作者: ornithology 時間: 2025-3-21 22:43 作者: acquisition 時間: 2025-3-22 03:58
,Bi-directional Contrastive Learning for?Domain Adaptive Semantic Segmentation,ng ground-truth labels to a target domain. A key to domain adaptive semantic segmentation is to learn domain-invariant and discriminative features without target ground-truth labels. To this end, we propose a bi-directional pixel-prototype contrastive learning framework that minimizes intra-class va作者: 結(jié)合 時間: 2025-3-22 06:39
,Learning Regional Purity for?Instance Segmentation on?3D Point Clouds,ds have been proposed recently for this task, with remarkable results and high efficiency. However, these methods heavily rely on instance centroid regression and do not explicitly detect object boundaries, thus may mistakenly group nearby objects into the same clusters in some scenarios. In this pa作者: Myosin 時間: 2025-3-22 10:33
Cross-Domain Few-Shot Semantic Segmentation,etting where base classes are sampled from the same domain as the novel classes. However, in many applications, collecting sufficient training data for meta-learning is infeasible or impossible. In this paper, we extend few-shot semantic segmentation to a new task, called Cross-Domain Few-Shot Seman作者: 加花粗鄙人 時間: 2025-3-22 13:07
,Generative Subgraph Contrast for?Self-Supervised Graph Representation Learning,raph contrastive learning methods rely on the vector inner product based similarity metric to distinguish the samples for graph representation. However, the handcrafted sample construction (e.g., the perturbation on the nodes or edges of the graph) may not effectively capture the intrinsic local str作者: 加花粗鄙人 時間: 2025-3-22 17:28
SdAE: Self-distillated Masked Autoencoder,dom patches of the input image and reconstructing the missing information has grown in concern. However, BeiT and PeCo need a “pre-pretraining” stage to produce discrete codebooks for masked patches representing. MAE does not require a pre-training codebook process, but setting pixels as reconstruct作者: Nomadic 時間: 2025-3-22 23:13 作者: 磨碎 時間: 2025-3-23 02:44
Open-Set Semi-Supervised Object Detection,ever, thus far these methods have assumed that the unlabeled data does not contain out-of-distribution (OOD) classes, which is unrealistic with larger-scale unlabeled datasets. In this paper, we consider a more practical yet challenging problem, Open-Set Semi-Supervised Object Detection (OSSOD). We 作者: 朋黨派系 時間: 2025-3-23 05:42
,Vibration-Based Uncertainty Estimation for?Learning from?Limited Supervision,ited supervision. However, both prediction probability and entropy estimate uncertainty from the instantaneous information. In this paper, we present a novel approach that measures uncertainty from the vibration of sequential data, ., the output probability during the training procedure. The key obs作者: 珠寶 時間: 2025-3-23 13:38 作者: glacial 時間: 2025-3-23 16:45
,Weakly Supervised Object Localization Through Inter-class Feature Similarity and?Intra-class Appearused features for WSOL. However, existing CAM-based methods tend to excessively pursue discriminative features for object recognition and hence ignore the feature similarities among different categories, thereby leading to CAMs incomplete for object localization. In addition, CAMs are sensitive to b作者: 倒轉(zhuǎn) 時間: 2025-3-23 18:56
,Active Learning Strategies for?Weakly-Supervised Object Detection,formance gap between them. We propose to narrow this gap by fine-tuning a base pre-trained weakly-supervised detector with a few fully-annotated samples automatically selected from the training set using “box-in-box” (BiB), a novel active learning strategy designed specifically to address the well-d作者: macabre 時間: 2025-3-23 23:44 作者: 獨裁政府 時間: 2025-3-24 04:35 作者: Aerophagia 時間: 2025-3-24 08:09
,Unsupervised Visual Representation Learning by?Synchronous Momentum Grouping,asses the vanilla supervised learning. Two mainstream unsupervised learning schemes are the instance-level contrastive framework and clustering-based schemes. The former adopts the extremely fine-grained instance-level discrimination whose supervisory signal is not efficient due to the false negativ作者: 使服水土 時間: 2025-3-24 12:09
Improving Few-Shot Part Segmentation Using Coarse Supervision,oit coarse labels such as figure-ground masks and keypoint locations that are readily available for some categories to improve part segmentation models. A key challenge is that these annotations were collected for different tasks and with different labeling styles and cannot be readily mapped to the作者: 話 時間: 2025-3-24 17:00
