Do Convnets Learn Correspondence?
Authors: Jonathan L Long, Ning Zhang, Trevor Darrell
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we study the effectiveness of convnet activation features for tasks requiring correspondence. We present evidence that convnet features localize at a much finer scale than their receptive field sizes, that they can be used to perform intraclass aligment as well as conventional hand-engineered features, and that they outperform conventional features in keypoint prediction on objects from PASCAL VOC 2011 [4]. |
| Researcher Affiliation | Academia | Jonathan Long Ning Zhang Trevor Darrell University of California Berkeley {jonlong, nzhang, trevor}@cs.berkeley.edu |
| Pseudocode | No | No structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures) were found in the paper. |
| Open Source Code | No | The paper states 'our network is the publicly available caffe reference model.', referring to a third-party tool used, but does not provide concrete access to source code for the methodology developed in this paper. |
| Open Datasets | Yes | We perform experiments using a network architecture almost identical1 to that popularized by Krizhevsky et al. [2] and trained for classification using the 1.2 million images of the ILSVRC 2012 challenge dataset [1]. All experiments are implemented using caffe [27], and our network is the publicly available caffe reference model. |
| Dataset Splits | Yes | We set the SVM parameter C = 10 6 for all experiments based on five-fold cross validation on the training set (see Figure 5). |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running experiments were provided in the paper. |
| Software Dependencies | No | The paper states 'All experiments are implemented using caffe [27]', but does not provide a specific version number for Caffe or any other software dependencies. |
| Experiment Setup | Yes | We set the SVM parameter C = 10 6 for all experiments based on five-fold cross validation on the training set (see Figure 5)... We rescale each bounding box to 500 500 and compute conv5 (with a stride of 16 pixels)... For each keypoint, we train a linear SVM with hard negative mining... We combine these to yield a final score f(Xi) = s(Xi)1 ηp(Xi)η, where η [0, 1] is a tradeoff parameter. In our experiments, we set η = 0.1 by cross validation. |