Search Results for author:
Found 8 papers, 2 papers with code
CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classification
Using the aggregated similarity scores as the initial pseudo labels at the training stage, we propose an optimization framework to train the parameters of the classification network and refine pseudo labels for unobserved labels.
Leveraging Inpainting for Single-Image Shadow Removal
In this work, we find that pretraining shadow removal networks on the image inpainting dataset can reduce the shadow remnants significantly: a naive encoder-decoder network gets competitive restoration quality w. r. t.
An Effective Approach for Multi-label Classification with Missing Labels
Through comprehensive experiments on three large-scale multi-label image datasets, i. e. MS-COCO, NUS-WIDE, and Pascal VOC12, we show that our method can handle the imbalance between positive labels and negative labels, while still outperforming existing missing-label learning approaches in most cases, and in some cases even approaches with fully labeled datasets.
Depth Monocular Estimation with Attention-based Encoder-Decoder Network from Single Image
Depth information is the foundation of perception, essential for autonomous driving, robotics, and other source-constrained applications.
G2NetPL: Generic Game-Theoretic Network for Partial-Label Image Classification
To effectively address partial-label classification, this paper proposes an end-to-end Generic Game-theoretic Network (G2NetPL) for partial-label learning, which can be applied to most partial-label settings, including a very challenging, but annotation-efficient case where only a subset of the training images are labeled, each with only one positive label, while the rest of the training images remain unlabeled.
Multi-Label Classification
Multi-Label Image Classification
+2
PLMCL: Partial-Label Momentum Curriculum Learning for Multi-Label Image Classification
A special case is to annotate only one positive label in each training image.
Multi-Label Classification
Multi-Label Image Classification
+1
PLGAN: Generative Adversarial Networks for Power-Line Segmentation in Aerial Images
Accurate segmentation of power lines in various aerial images is very important for UAV flight safety.
TTPLA: An Aerial-Image Dataset for Detection and Segmentation of Transmission Towers and Power Lines
Accurate detection and segmentation of transmission towers~(TTs) and power lines~(PLs) from aerial images plays a key role in protecting power-grid security and low-altitude UAV safety.
