作者: arrhythmic 時(shí)間: 2025-3-21 22:52
,Exact Diffusion Inversion via?Bidirectional Integration Approximation,text inversion [.]. However, the above methods introduce considerable computational overhead. In this paper, we propose a new technique, . (BDIA), to perform exact diffusion inversion with negligible computational overhead. We consider a family of second order integration algorithms obtained by aver作者: 討好美人 時(shí)間: 2025-3-22 03:54
,Textual Query-Driven Mask Transformer for?Domain Generalized Segmentation,ext embeddings of vision-language models. We employ the text embeddings as object queries within a transformer-based segmentation framework (textual object queries). These queries are regarded as a domain-invariant basis for pixel grouping in DGSS. To leverage the power of textual object queries, we作者: 完成才能戰(zhàn)勝 時(shí)間: 2025-3-22 06:35 作者: Occlusion 時(shí)間: 2025-3-22 11:35 作者: 施加 時(shí)間: 2025-3-22 15:48
,Object-Centric Diffusion for?Efficient Video Editing,o inputs, following textual edit prompts. However, such solutions typically incur heavy memory and computational costs to generate temporally-coherent frames, either in the form of diffusion inversion and/or cross-frame attention. In this paper, we conduct an analysis of such inefficiencies, and sug作者: 施加 時(shí)間: 2025-3-22 17:02 作者: MOT 時(shí)間: 2025-3-22 22:12
,McGrids: Monte Carlo-Driven Adaptive Grids for?Iso-Surface Extraction,etric shapes with complicated geometric details, many existing algorithms suffer from high computational costs and memory usage. This paper proposes McGrids, a novel approach to improve the efficiency of iso-surface extraction. The key idea is to construct adaptive grids for iso-surface extraction r作者: Amenable 時(shí)間: 2025-3-23 01:59 作者: 相信 時(shí)間: 2025-3-23 07:36
,Adapt2Reward: Adapting Video-Language Models to?Generalizable Robotic Rewards via?Failure Prompts,einforcement learning and planning for such robotic agents is a generalizable reward function. Recent advances in vision-language models, such as CLIP, have?shown remarkable performance in the domain of deep learning, paving?the way for open-domain visual recognition. However, collecting data?on rob作者: palette 時(shí)間: 2025-3-23 12:56 作者: 橫條 時(shí)間: 2025-3-23 14:32
Agglomerative Token Clustering, across image classification,?image synthesis, and object detection & segmentation tasks. ATC merges clusters through bottom-up hierarchical clustering, without?the introduction of extra learnable parameters. We find that?ATC achieves state-of-the-art performance across all tasks, and can?even perfo作者: hereditary 時(shí)間: 2025-3-23 21:59 作者: Rebate 時(shí)間: 2025-3-24 00:06 作者: 人類學(xué)家 時(shí)間: 2025-3-24 04:48
,ClusteringSDF: Self-Organized Neural Implicit Surfaces for?3D Decomposition, machine-generated segments, integrating them to achieve 3D consistency. In this paper,?we propose ClusteringSDF, a novel approach achieving both segmentation?and reconstruction in 3D via the neural implicit surface representation, specifically the Signed Distance Function (SDF), where?the segmentat作者: Metamorphosis 時(shí)間: 2025-3-24 10:11
,NAMER: Non-autoregressive Modeling for?Handwritten Mathematical Expression Recognition, in document understanding. Current methods typically approach HMER as an image-to-sequence generation task within an autoregressive (AR) encoder-decoder framework. However, these approaches suffer from several drawbacks: 1) a lack of overall language context, limiting information utilization beyond作者: stress-response 時(shí)間: 2025-3-24 14:21 作者: AORTA 時(shí)間: 2025-3-24 18:20 作者: 我們的面粉 時(shí)間: 2025-3-24 22:35 作者: Alopecia-Areata 時(shí)間: 2025-3-25 00:34
0302-9743 reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation..978-3-031-72997-3978-3-031-72998-0Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Chivalrous 時(shí)間: 2025-3-25 06:56
https://doi.org/10.1007/978-3-322-82723-4uted from surface meshes and learned implicit fields from real multiview images. The experiment results show that our McGrids can significantly reduce the number of implicit field queries, resulting in significant memory reduction, while producing high-quality meshes with rich geometric details.作者: Graduated 時(shí)間: 2025-3-25 11:17
https://doi.org/10.1007/978-94-011-1946-7e core of ClusteringSDF, we introduce a highly efficient .?for lifting 2D labels to 3D. Experimental results on the challenging scenes from ScanNet and Replica datasets show that ClusteringSDF ?can achieve competitive performance compared to the state-of-the-art with significantly reduced training time.作者: emission 時(shí)間: 2025-3-25 12:57
Ortega Y Gasset, Phenomenology and Quixoted visually indicate them within images, outperforming strong baselines both on the binary alignment classification and the explanation generation tasks. Our code and human curated test set are available at: ..作者: 優(yōu)雅 時(shí)間: 2025-3-25 16:02
