Zhaoxiang Zhang, Chunfeng Song, Renjie Zou
We lifted 15 functions out of this paper's own repositories and ran 12 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 |
|---|---|---|
| googolxx/stf | canonical | 12 of 15 |
| Function | Status | Where it lives |
|---|---|---|
| conv | Ran | googolxx/stf/compressai/models/utils.py code served (permissive licence) · get_code("f2cea3e9848ae52d") |
| conv1x1 | Ran | googolxx/stf/compressai/layers/layers.py code served (permissive licence) · get_code("15eae621014906be") |
| conv3x3 | Ran | googolxx/stf/compressai/layers/layers.py code served (permissive licence) · get_code("0cbeed6985b2cf30") |
| find_named_buffer | Ran | googolxx/stf/compressai/models/utils.py code served (permissive licence) · get_code("a94389b554ddc179") |
| get_scale_table | Ran | googolxx/stf/compressai/models/cnn.py code served (permissive licence) · get_code("feb29b32fc1ec50e") |
| get_scale_table | Ran | googolxx/stf/compressai/models/stf.py code served (permissive licence) · get_code("1eca81f63e28f103") |
| ste_round | Ran | googolxx/stf/compressai/ops/ops.py code served (permissive licence) · get_code("8e528b0db078ab0e") |
| subpel_conv3x3 | Ran | googolxx/stf/compressai/layers/layers.py code served (permissive licence) · get_code("8bc3a872b71e34f4") |
| window_partition | Ran | googolxx/stf/compressai/models/stf.py code served (permissive licence) · get_code("f6b2d702fc756d64") |
| window_partition | Ran | googolxx/stf/compressai/layers/win_attention.py code served (permissive licence) · get_code("565f4668acf5b8f4") |
| window_reverse | Ran | googolxx/stf/compressai/layers/win_attention.py code served (permissive licence) · get_code("fb32094c6dbece71") |
| window_reverse | Ran | googolxx/stf/compressai/models/stf.py code served (permissive licence) · get_code("7b0d02f75bf6b439") |
| find_named_module | Not yet run | googolxx/stf/compressai/models/utils.py code served (permissive licence) · get_code("0b4d7c9d1baaf828") |
| lower_bound_bwd | Not yet run | googolxx/stf/compressai/ops/bound_ops.py code served (permissive licence) · get_code("07f8484bf7fe9b22") |
| lower_bound_fwd | Not yet run | googolxx/stf/compressai/ops/bound_ops.py code served (permissive licence) · get_code("9d2139d32f9bca32") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Learned image compression methods have exhibited superior rate-distortion performance than classical image compression standards. Most existing learned image compression models are based on Convolutional Neural Networks (CNNs). Despite great contributions, a main drawback of CNN based model is that its structure is not designed for capturing local redundancy, especially the nonrepetitive textures, which severely affects the reconstruction quality. Therefore, how to make full use of both global structure and local texture becomes the core problem for learning-based image compression. Inspired by recent progresses of Vision Transformer (ViT) and Swin Transformer, we found that combining the local-aware attention mechanism with the global-related feature learning could meet the expectation in image compression. In this paper, we first extensively study the effects of multiple kinds of attention mechanisms for local features learning, then introduce a more straightforward yet effective window-based local attention block. The proposed window-based attention is very flexible which could work as a plug-and-play component to enhance CNN and Transformer models. Moreover, we propose a novel Symmetrical TransFormer (STF) framework with absolute transformer blocks in the down-sampling encoder and up-sampling decoder. Extensive experimental evaluations have shown that the proposed method is effective and outperforms the state-of-the-art methods. The code is publicly available at https://github.com/ Googolxx/STF.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2203.08450")
get_code_for_paper("2203.08450")
have("2203.08450")
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