作者: Lipohypertrophy 時(shí)間: 2025-3-21 23:21 作者: incision 時(shí)間: 2025-3-22 03:02 作者: notice 時(shí)間: 2025-3-22 07:01 作者: Pathogen 時(shí)間: 2025-3-22 11:09
Skyline-Based Temporal Graph Explorationkes it an extremely challenging task. Most previous best-performing methods adopt prototypical learning or affinity learning. Nevertheless, they either neglect to further utilize support pixels for facilitating segmentation and lose spatial information, or are not robust to noisy pixels and computat作者: 追蹤 時(shí)間: 2025-3-22 16:25 作者: 追蹤 時(shí)間: 2025-3-22 20:51
https://doi.org/10.1007/978-981-16-2502-2sting methods are bottom-up approaches that try to group pixels into regions based on their visual cues or certain predefined rules. As a result, it is difficult for these bottom-up approaches to generate fine-grained semantic segmentation when coming to complicated scenes with multiple objects and 作者: Introvert 時(shí)間: 2025-3-22 23:48
V.S. Koroliuk,N. Limnios,I.V. Samoilenkoots performing daily tasks in human environments..Besides parsing the articulated parts and joint parameters, researchers recently advocate learning manipulation affordance over the input shape geometry which is more task-aware and geometrically fine-grained..However, taking only passive observation作者: AGONY 時(shí)間: 2025-3-23 02:36
Degradation and Fuzzy Informationnsformers can benefit correlation map aggregation through self-attention over a global receptive field. However, the tokenization of a correlation map for transformer processing can be detrimental, because the discontinuity at token boundaries reduces the local context available near the token edges作者: GREG 時(shí)間: 2025-3-23 06:00
V.S. Koroliuk,N. Limnios,I.V. Samoilenkof understanding a viewer’s behaviors and intentions. We provide a labeled dataset consisting of 11,243 egocentric images with per-pixel segmentation labels of hands and objects being interacted with during a diverse array of daily activities. Our dataset is the first to label detailed hand-object co作者: 舔食 時(shí)間: 2025-3-23 10:23 作者: precede 時(shí)間: 2025-3-23 15:35
Gunnar Sohlenius,Leif Clausson,Ann Kjellberg methods learn and predict the complete silhouettes of target instances in 2D space. However, masks in 2D space are only some observations and samples from the 3D model in different viewpoints and thus can not represent the real complete physical shape of the instances. With the 2D masks learned, 2D作者: farewell 時(shí)間: 2025-3-23 18:53
Use of Constraint Programming for Designthe 2D images counterpart. In this work, we deal with the data scarcity challenge of 3D tasks by transferring knowledge from strong 2D models via RGB-D images. Specifically, we utilize a strong and well-trained semantic segmentation model for 2D images to augment RGB-D images with pseudo-label. The 作者: regale 時(shí)間: 2025-3-24 00:31 作者: nerve-sparing 時(shí)間: 2025-3-24 04:46 作者: 自戀 時(shí)間: 2025-3-24 09:53
L. Asión-Su?er,I. López-Forniésand shape information of 3D instances. We show that instance kernels enable easy mask inference by simply scanning kernels over the entire scenes, avoiding the heavy reliance on proposals or heuristic clustering algorithms in standard 3D instance segmentation pipelines. The idea of instance kernel i作者: 桉樹 時(shí)間: 2025-3-24 11:15
L. Asión-Su?er,I. López-Forniésalues from known to unknown regions. However, not all natural images have a specifically known foreground. Images of transparent objects, like glass, smoke, web, etc., have less or no known foreground. In this paper, we propose a Transformer-based network, TransMatting, to model transparent objects 作者: exophthalmos 時(shí)間: 2025-3-24 15:28 作者: ANIM 時(shí)間: 2025-3-24 19:04
Advances in Design Engineering IIgnition (.., object detection and panoptic segmentation). Originated from Natural Language Processing (NLP), transformer architectures, consisting of self-attention and cross-attention, effectively learn long-range interactions between elements in a sequence. However, we observe that most existing t作者: intolerance 時(shí)間: 2025-3-25 01:04 作者: inscribe 時(shí)間: 2025-3-25 06:02 作者: fastness 時(shí)間: 2025-3-25 10:12
