Bodo Rosenhahn, Michael Yang, Yuren Cong, Wentong Liao, Hanno Ackermann
We lifted 14 functions out of this paper's own repositories and ran 7 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 |
|---|---|---|
| yrcong/STTran | canonical | 2 of 9 |
| lunaproject22/aar | — | 5 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| TransformerDecoder | Ran | lunaproject22/aar/lib/transformer.py pointer only (licence: NONE) · get_code("22fd84f6ef1eaa10") |
| TransformerDecoderLayer | Ran | lunaproject22/aar/lib/transformer.py pointer only (licence: NONE) · get_code("65aa79798bcd2990") |
| TransformerEncoder | Ran | lunaproject22/aar/lib/transformer.py pointer only (licence: NONE) · get_code("040179c6bd8eb32f") |
| TransformerEncoderLayer | Ran | lunaproject22/aar/lib/transformer.py pointer only (licence: NONE) · get_code("cf0d96423bcfe244") |
| get_ranking | Ran | yrcong/STTran/lib/pytorch_misc.py code served (permissive licence) · get_code("a1e587129dd2dfaf") |
| pairwise | Ran | yrcong/STTran/lib/pytorch_misc.py code served (permissive licence) · get_code("fef49730c9e9a2b8") |
| transformer | Ran | lunaproject22/aar/lib/transformer.py pointer only (licence: NONE) · get_code("1e8a7993eb1f6599") |
| cuda_collate_fn | Not yet run | yrcong/STTran/dataloader/action_genome.py code served (permissive licence) · get_code("3b53b8b24f740103") |
| im_list_to_blob | Not yet run | yrcong/STTran/lib/funcs.py code served (permissive licence) · get_code("d614ca29b05d68ec") |
| load_word_vectors | Not yet run | yrcong/STTran/lib/word_vectors.py code served (permissive licence) · get_code("7fee693dfb2f64b5") |
| obj_edge_vectors | Not yet run | yrcong/STTran/lib/word_vectors.py code served (permissive licence) · get_code("3b52f8f6c24ea8da") |
| optimistic_restore | Not yet run | yrcong/STTran/lib/pytorch_misc.py code served (permissive licence) · get_code("6d0daf87a6ec7e39") |
| reporthook | Not yet run | yrcong/STTran/lib/word_vectors.py code served (permissive licence) · get_code("e31361e8a2fbb396") |
| transpose_packed_sequence_inds | Not yet run | yrcong/STTran/lib/funcs.py code served (permissive licence) · get_code("bbdc9c82250ba50d") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Dynamic scene graph generation aims at generating a scene graph of the given video. Compared to the task of scene graph generation from images, it is more challenging because of the dynamic relationships between objects and the temporal dependencies between frames allowing for a richer semantic interpretation. In this paper, we propose Spatial-temporal Transformer (STTran), a neural network that consists of two core modules: (1) a spatial encoder that takes an input frame to extract spatial context and reason about the visual relationships within a frame, and (2) a temporal decoder which takes the output of the spatial encoder as input in order to capture the temporal dependencies between frames and infer the dynamic relationships. Furthermore, STTran is flexible to take varying lengths of videos as input without clipping, which is especially important for long videos. Our method is validated on the benchmark dataset Action Genome (AG). The experimental results demonstrate the superior performance of our method in terms of dynamic scene graphs. Moreover, a set of ablative studies is conducted and the effect of each proposed module is justified. Code available at: https://github.com/yrcong/STTran.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2107.12309")
get_code_for_paper("2107.12309")
have("2107.12309")
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