All Questions
7 questions
1
vote
1
answer
101
views
Single-Shot Learning for Object Re-Identification
I am looking for a way to re-identify/classify/recognize x real life objects (x < 50) with a camera. Each object should be presented to the AI only once for learning and there's always only one of ...
- 111
2
votes
0
answers
50
views
FasterRCNN's RPN network training
I would like to know if my understanding of RPN training is correct, and if never training the RPN on some specific anchor box is bad (i.e if the anchor never sees good nor bad examples).
To make my ...
2
votes
1
answer
208
views
Is it possible to train a CNN to predict the dimensions of primitive objects from point clouds?
Is it possible to train a convolutional neural network (CNN) to predict the dimensions of primitive objects such as (spheres, cylinders, cuboids, etc.) from point clouds?
The input to the CNN will be ...
- 43
2
votes
1
answer
170
views
Pose estimation using CNNs on Point clouds
In the case of single shot detection of point clouds, that is the point cloud of an object is taken only from one camera view without any registration. Can a Convolutional Network estimate the 6d pose ...
- 43
1
vote
1
answer
47
views
Having trouble understanding some of the details of R-CNN (first one)
Here is what I understand (what I think I understand).
We first train out model on our images using transfer learning.
So now we have a pre-trained model.
For each image in out dataset, we compute ...
- 209
5
votes
0
answers
365
views
What are the ways to calculate the error rate of a deep Convolutional Neural Network, when the network produces different results using the same data?
I am new to the object recognition community. Here I am asking about the broadly accepted ways to calculate the error rate of a deep CNN when the network produces different results using the same data....
- 51
5
votes
1
answer
812
views
In YOLO, when is $\mathbb{1}_{i j}^{\mathrm{obj}} = 1$, and what are the ground-truth labels for $x_i$ and $y_i$?
I'm trying to implement a custom version of the YOLO neural network. Originally, it was described in the paper You Only Look Once: Unified, Real-Time Object Detection (2016). I have some problems ...
- 276