Confidential
发表于 2025-3-30 10:16:52
https://doi.org/10.1007/978-3-030-71858-9k on this dataset comparing five state-of-the-art XFR algorithms on three facial matchers. This benchmark includes two new algorithms called subtree EBP and Density-based Input Sampling for Explanation (DISE) which outperform the state-of-the-art XFR by a wide margin.
achlorhydria
发表于 2025-3-30 13:56:52
Kari Alho,Markku Kotilainen,Mika Widgrénlations across two public datasets and show a significant performance improvement over previous state-of-the-art methods. Lastly, we offer new metrics incorporating admissibility criteria to further study and evaluate the diversity of predictions. Codes are at: ..
淘气
发表于 2025-3-30 19:48:33
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整洁
发表于 2025-3-30 22:49:02
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FOVEA
发表于 2025-3-31 03:06:42
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银版照相
发表于 2025-3-31 06:05:37
CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions,presentation that can well describe correspondences between instances in frame pairs. We adapt the discrete association process to a differentiable form, such that end-to-end training becomes feasible. Experiments are conducted in two aspects: We first compare our method with existing unsupervised r
植物学
发表于 2025-3-31 11:53:09
Towards Real-Time Multi-Object Tracking,ast association method that works in conjunction with the joint model. In both components the computation cost is significantly reduced compared with former MOT systems, resulting in a neat and fast baseline for future follow-ups on real-time MOT algorithm design. To our knowledge, this work reports
monochromatic
发表于 2025-3-31 14:49:47
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overture
发表于 2025-3-31 20:15:38
Unsupervised Deep Metric Learning with Transformed Attention Consistency and Contrastive Clusteringmilar images, even undergoing different image transforms, should share the same consistent visual attention map. This consistency leads to a pairwise self-supervision loss, allowing us to learn a Siamese deep neural network to encode and compare images against their transformed or matched pairs. To
CLOT
发表于 2025-3-31 22:51:02
STEm-Seg: Spatio-Temporal Embeddings for Instance Segmentation in Videos,is end, we introduce (i) novel mixing functions that enhance the feature representation of spatio-temporal embeddings, and (ii) a single-stage, proposal-free network that can reason about temporal context. Our network is trained end-to-end to learn spatio-temporal embeddings as well as parameters re