We lifted 3 functions out of this paper's own repositories and ran 0 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 |
|---|---|---|
| hudaAlamri/DSTC7-Audio-Visual-Scene-Aware-Dialog-AVSD-Challenge | pwc_unofficial | 0 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| load | Not yet run | hudaAlamri/DSTC7-Audio-Visual-Scene-Aware-Dialog-AVSD-Challenge/AVSD_Baseline/Baseline/local/data_handler.py code served (permissive licence) · get_code("a8d85743d4b8f715") |
| make_batch | Not yet run | hudaAlamri/DSTC7-Audio-Visual-Scene-Aware-Dialog-AVSD-Challenge/AVSD_Baseline/Baseline/local/data_handler.py code served (permissive licence) · get_code("83b8b889613b52d2") |
| make_batch_indices | Not yet run | hudaAlamri/DSTC7-Audio-Visual-Scene-Aware-Dialog-AVSD-Challenge/AVSD_Baseline/Baseline/local/data_handler.py code served (permissive licence) · get_code("04573f83ecde5ef2") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Dialog systems need to understand dynamic visual scenes in order to have conversations with users about the objects and events around them. Scene-aware dialog systems for real-world applications could be developed by integrating state-of-the-art technologies from multiple research areas, including: end-to-end dialog technologies, which generate system responses using models trained from dialog data; visual question answering (VQA) technologies, which answer questions about images using learned image features; and video description technologies, in which descriptions/captions are generated from videos using multimodal information. We introduce a new dataset of dialogs about videos of human behaviors. Each dialog is a typed conversation that consists of a sequence of 10 question-and-answer(QA) pairs between two Amazon Mechanical Turk (AMT) workers. In total, we collected dialogs on roughly 9,000 videos. Using this new dataset for Audio Visual Scene-aware dialog (AVSD), we trained an end-to-end conversation model that generates responses in a dialog about a video. Our experiments demonstrate that using multimodal features that were developed for multimodal attention-based video description enhances the quality of generated dialog about dynamic scenes (videos). Our dataset, model code and pretrained models will be publicly available for a new Video Scene-Aware Dialog challenge.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1806.08409")
get_code_for_paper("1806.08409")
have("1806.08409")
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