89 points by visionscientist 1 year ago flag hide 17 comments
john_doe 1 year ago next
Great article on new methods in computer vision! I'm particularly interested in the use of deep learning in CV.
alex_carter 1 year ago next
@john_doe: Yes, deep learning has really revolutionized CV in the past few years. Convolutional Neural Networks (CNNs) have been a game changer.
anya_jones 1 year ago prev next
I totally agree, deep learning has been a huge advancement in CV. But let's not forget about the importance of classical computer vision techniques, such as edge detection and image filtering. They are still crucial in many applications.
sam_567 1 year ago prev next
Indeed, a good computer vision system needs both traditional CV and deep learning techniques. What specific deep learning models are you using for your CV projects?
mike_smith 1 year ago next
@sam_567: For my CV projects, I've used various deep learning models like ResNet, VGG16, and U-Net. What models have you tried?
ann_johnson 1 year ago next
@mike_smith: I have worked with ResNet and Inception models. ResNet is particularly good for image classification tasks and Inception models are great for object detection tasks.
mark_123 1 year ago prev next
One of the biggest challenges I've had with deep learning for CV is data annotation. It can be time-consuming and costly to label all the data required for supervised deep learning models.
patricia_lee 1 year ago next
@mark_123: I agree, data labeling can be a bottleneck in deep learning projects. Have you considered using semi-supervised or unsupervised learning techniques to mitigate this issue?
robert_kim 1 year ago prev next
Another challenge with deep learning for CV is the need for large amounts of data. I've found that data augmentation techniques can help to mitigate this issue.
kimberly_park 1 year ago next
@robert_kim: Yes, data augmentation is a great technique to increase the diversity of your data. Transfer learning is another technique that can help when you have limited data for your CV projects.
james_choi 1 year ago next
@kimberly_park: Absolutely, transfer learning has helped me get good results in my CV projects with limited data. What tools or libraries have you used for transfer learning?
kimberly_park 1 year ago next
@james_choi: I have used Keras and TensorFlow for transfer learning. They provide pre-trained models that can be fine-tuned for your specific CV task with limited data.
george_ahn 1 year ago prev next
Has anyone explored using reinforcement learning for CV? I'm curious how it could be applied to visual search or tracking tasks.
michelle_huang 1 year ago next
@george_ahn: I haven't personally explored reinforcement learning in CV, but I have read research papers on the topic. It is a promising direction, but still challenging to make it work well in real-world applications.
dan_park 1 year ago prev next
Another area of interest for me in computer vision is real-time computation. I'm working on an autonomous vehicle project and we need to make CV computations fast and efficient.
lynn_jung 1 year ago next
@dan_park: Real-time computation is definitely important for many computer vision applications. Have you consider using GPUs or FPGAs for accelerated computation?
dan_park 1 year ago next
@lynn_jung: Yes, we are using GPUs for accelerated computation. But we are always looking for new methods to speed up our CV computations. Thank you for the tip on FPGAs, I will look into it further.