,McGrids: Monte Carlo-Driven Adaptive Grids for?Iso-Surface Extraction,uted from surface meshes and learned implicit fields from real multiview images. The experiment results show that our McGrids can significantly reduce the number of implicit field queries, resulting in significant memory reduction, while producing high-quality meshes with rich geometric details.作者: Limousine 時(shí)間: 2025-3-25 21:28
,ClusteringSDF: Self-Organized Neural Implicit Surfaces for?3D Decomposition,e core of ClusteringSDF, we introduce a highly efficient .?for lifting 2D labels to 3D. Experimental results on the challenging scenes from ScanNet and Replica datasets show that ClusteringSDF ?can achieve competitive performance compared to the state-of-the-art with significantly reduced training time.作者: Impugn 時(shí)間: 2025-3-26 00:22
,Mismatch Quest: Visual and?Textual Feedback for?Image-Text Misalignment,d visually indicate them within images, outperforming strong baselines both on the binary alignment classification and the explanation generation tasks. Our code and human curated test set are available at: ..作者: inventory 時(shí)間: 2025-3-26 04:20 作者: 相互影響 時(shí)間: 2025-3-26 12:23 作者: 糾纏,纏繞 時(shí)間: 2025-3-26 14:57 作者: Amendment 時(shí)間: 2025-3-26 16:48
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: 沉著 時(shí)間: 2025-3-26 21:34
Instructions to the Worker Bee,rm on par with prior state-of-the-art when applied ., . without fine-tuning. ATC is particularly effective when applied with low keep rates, where only a?small fraction of tokens are kept and retaining task performance?is especially difficult.作者: FLORA 時(shí)間: 2025-3-27 05:08
Epilogue: A New Default Future?,s to facilitate human-AI interaction at the video level. However, how to effectively encode and understand videos in video-based dialogue systems remains to be solved. In this paper, we investigate a straightforward yet unexplored question: Can we feed all spatial-temporal tokens into the LLM, thus 作者: 嚴(yán)峻考驗(yàn) 時(shí)間: 2025-3-27 09:14
https://doi.org/10.1007/978-1-349-07005-3text inversion [.]. However, the above methods introduce considerable computational overhead. In this paper, we propose a new technique, . (BDIA), to perform exact diffusion inversion with negligible computational overhead. We consider a family of second order integration algorithms obtained by aver作者: flutter 時(shí)間: 2025-3-27 09:56
https://doi.org/10.1007/978-1-349-07005-3ext embeddings of vision-language models. We employ the text embeddings as object queries within a transformer-based segmentation framework (textual object queries). These queries are regarded as a domain-invariant basis for pixel grouping in DGSS. To leverage the power of textual object queries, we作者: etidronate 時(shí)間: 2025-3-27 16:18
The Government-Stock Exchange Accord Despite significant progress in the field, prior methods still suffer from multi-view consistency and a lack of emotional expressiveness. To address these issues, we collect .dataset with calibrated multi-view videos, emotional annotations, and per-frame 3D geometry. By training on the .dataset, we作者: Feckless 時(shí)間: 2025-3-27 21:36 作者: disrupt 時(shí)間: 2025-3-27 22:50 作者: profligate 時(shí)間: 2025-3-28 03:56 作者: 無節(jié)奏 時(shí)間: 2025-3-28 08:55
https://doi.org/10.1007/978-3-322-82723-4etric shapes with complicated geometric details, many existing algorithms suffer from high computational costs and memory usage. This paper proposes McGrids, a novel approach to improve the efficiency of iso-surface extraction. The key idea is to construct adaptive grids for iso-surface extraction r作者: 傾聽 時(shí)間: 2025-3-28 14:01 作者: 紅腫 時(shí)間: 2025-3-28 14:46
https://doi.org/10.1007/978-3-476-04311-5einforcement learning and planning for such robotic agents is a generalizable reward function. Recent advances in vision-language models, such as CLIP, have?shown remarkable performance in the domain of deep learning, paving?the way for open-domain visual recognition. However, collecting data?on rob作者: Detonate 時(shí)間: 2025-3-28 21:10
https://doi.org/10.1007/978-3-476-04311-5urther correct these errors. In this paper, we investigate a multi-step iterative approach for the first time to tackle the challenging natural image matting task, and achieve excellent performance by introducing a pixel-level denoising diffusion method (DiffMatte) for the alpha matte refinement. To作者: 向下 時(shí)間: 2025-3-29 01:35
Instructions to the Worker Bee, across image classification,?image synthesis, and object detection & segmentation tasks. ATC merges clusters through bottom-up hierarchical clustering, without?the introduction of extra learnable parameters. We find that?ATC achieves state-of-the-art performance across all tasks, and can?even perfo作者: 嚴(yán)厲譴責(zé) 時(shí)間: 2025-3-29 03:44
