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Found 6 papers, 3 papers with code
Camera-Driven Representation Learning for Unsupervised Domain Adaptive Person Re-identification
To address the camera bias problem, we also introduce a camera-diversity (CD) loss encouraging person images of the same pseudo label, but captured across various cameras, to involve more for discriminative feature learning, providing person representations robust to inter-camera variations.
Domain Adaptation
Domain Adaptive Person Re-Identification
+4
Newton Raphson Emulation Network for Highly Efficient Computation of Numerous Implied Volatilities
In finance, implied volatility is an important indicator that reflects the market situation immediately.
Set2Box: Similarity Preserving Representation Learning of Sets
Through extensive experiments on 8 real-world datasets, we show that, compared to baseline approaches, Set2Box+ is (a) Accurate: achieving up to 40. 8X smaller estimation error while requiring 60% fewer bits to encode sets, (b) Concise: yielding up to 96. 8X more concise representations with similar estimation error, and (c) Versatile: enabling the estimation of four set-similarity measures from a single representation of each set.
Bi-directional Contrastive Learning for Domain Adaptive Semantic Segmentation
We present a novel unsupervised domain adaptation method for semantic segmentation that generalizes a model trained with source images and corresponding ground-truth labels to a target domain.
Video-based Person Re-identification with Spatial and Temporal Memory Networks
Spatial and temporal distractors in person videos, such as background clutter and partial occlusions over frames, respectively, make this task much more challenging than image-based person reID.
Ranked #8 on
Person Re-Identification
on MARS
Hypergraph Motifs: Concepts, Algorithms, and Discoveries
(Q3) how can we identify domains which hypergraphs are from?
Social and Information Networks Databases Data Structures and Algorithms H.2.8
