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Techniques and challenges of image segmentation: A review
Y Yu, C Wang, Q Fu, R Kou, F Huang, B Yang, T Yang… - Electronics, 2023 - mdpi.com
Image segmentation, which has become a research hotspot in the field of image processing
and computer vision, refers to the process of dividing an image into meaningful and non …
and computer vision, refers to the process of dividing an image into meaningful and non …
NTIRE 2024 image shadow removal challenge report
This work reviews the results of the NTIRE 2024 Challenge on Shadow Removal. Building
on the last year edition the current challenge was organized in two tracks with a track …
on the last year edition the current challenge was organized in two tracks with a track …
Segment anything
Abstract We introduce the Segment Anything (SA) project: a new task, model, and dataset for
image segmentation. Using our efficient model in a data collection loop, we built the largest …
image segmentation. Using our efficient model in a data collection loop, we built the largest …
Scannet++: A high-fidelity dataset of 3d indoor scenes
We present ScanNet++, a large-scale dataset that couples together capture of high-quality
and commodity-level geometry and color of indoor scenes. Each scene is captured with a …
and commodity-level geometry and color of indoor scenes. Each scene is captured with a …
Fast segment anything
The recently proposed segment anything model (SAM) has made a significant influence in
many computer vision tasks. It is becoming a foundation step for many high-level tasks, like …
many computer vision tasks. It is becoming a foundation step for many high-level tasks, like …
Towards open vocabulary learning: A survey
In the field of visual scene understanding, deep neural networks have made impressive
advancements in various core tasks like segmentation, tracking, and detection. However …
advancements in various core tasks like segmentation, tracking, and detection. However …
Mask3d: Mask transformer for 3d semantic instance segmentation
Modern 3D semantic instance segmentation approaches predominantly rely on specialized
voting mechanisms followed by carefully designed geometric clustering techniques. Building …
voting mechanisms followed by carefully designed geometric clustering techniques. Building …
Segclip: Patch aggregation with learnable centers for open-vocabulary semantic segmentation
Recently, the contrastive language-image pre-training, eg, CLIP, has demonstrated
promising results on various downstream tasks. The pre-trained model can capture enriched …
promising results on various downstream tasks. The pre-trained model can capture enriched …
Edter: Edge detection with transformer
Convolutional neural networks have made significant progresses in edge detection by
progressively exploring the context and semantic features. However, local details are …
progressively exploring the context and semantic features. However, local details are …
Region-based convolutional neural network for segmenting text in epigraphical images
Indian history is derived from ancient writings on the inscriptions, palm leaves, copper
plates, coins, and many more mediums. Epigraphers read these inscriptions and produce …
plates, coins, and many more mediums. Epigraphers read these inscriptions and produce …