作者: HAVOC 時(shí)間: 2025-3-21 20:15 作者: narcotic 時(shí)間: 2025-3-22 02:42
,Uncertainty-Driven Spectral Compressive Imaging with?Spatial-Frequency Transformer,odel cross-window connections, and expand its receptive fields while maintaining linear complexity. We use SF-block as the main building block in a multi-scale U-shape network to form our Specformer. In addition, we introduce an uncertainty-driven loss function, which can reinforce the network’s att作者: 債務(wù) 時(shí)間: 2025-3-22 07:53
,MapTracker: Tracking with?Strided Memory Fusion for?Consistent Vector HD Mapping, produce consistent ground truth with temporal alignments and 2) Augmenting existing mAP metrics with consistency checks. MapTracker significantly outperforms existing methods on both nuScenes and Agroverse2 datasets by over 8% and 19% on the conventional and the new consistency-aware metrics, respe作者: Spinal-Tap 時(shí)間: 2025-3-22 10:42 作者: insecticide 時(shí)間: 2025-3-22 16:49
,X-Former: Unifying Contrastive and?Reconstruction Learning for?MLLMs,n mechanism. Specifically, X-Former first bootstraps vision-language representation learning and multimodal-to-multimodal generative learning from two frozen vision encoders, i.e., CLIP-ViT (CL-based) and MAE-ViT (MIM-based). It further bootstraps vision-to-language generative learning from a frozen作者: insecticide 時(shí)間: 2025-3-22 17:23 作者: arbovirus 時(shí)間: 2025-3-22 23:53
,Revisiting Supervision for?Continual Representation Learning, multi-layer perceptron head, can outperform self-supervised models in continual representation learning. This highlights the importance of the multi-layer perceptron projector in shaping feature transferability across a sequence of tasks in continual learning. The code is available on ..作者: sterilization 時(shí)間: 2025-3-23 02:46 作者: 有斑點(diǎn) 時(shí)間: 2025-3-23 09:28 作者: 背帶 時(shí)間: 2025-3-23 10:33 作者: 愛哭 時(shí)間: 2025-3-23 14:48
,External Knowledge Enhanced 3D Scene Generation from?Sketch,cluding the 3D object instances as well as their layout. Experiments on the 3D-FRONT dataset show that our model improves FID, CKL by 17.41%, 37.18% in 3D scene generation and FID, KID by 19.12%, 20.06% in 3D scene completion compared to the nearest competitor DiffuScene.作者: avulsion 時(shí)間: 2025-3-23 18:28
: Gradient Guided Generalizable Reconstruction,n with data-driven priors from fast feed-forward prediction methods. Experiments on urban-driving and drone datasets show that . generalizes across diverse large scenes and accelerates the reconstruction process by at least . while achieving comparable or better realism compared?to 3DGS, and also be作者: 種類 時(shí)間: 2025-3-24 01:33
,DreamScene360: Unconstrained Text-to-3D Scene Generation with?Panoramic Gaussian Splatting,ues inherent in single-view inputs, we impose semantic and geometric constraints on both synthesized and input camera views as regularizations. These guide the optimization of Gaussians, aiding in the reconstruction of unseen regions. In summary, our method offers a globally consistent 3D scene with作者: hypertension 時(shí)間: 2025-3-24 05:57 作者: sperse 時(shí)間: 2025-3-24 10:30 作者: Glycogen 時(shí)間: 2025-3-24 11:57
https://doi.org/10.1007/3-540-30147-Xodel cross-window connections, and expand its receptive fields while maintaining linear complexity. We use SF-block as the main building block in a multi-scale U-shape network to form our Specformer. In addition, we introduce an uncertainty-driven loss function, which can reinforce the network’s att作者: GET 時(shí)間: 2025-3-24 17:00
