI, that enables distilling rich semantics of real images into synthetic ones. To this end, FYI embeds a horizontal flipping technique into distillation processes, mitigating the influence of the bilateral equivalence, while capturing more details of objects. Exp作者: 獨白 時間: 2025-4-1 04:30 作者: insurrection 時間: 2025-4-1 10:00 作者: BABY 時間: 2025-4-1 13:49 作者: Expand 時間: 2025-4-1 16:03 作者: Palpate 時間: 2025-4-1 20:30 作者: 謙虛的人 時間: 2025-4-2 01:39
,PolyRoom: Room-Aware Transformer for?Floorplan Reconstruction,onally, we propose a room-aware query initialization scheme to prevent non-polygonal sequences and introduce room-aware self-attention to enhance memory efficiency and model performance. Experimental results on two widely used datasets demonstrate that PolyRoom surpasses current state-of-the-art met