https://doi.org/10.1007/978-3-031-19818-2artificial intelligence; computer systems; computer vision; data security; databases; education; face reco作者: Enteropathic 時(shí)間: 2025-3-25 13:14 作者: ordain 時(shí)間: 2025-3-25 16:23 作者: 裁決 時(shí)間: 2025-3-25 21:52
Conference proceedings 2022ning; 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; object recognition; motion estimation..作者: 滴注 時(shí)間: 2025-3-26 01:27 作者: Adrenal-Glands 時(shí)間: 2025-3-26 04:40 作者: Neutral-Spine 時(shí)間: 2025-3-26 09:15 作者: 令人發(fā)膩 時(shí)間: 2025-3-26 13:57
V.S. Koroliuk,N. Limnios,I.V. SamoilenkodaAfford, that learns to perform very few test-time interactions for quickly adapting the affordance priors to more accurate instance-specific posteriors. We conduct large-scale experiments using the PartNet-Mobility dataset and prove that our system performs better than baselines. We will release our code and data upon paper acceptance.作者: 圓桶 時(shí)間: 2025-3-26 20:21 作者: ungainly 時(shí)間: 2025-3-27 00:56
Use of Constraint Programming for Designproved by our pre-training, suggesting that the transferred knowledge is helpful in semi-supervised setting. We verify the effectiveness of our approach on two popular 3D models and three different tasks. On ScanNet official evaluation, we establish new state-of-the-art semantic segmentation results on the data-efficient track.作者: 誹謗 時(shí)間: 2025-3-27 03:04
L. Asión-Su?er,I. López-Forniése propagation from encoder to decoder for maintaining the contexture of transparent objects. In addition, we create a high-resolution matting dataset of transparent objects with small known foreground areas. Experiments on several matting benchmarks demonstrate the superiority of our proposed method over the current state-of-the-art methods.作者: gastritis 時(shí)間: 2025-3-27 05:30 作者: 最初 時(shí)間: 2025-3-27 10:11 作者: Interdict 時(shí)間: 2025-3-27 16:32 作者: 雪崩 時(shí)間: 2025-3-27 19:05
,Data Efficient 3D Learner via?Knowledge Transferred from?2D Model,proved by our pre-training, suggesting that the transferred knowledge is helpful in semi-supervised setting. We verify the effectiveness of our approach on two popular 3D models and three different tasks. On ScanNet official evaluation, we establish new state-of-the-art semantic segmentation results on the data-efficient track.作者: Gobble 時(shí)間: 2025-3-28 00:07
,TransMatting: Enhancing Transparent Objects Matting with?Transformers,e propagation from encoder to decoder for maintaining the contexture of transparent objects. In addition, we create a high-resolution matting dataset of transparent objects with small known foreground areas. Experiments on several matting benchmarks demonstrate the superiority of our proposed method over the current state-of-the-art methods.作者: WAX 時(shí)間: 2025-3-28 02:43
,MVSalNet: Multi-view Augmentation for?RGB-D Salient Object Detection,s multi-view outputs through a fusion model to produce final saliency prediction. A dynamic filtering module is also designed to facilitate more effective and flexible feature extraction. Extensive experiments on 6 widely used datasets demonstrate that our approach compares favorably against state-of-the-art approaches.作者: syring 時(shí)間: 2025-3-28 08:11 作者: flaunt 時(shí)間: 2025-3-28 14:10
Skyline-Based Temporal Graph Exploration function, the level set for each instance is iteratively optimized within its corresponding bounding box annotation. The experimental results on four challenging benchmarks demonstrate the leading performance of our proposed approach to robust instance segmentation in various scenarios. The code is作者: AUGUR 時(shí)間: 2025-3-28 17:05
Skyline-Based Temporal Graph Exploration to make agent tokens capable of dividing different objects into diverse parts in an adaptive manner, we customize the agent learning decoder according to the three characteristics of context awareness, spatial awareness and diversity. Second, the proposed agent matching decoder is responsible for d作者: 葡萄糖 時(shí)間: 2025-3-28 21:27 作者: 嚴(yán)重傷害 時(shí)間: 2025-3-29 00:35 作者: 小溪 時(shí)間: 2025-3-29 04:22 作者: Carminative 時(shí)間: 2025-3-29 07:35
