Optimization strategies of fruit detection to overcome the challenge of unstructured background in field orchard environment: A review
Y Tang, J Qiu, Y Zhang, D Wu, Y Cao, K Zhao… - Precision Agriculture, 2023 - Springer
The demand for intelligent agriculture is increasing due to the continuous impact of world
food and environmental crises. Focusing on fruit detection, with the rapid development of …
food and environmental crises. Focusing on fruit detection, with the rapid development of …
Image inpainting based on deep learning: A review
Z Qin, Q Zeng, Y Zong, F Xu - Displays, 2021 - Elsevier
Image inpainting aims to restore the pixel features of damaged parts in incomplete image
and plays a key role in many computer vision tasks. Image inpainting technology based on …
and plays a key role in many computer vision tasks. Image inpainting technology based on …
Multimodal foundation models: From specialists to general-purpose assistants
Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …
methods to data compression. Recent advances in statistical machine learning have opened …
Incremental transformer structure enhanced image inpainting with masking positional encoding
Image inpainting has made significant advances in recent years. However, it is still
challenging to recover corrupted images with both vivid textures and reasonable structures …
challenging to recover corrupted images with both vivid textures and reasonable structures …
Deep learning for image inpainting: A survey
Image inpainting has been widely exploited in the field of computer vision and image
processing. The main purpose of image inpainting is to produce visually plausible structure …
processing. The main purpose of image inpainting is to produce visually plausible structure …
Weakly supervised contrastive learning
Unsupervised visual representation learning has gained much attention from the computer
vision community because of the recent achievement of contrastive learning. Most of the …
vision community because of the recent achievement of contrastive learning. Most of the …
Inpaint anything: Segment anything meets image inpainting
Modern image inpainting systems, despite the significant progress, often struggle with mask
selection and holes filling. Based on Segment-Anything Model (SAM), we make the first …
selection and holes filling. Based on Segment-Anything Model (SAM), we make the first …
A review of lane detection methods based on deep learning
J Tang, S Li, P Liu - Pattern Recognition, 2021 - Elsevier
Lane detection is an application of environmental perception, which aims to detect lane
areas or lane lines by camera or lidar. In recent years, gratifying progress has been made in …
areas or lane lines by camera or lidar. In recent years, gratifying progress has been made in …
Image inpainting with local and global refinement
Image inpainting has made remarkable progress with recent advances in deep learning.
Popular networks mainly follow an encoder-decoder architecture (sometimes with skip …
Popular networks mainly follow an encoder-decoder architecture (sometimes with skip …
Ressl: Relational self-supervised learning with weak augmentation
Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved
great success in learning visual representations without data annotations. However, most of …
great success in learning visual representations without data annotations. However, most of …