Beautiful Lies and Beautiful Truths,ue to the rapid iteration of?3D sensors, which leads to significantly different distributions?in point clouds. This, in turn, results in subpar performance of?3D cross-sensor object detection. This paper introduces?a .ross .echanism .ataset,?named ., to support research tackling this challenge. CMD?作者: Endometrium 時(shí)間: 2025-3-29 07:25
Balzac’s Allegories of Energy in ,to-image diffusion model presents?the potential to resolve this task by employing synthetic image-caption pairs generated by this pre-trained prior. Nonetheless,?the defective details in the salient regions of the synthetic images introduce semantic misalignment between the synthetic image?and text,作者: Dorsal-Kyphosis 時(shí)間: 2025-3-29 13:58
https://doi.org/10.1007/978-94-011-1946-7 machine-generated segments, integrating them to achieve 3D consistency. In this paper,?we propose ClusteringSDF, a novel approach achieving both segmentation?and reconstruction in 3D via the neural implicit surface representation, specifically the Signed Distance Function (SDF), where?the segmentat作者: 和諧 時(shí)間: 2025-3-29 16:09 作者: curettage 時(shí)間: 2025-3-29 22:08
https://doi.org/10.1007/978-94-011-0898-0rom a finite vocabulary. To this end, we propose two surprisingly simple modifications to decoder-only transformers: 1) at the input, we replace the finite-vocabulary lookup table with a linear projection of the input vectors; and 2) at the output, we replace the logits prediction (usually mapped to作者: Catheter 時(shí)間: 2025-3-30 03:28 作者: 完整 時(shí)間: 2025-3-30 06:42 作者: Fibrinogen 時(shí)間: 2025-3-30 12:00
Computer Vision – ECCV 2024978-3-031-72998-0Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 染色體 時(shí)間: 2025-3-30 16:25 作者: Platelet 時(shí)間: 2025-3-30 16:48
978-3-031-72997-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 廣大 時(shí)間: 2025-3-30 21:34 作者: Peristalsis 時(shí)間: 2025-3-31 02:28 作者: contradict 時(shí)間: 2025-3-31 07:10
https://doi.org/10.1007/978-1-349-07005-3ase. It is demonstrated with experiments that BDIA-DDIM is effective for (round-trip) prompt-driven image editing. Our experiments further show that BDIA-DDIM produces markedly better image sampling quality than DDIM and EDICT for text-to-image generation and conventional image sampling. BDIA can al作者: 中止 時(shí)間: 2025-3-31 11:17 作者: 特征 時(shí)間: 2025-3-31 16:31 作者: 不要嚴(yán)酷 時(shí)間: 2025-3-31 19:11 作者: Blazon 時(shí)間: 2025-4-1 00:12
Die Information beherrschen und teilenregions and spending most on the former, and ii) Object-Centric Token Merging, which reduces cost of cross-frame attention by fusing redundant tokens in unimportant background regions. Both techniques are readily applicable to a given video editing model . retraining, and can drastically reduce its 作者: 沒有希望 時(shí)間: 2025-4-1 02:25 作者: hermitage 時(shí)間: 2025-4-1 08:50 作者: ONYM 時(shí)間: 2025-4-1 12:45
https://doi.org/10.1007/978-3-476-04311-5deos. To enhance the model’s ability to distinguish between successful and failed robot executions, we cluster failure video features to enable the model to identify patterns within.?For each cluster, we integrate a newly trained failure prompt into?the text encoder to represent the corresponding fa作者: 縮影 時(shí)間: 2025-4-1 16:29
https://doi.org/10.1007/978-3-476-04311-5used in training and inference, mitigating performance degradation caused by sampling drift. Extensive experimental results demonstrate that DiffMatte not only reaches the state-of-the-art level on the mainstream Composition-1k test set, surpassing the previous best methods by . and . in the SAD met作者: 抗體 時(shí)間: 2025-4-1 19:24
Beautiful Lies and Beautiful Truths, various domain adaptation methods in mitigating sensor-based domain differences. We also proposed?a . method to reduce domain disparities from?the perspectives of .ensity, .ntensity,?and .eometry, which effectively bridges the domain gap between different sensors. The experimental results on the CM作者: harangue 時(shí)間: 2025-4-2 01:49
Balzac’s Allegories of Energy in ,space. Next, the patch-wise visual features of the input image are selectively fused with the textual features of the salient visual concepts, leading to a mixed-up feature map with less defective content. Finally, a visual-semantic encoder is exploited to refine the derived feature map, which?is fu作者: 有角 時(shí)間: 2025-4-2 03:54
Balzac’s Allegories of Energy in ,ns and establishes connectivities in parallel, leveraging comprehensive visual and linguistic contexts. Experiments on CROHME 2014/2016/2019 and HME100K datasets demonstrate that NAMER not only outperforms the current state-of-the-art (SOTA) methods on ExpRate by 1.93%/2.35%/1.49%/0.62%, but also ac作者: 免除責(zé)任 時(shí)間: 2025-4-2 08:00
https://doi.org/10.1007/978-94-011-0898-0ce competitive with recent latent diffusion models. Finally, we obtain strong results outside of image generation when applying GIVT to panoptic segmentation and depth estimation with a VAE variant of the UViM framework.