Hoda A. ElMaraghy,Waguih H. ElMaraghyeliably localize inpainting artifacts within inpainted images. Second, we propose a new interpretable evaluation metric called Perceptual Artifact Ratio (PAR), which is the ratio of objectionable inpainted regions to the entire inpainted area. PAR demonstrates a strong correlation with real user pre作者: 泛濫 時(shí)間: 2025-3-29 11:50
Gunnar Sohlenius,Leif Clausson,Ann KjellbergD shape prior for 2D AIS. To deal with the diversity of 3D shapes, our method is pretrained on large 3D reconstruction datasets for high-quality results. And we adopt the unsupervised 3D reconstruction method to avoid relying on 3D annotations. In this approach, our method can reconstruct 3D models 作者: insert 時(shí)間: 2025-3-29 17:32 作者: pester 時(shí)間: 2025-3-29 23:05
Jorge Sierra-Pérez,Ignacio López-Forniéshe GP. Our approach sets a new state-of-the-art on the PASCAL-5. and COCO-20. benchmarks, achieving an absolute gain of . mIoU in the COCO-20. 5-shot setting. Furthermore, the segmentation quality of our approach scales gracefully when increasing the support set size, while achieving robust cross-da作者: 抗體 時(shí)間: 2025-3-30 00:24
L. Asión-Su?er,I. López-Forniésscheme is devised to simultaneously aggregate duplicated candidates and collect context around the merged centroids to form the instance kernels. Once instance kernels are available, instance masks can be reconstructed via dynamic convolutions whose weights are conditioned on instance kernels. The w作者: 冰雹 時(shí)間: 2025-3-30 07:33 作者: 上流社會(huì) 時(shí)間: 2025-3-30 11:07 作者: 開始發(fā)作 時(shí)間: 2025-3-30 16:13
https://doi.org/10.1007/978-3-031-20325-1they usually suffer from the over-expansion due to an absence of guidelines on when to stop erasing. We experimentally verify that the over-expansion is due to rigid classification, and metric learning can be a flexible remedy for it. AEFT is devised to learn the concept of erasing with the triplet 作者: GOUGE 時(shí)間: 2025-3-30 16:56 作者: 補(bǔ)角 時(shí)間: 2025-3-31 00:03 作者: BARGE 時(shí)間: 2025-3-31 01:27 作者: organic-matrix 時(shí)間: 2025-3-31 08:22 作者: coalition 時(shí)間: 2025-3-31 11:05 作者: 和平 時(shí)間: 2025-3-31 13:21 作者: 松緊帶 時(shí)間: 2025-3-31 20:17 作者: Essential 時(shí)間: 2025-4-1 00:09
,Adaptive Spatial-BCE Loss for?Weakly Supervised Semantic Segmentation,strategy to adaptively generate thresholds to divide the foreground and background. Benefiting from high-quality initial pseudo-labels by Spatial-BCE Loss, our method also reduce the reliance on post-processing, thereby simplifying the pipeline of WSSS. Our method is validated on the PASCAL VOC 2012作者: biopsy 時(shí)間: 2025-4-1 05:39 作者: Blood-Vessels 時(shí)間: 2025-4-1 09:08 作者: Inexorable 時(shí)間: 2025-4-1 10:14
-means Mask Transformer, as a clustering process. Inspired by the traditional .-means clustering algorithm, we develop a .-means .sk .former (.MaX-DeepLab) for segmentation tasks, which not only improves the state-of-the-art, but also enjoys a simple and elegant design. As a result, our .MaX-DeepLab achieves a new state-of作者: 杠桿支點(diǎn) 時(shí)間: 2025-4-1 16:22
,SegPGD: An Effective and?Efficient Adversarial Attack for?Evaluating and?Boosting Segmentation Robuk, we propose an effective and efficient segmentation attack method, dubbed SegPGD. Besides, we provide a convergence analysis to show the proposed SegPGD can create more effective adversarial examples than PGD under the same number of attack iterations. Furthermore, we propose to apply our SegPGD a作者: 停止償付 時(shí)間: 2025-4-1 21:45
,Adversarial Erasing Framework via?Triplet with?Gated Pyramid Pooling Layer for?Weakly Supervised Sethey usually suffer from the over-expansion due to an absence of guidelines on when to stop erasing. We experimentally verify that the over-expansion is due to rigid classification, and metric learning can be a flexible remedy for it. AEFT is devised to learn the concept of erasing with the triplet