Reproduction: Blossoms, Fruits, Seeds produce consistent ground truth with temporal alignments and 2) Augmenting existing mAP metrics with consistency checks. MapTracker significantly outperforms existing methods on both nuScenes and Agroverse2 datasets by over 8% and 19% on the conventional and the new consistency-aware metrics, respe作者: fabricate 時(shí)間: 2025-3-24 19:12 作者: FECK 時(shí)間: 2025-3-25 00:30
https://doi.org/10.1007/978-1-4939-6795-7n mechanism. Specifically, X-Former first bootstraps vision-language representation learning and multimodal-to-multimodal generative learning from two frozen vision encoders, i.e., CLIP-ViT (CL-based) and MAE-ViT (MIM-based). It further bootstraps vision-to-language generative learning from a frozen作者: Ischemia 時(shí)間: 2025-3-25 03:48 作者: DOTE 時(shí)間: 2025-3-25 08:36
Cristina Adams Dr,Rui Murrieta,Walter Neves multi-layer perceptron head, can outperform self-supervised models in continual representation learning. This highlights the importance of the multi-layer perceptron projector in shaping feature transferability across a sequence of tasks in continual learning. The code is available on ..作者: 白楊 時(shí)間: 2025-3-25 12:35
Cristina Adams Dr,Rui Murrieta,Walter Nevesy curated with well-designed quality control schemes, surpassing existing similar benchmarks in terms of the number and variety of evaluation questions and abilities; 2. MMBench introduces a rigorous CircularEval strategy and incorporates large language models to convert free-form predictions into p作者: AIL 時(shí)間: 2025-3-25 15:59 作者: Duodenitis 時(shí)間: 2025-3-25 21:05 作者: antidepressant 時(shí)間: 2025-3-26 01:45
Produkt- und Markenschutz auf Amazoncluding the 3D object instances as well as their layout. Experiments on the 3D-FRONT dataset show that our model improves FID, CKL by 17.41%, 37.18% in 3D scene generation and FID, KID by 19.12%, 20.06% in 3D scene completion compared to the nearest competitor DiffuScene.作者: condone 時(shí)間: 2025-3-26 07:15 作者: 有助于 時(shí)間: 2025-3-26 09:49 作者: Anonymous 時(shí)間: 2025-3-26 16:33 作者: Cacophonous 時(shí)間: 2025-3-26 18:49 作者: 侵蝕 時(shí)間: 2025-3-26 22:43
Advanced Theory of Infinite Seriesmation in the absence of brightness changes. Our techniques are general, can be adapted to any structured representation to stack events and outperform state-of-the-art fusion methods applied to event-based stereo.作者: 推延 時(shí)間: 2025-3-27 02:08 作者: 漸強(qiáng) 時(shí)間: 2025-3-27 06:13
,LiDAR-Event Stereo Fusion with?Hallucinations,mation in the absence of brightness changes. Our techniques are general, can be adapted to any structured representation to stack events and outperform state-of-the-art fusion methods applied to event-based stereo.作者: archetype 時(shí)間: 2025-3-27 13:20 作者: 過于平凡 時(shí)間: 2025-3-27 16:18 作者: 親屬 時(shí)間: 2025-3-27 18:40
0302-9743 reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation..978-3-031-72657-6978-3-031-72658-3Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Generator 時(shí)間: 2025-3-28 01:10
Reproduction: Blossoms, Fruits, Seeds generation. Experimental comparison with existing methods demonstrates that our AccDiffusion effectively addresses the issue of repeated object generation and leads to better performance in higher-resolution image generation. Our code is released at ..作者: 財(cái)主 時(shí)間: 2025-3-28 02:27
General Data: Dimensions, Age, etc.g details. Extensive experiments on public datasets, including LLFF, Shiny, mip-NeRF 360, and MVImgNet, show that CaesarNeRF delivers state-of-the-art performance across varying numbers of reference views, proving effective even with a single reference image.作者: Cougar 時(shí)間: 2025-3-28 10:09 作者: goodwill 時(shí)間: 2025-3-28 13:13 作者: sparse 時(shí)間: 2025-3-28 15:31 作者: JAUNT 時(shí)間: 2025-3-28 19:49
