Falter
发表于 2025-3-21 17:41:31
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ornithology
发表于 2025-3-21 22:43:01
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acquisition
发表于 2025-3-22 03:58:08
,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
结合
发表于 2025-3-22 06:39:59
,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
Myosin
发表于 2025-3-22 10:33:01
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
加花粗鄙人
发表于 2025-3-22 13:07:40
,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
加花粗鄙人
发表于 2025-3-22 17:28:50
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
Nomadic
发表于 2025-3-22 23:13:22
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磨碎
发表于 2025-3-23 02:44:08
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
朋党派系
发表于 2025-3-23 05:42:16
,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