,What to?Hide from?Your Students: Attention-Guided Masked Image Modeling,e token masking differs from token masking in text, due to the amount and correlation of tokens in an image. In particular, to generate a challenging pretext task for MIM, we advocate a shift from random masking to informed masking. We develop and exhibit this idea in the context of distillation-bas作者: 昏迷狀態(tài) 時間: 2025-3-24 22:36
Pointly-Supervised Panoptic Segmentation,evel labels used by fully supervised methods, point-level labels only provide a single point for each target as supervision, significantly reducing the annotation burden. We formulate the problem in an end-to-end framework by simultaneously generating panoptic pseudo-masks from point-level labels an作者: Microaneurysm 時間: 2025-3-25 02:30 作者: integral 時間: 2025-3-25 03:23 作者: EXPEL 時間: 2025-3-25 10:15
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: 埋伏 時間: 2025-3-25 13:27
https://doi.org/10.1007/978-1-349-15632-0 task. Finally, we introduce a new unsupervised correspondence approach which utilizes the strength of pre-trained features while encouraging better matches during training. This results in significantly better matching performance compared to current state-of-the-art methods.作者: Original 時間: 2025-3-25 19:09
Consumption at Low Income Levels,e supervision from diverse labels. To evaluate our approach we develop a benchmark on the Caltech-UCSD birds and OID Aircraft dataset. Our approach outperforms baselines based on multi-task learning, semi-supervised learning, and competitive methods relying on loss functions manually designed to exploit coarse supervision.作者: FLIC 時間: 2025-3-25 20:01
Demystifying Unsupervised Semantic Correspondence Estimation, task. Finally, we introduce a new unsupervised correspondence approach which utilizes the strength of pre-trained features while encouraging better matches during training. This results in significantly better matching performance compared to current state-of-the-art methods.作者: 性學院 時間: 2025-3-26 00:43 作者: asthma 時間: 2025-3-26 05:35 作者: Anterior 時間: 2025-3-26 10:56
Medical and Dental Expense Benefitsng to a given object lie in the same quadratic surface. The proposed method is based on a characterization of each surface in terms of the Christoffel polynomial associated with the probability that a given point belongs to the surface. This allows for efficiently segmenting points “one surface at a time” in ..作者: CLAM 時間: 2025-3-26 12:46
https://doi.org/10.1007/978-94-015-7813-4ng a powerful tool that can be used for semi-supervised, active learning, and one-bit supervision. Experiments on the CIFAR10, CIFAR100, mini-ImageNet and ImageNet datasets validate the effectiveness of our approach.作者: 議程 時間: 2025-3-26 19:08
https://doi.org/10.1007/978-3-8350-5429-5on-based self-supervised learning on classification tokens. We confirm that AttMask accelerates the learning process and improves the performance on a variety of downstream tasks. We provide the implementation code at ..作者: Overstate 時間: 2025-3-26 22:25
The Economics of Superstars and Celebritiesptic targets. We conduct experiments on the Pascal VOC and the MS COCO datasets to demonstrate the approach’s effectiveness and show state-of-the-art performance in the weakly-supervised panoptic segmentation problem. Codes are available at ..作者: 時代錯誤 時間: 2025-3-27 02:03
Fast Two-View Motion Segmentation Using Christoffel Polynomials,ng to a given object lie in the same quadratic surface. The proposed method is based on a characterization of each surface in terms of the Christoffel polynomial associated with the probability that a given point belongs to the surface. This allows for efficiently segmenting points “one surface at a time” in ..作者: BLAND 時間: 2025-3-27 06:58
,Vibration-Based Uncertainty Estimation for?Learning from?Limited Supervision,ng a powerful tool that can be used for semi-supervised, active learning, and one-bit supervision. Experiments on the CIFAR10, CIFAR100, mini-ImageNet and ImageNet datasets validate the effectiveness of our approach.作者: 千篇一律 時間: 2025-3-27 09:25 作者: 鐵塔等 時間: 2025-3-27 13:57 作者: BULLY 時間: 2025-3-27 18:27
https://doi.org/10.1007/978-3-031-20056-4artificial intelligence; clustering algorithms; computer systems; computer vision; data mining; image ana作者: Custodian 時間: 2025-3-28 00:41 作者: 思考才皺眉 時間: 2025-3-28 04:18 作者: 別名 時間: 2025-3-28 09:17
Medical and Dental Expense Benefitsll understood problem, existing methods scale poorly with the number of correspondences. In this paper we propose a fast segmentation algorithm that scales linearly with the number of correspondences and show that on benchmark datasets it offers the best trade-off between error and computational tim作者: 難管 時間: 2025-3-28 12:27 作者: 傲慢物 時間: 2025-3-28 17:38 作者: crumble 時間: 2025-3-28 19:44 作者: 制定 時間: 2025-3-29 02:23
