We lifted 15 functions out of this paper's own repositories and ran 11 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| facebookresearch/GloRe | canonical | 1 of 1 |
| yangyucheng000/glore_res200 | pwc_unofficial | 6 of 10 |
| ChriXiang/GloRe_pytorch | reimplementation | 4 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| ResNet18 | Ran | ChriXiang/GloRe_pytorch/resnet_example.py pointer only (licence: NONE) · get_code("fb183196c497a93d") |
| ResNet34 | Ran | ChriXiang/GloRe_pytorch/resnet_example.py pointer only (licence: NONE) · get_code("e39cd693a1b17d14") |
| ResNet50 | Ran | ChriXiang/GloRe_pytorch/resnet_example.py pointer only (licence: NONE) · get_code("5dc8f93be8ef2d48") |
| autofill | Ran | facebookresearch/GloRe/test/test-single-clip.py code served (permissive licence) · get_code("53974ebeab9f0b81") |
| calcul_acc | Ran | yangyucheng000/glore_res200/postprocess.py code served (permissive licence) · get_code("5af3c724a0f8260c") |
| get_lr | Ran | yangyucheng000/glore_res200/src/lr_generator.py code served (permissive licence) · get_code("ccb82a7bb4dc4e76") |
| glore | Ran | ChriXiang/GloRe_pytorch/glore.py pointer only (licence: NONE) · get_code("1f6faceba55c7873") |
| linear_warmup_lr | Ran | yangyucheng000/glore_res200/src/lr_generator.py code served (permissive licence) · get_code("9da7260d717664f9") |
| shear_x | Ran | yangyucheng000/glore_res200/src/transform_utils.py code served (permissive licence) · get_code("8eaf53b48155177c") |
| shear_y | Ran | yangyucheng000/glore_res200/src/transform_utils.py code served (permissive licence) · get_code("523681f17bc13539") |
| translate_x_rel | Ran | yangyucheng000/glore_res200/src/transform_utils.py code served (permissive licence) · get_code("f38706e32849458d") |
| cre_groundtruth_dict_fromjson | Not yet run | yangyucheng000/glore_res200/infer/util/eval_by_sdk.py code served (permissive licence) · get_code("91090d4b24121f00") |
| cre_groundtruth_dict_fromtxt | Not yet run | yangyucheng000/glore_res200/infer/util/eval_by_sdk.py code served (permissive licence) · get_code("cc13de83d0285dde") |
| gen_file_name | Not yet run | yangyucheng000/glore_res200/infer/util/eval_by_sdk.py code served (permissive licence) · get_code("8e4b6f31a18e273d") |
| warmup_cosine_annealing_lr | Not yet run | yangyucheng000/glore_res200/src/lr_generator.py code served (permissive licence) · get_code("d4a0bdf1190319c4") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Globally modeling and reasoning over relations between regions can be beneficial for many computer vision tasks on both images and videos. Convolutional Neural Networks (CNNs) excel at modeling local relations by convolution operations, but they are typically inefficient at capturing global relations between distant regions and require stacking multiple convolution layers. In this work, we propose a new approach for reasoning globally in which a set of features are globally aggregated over the coordinate space and then projected to an interaction space where relational reasoning can be efficiently computed. After reasoning, relation-aware features are distributed back to the original coordinate space for down-stream tasks. We further present a highly efficient instantiation of the proposed approach and introduce the Global Reasoning unit (GloRe unit) that implements the coordinate-interaction space mapping by weighted global pooling and weighted broadcasting, and the relation reasoning via graph convolution on a small graph in interaction space. The proposed GloRe unit is lightweight, end-to-end trainable and can be easily plugged into existing CNNs for a wide range of tasks. Extensive experiments show our GloRe unit can consistently boost the performance of state-of-the-art backbone architectures, including ResNet, ResNeXt, SE-Net and DPN, for both 2D and 3D CNNs, on image classification, semantic segmentation and video action recognition task.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1811.12814")
get_code_for_paper("1811.12814")
have("1811.12814")
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