,FLAT: Flux-Aware Imperceptible Adversarial Attacks on?3D Point Clouds,ion vectors are adjusted to minimize these alterations, thereby improving imperceptibility. Extensive experiments validate the effectiveness of FLAT in generating imperceptible adversarial point clouds, and its superiority to the state-of-the-art methods.作者: PRISE 時(shí)間: 2025-3-29 01:47 作者: somnambulism 時(shí)間: 2025-3-29 07:08 作者: 四溢 時(shí)間: 2025-3-29 10:55 作者: left-ventricle 時(shí)間: 2025-3-29 11:33 作者: Coterminous 時(shí)間: 2025-3-29 17:49
,CaesarNeRF: Calibrated Semantic Representation for?Few-Shot Generalizable Neural Rendering,-level rendering. We introduce CaesarNeRF, an end-to-end approach that leverages scene-level .librat.d .em.ntic .epresentation along with pixel-level representations to advance few-shot, generalizable neural rendering, facilitating a holistic understanding without compromising high-quality details. 作者: fructose 時(shí)間: 2025-3-29 22:55
,MapTracker: Tracking with?Strided Memory Fusion for?Consistent Vector HD Mapping,tent reconstructions over time. Our method, ., accumulates a sensor stream into memory buffers of two latent representations: 1) Raster latents in the bird’s-eye-view (BEV) space and 2) Vector latents over the road elements (i.e., pedestrian-crossings, lane-dividers, and road-boundaries). The approa作者: dictator 時(shí)間: 2025-3-30 03:48 作者: 皮薩 時(shí)間: 2025-3-30 07:38 作者: 痛打 時(shí)間: 2025-3-30 08:37 作者: 配置 時(shí)間: 2025-3-30 15:34
,Learning Anomalies with?Normality Prior for?Unsupervised Video Anomaly Detection, are rare, diverse, and usually not well-defined. Existing UVAD methods are purely data-driven and perform unsupervised learning by identifying various abnormal patterns in videos. Since these methods largely rely on the feature representation and data distribution, they can only learn salient anoma作者: 不理會(huì) 時(shí)間: 2025-3-30 20:31 作者: 和藹 時(shí)間: 2025-3-30 20:42
,FLAT: Flux-Aware Imperceptible Adversarial Attacks on?3D Point Clouds,g a variety of geometric constraints, existing adversarial attack solutions often display unsatisfactory imperceptibility due to inadequate consideration of uniformity changes. In this paper, we propose FLAT, a novel framework designed to generate imperceptible adversarial point clouds by addressing作者: 套索 時(shí)間: 2025-3-31 02:58 作者: tackle 時(shí)間: 2025-3-31 07:05 作者: GRAZE 時(shí)間: 2025-3-31 10:28 作者: 評(píng)論性 時(shí)間: 2025-3-31 13:43
,Anytime Continual Learning for?Open Vocabulary Classification,rom batch training and rigid models by requiring that a system can predict any set of labels at any time and efficiently update and improve when receiving one or more training samples at any time. Despite the challenging goal, we achieve substantial improvements over recent methods. We propose a dyn作者: Fecundity 時(shí)間: 2025-3-31 20:53
,External Knowledge Enhanced 3D Scene Generation from?Sketch,d diffusion architecture (SEK) for generating customized, diverse, and plausible 3D scenes. SEK conditions the denoising process with a hand-drawn sketch of the target scene and cues from an object relationship knowledge base. We first construct an external knowledge base containing object relations作者: 緩解 時(shí)間: 2025-4-1 01:31 作者: 托運(yùn) 時(shí)間: 2025-4-1 02:40 作者: FOVEA 時(shí)間: 2025-4-1 09:53 作者: AXIS 時(shí)間: 2025-4-1 12:05 作者: 軟膏 時(shí)間: 2025-4-1 16:04
https://doi.org/10.1007/978-1-4419-6415-1tions. In this paper, we present an unsupervised approach for segmenting moving objects in videos downgraded by atmospheric turbulence. Our key approach is a detect-then-grow scheme: we first identify a small set of moving object pixels with high confidence, then gradually grow a foreground mask fro作者: bifurcate 時(shí)間: 2025-4-1 21:01