The Economics of Social Problemsetting where base classes are sampled from the same domain as the novel classes. However, in many applications, collecting sufficient training data for meta-learning is infeasible or impossible. In this paper, we extend few-shot semantic segmentation to a new task, called Cross-Domain Few-Shot Seman作者: PALMY 時間: 2025-3-29 03:31
The Economics of Social Problemsraph contrastive learning methods rely on the vector inner product based similarity metric to distinguish the samples for graph representation. However, the handcrafted sample construction (e.g., the perturbation on the nodes or edges of the graph) may not effectively capture the intrinsic local str作者: 出生 時間: 2025-3-29 08:47
https://doi.org/10.1007/978-1-349-15632-0dom patches of the input image and reconstructing the missing information has grown in concern. However, BeiT and PeCo need a “pre-pretraining” stage to produce discrete codebooks for masked patches representing. MAE does not require a pre-training codebook process, but setting pixels as reconstruct作者: 使無效 時間: 2025-3-29 14:55
https://doi.org/10.1007/978-1-349-15632-0 methods across multiple challenging datasets using a standardized evaluation protocol where we vary factors such as the backbone architecture, the pre-training strategy, and the pre-training and finetuning datasets. To better understand the failure modes of these methods, and in order to provide a 作者: 歡騰 時間: 2025-3-29 17:41
Rent Seeking: The Problem of Definitionever, thus far these methods have assumed that the unlabeled data does not contain out-of-distribution (OOD) classes, which is unrealistic with larger-scale unlabeled datasets. In this paper, we consider a more practical yet challenging problem, Open-Set Semi-Supervised Object Detection (OSSOD). We 作者: Cubicle 時間: 2025-3-29 23:01
https://doi.org/10.1007/978-94-015-7813-4ited supervision. However, both prediction probability and entropy estimate uncertainty from the instantaneous information. In this paper, we present a novel approach that measures uncertainty from the vibration of sequential data, ., the output probability during the training procedure. The key obs作者: 落葉劑 時間: 2025-3-30 03:31
Rent Seeking: The Problem of Definitionthat pretext tasks could be used to mitigate this domain shift by learning domain invariant representations. However, in practice, we find that most existing pretext tasks are ineffective against other established techniques. Thus, we theoretically analyze how and when a subsidiary pretext task coul作者: 使糾纏 時間: 2025-3-30 06:07 作者: 名字的誤用 時間: 2025-3-30 11:36
Lecture Notes in Production Engineeringformance gap between them. We propose to narrow this gap by fine-tuning a base pre-trained weakly-supervised detector with a few fully-annotated samples automatically selected from the training set using “box-in-box” (BiB), a novel active learning strategy designed specifically to address the well-d作者: cortex 時間: 2025-3-30 15:51 作者: FEAS 時間: 2025-3-30 17:06 作者: Monotonous 時間: 2025-3-30 21:16
Consumption at Low Income Levels,asses the vanilla supervised learning. Two mainstream unsupervised learning schemes are the instance-level contrastive framework and clustering-based schemes. The former adopts the extremely fine-grained instance-level discrimination whose supervisory signal is not efficient due to the false negativ作者: HUSH 時間: 2025-3-31 04:46 作者: 閑聊 時間: 2025-3-31 05:09
https://doi.org/10.1007/978-3-8350-5429-5e token masking differs from token masking in text, due to the amount and correlation of tokens in an image. In particular, to generate a challenging pretext task for MIM, we advocate a shift from random masking to informed masking. We develop and exhibit this idea in the context of distillation-bas作者: Finasteride 時間: 2025-3-31 09:43
The Economics of Superstars and Celebritiesevel labels used by fully supervised methods, point-level labels only provide a single point for each target as supervision, significantly reducing the annotation burden. We formulate the problem in an end-to-end framework by simultaneously generating panoptic pseudo-masks from point-level labels an作者: Nerve-Block 時間: 2025-3-31 16:32 作者: Badger 時間: 2025-3-31 21:35
Retirement and Profit Sharing Plansmodule based on Cross-Attention that can perform adaptive and asymmetric information exchange between the RGB and depth encoder. Our proposed framework, namely UCTNet, is an encoder-decoder network that naturally incorporates these two key designs for robust and accurate RGB-D Segmentation. Experime作者: configuration 時間: 2025-3-31 23:12
The Economics of Social Problemsl features in the source image and a prototype in the target image. The cross-domain matching encourages domain-invariant feature representations, while the bidirectional pixel-prototype correspondences aggregate features for the same object class, providing discriminative features. To establish tra
