We lifted 8 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 |
|---|---|---|
| katerynaCh/multimodal-emotion-recognition | canonical | 6 of 7 |
| shravan-18/AVTCA | pwc_unofficial | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| calculate_accuracy | Ran | katerynaCh/multimodal-emotion-recognition/utils.py code served (permissive licence) · get_code("91215ba9fdfd75c0") |
| calculate_accuracy1 | Ran | shravan-18/AVTCA/utils.py code served (permissive licence) · get_code("b78f36e2a6b31a28") |
| channel_shuffle | Ran | katerynaCh/multimodal-emotion-recognition/models/efficientface.py code served (permissive licence) · get_code("b9da06d4f527dd6c") |
| conv1d_block | Ran | katerynaCh/multimodal-emotion-recognition/models/multimodalcnn.py code served (permissive licence) · get_code("f5dfc77d5b10b99f") |
| conv1d_block_audio | Ran | katerynaCh/multimodal-emotion-recognition/models/multimodalcnn.py code served (permissive licence) · get_code("c4e16f9f1a9c4648") |
| drop_path | Ran | katerynaCh/multimodal-emotion-recognition/models/transformer_timm.py code served (permissive licence) · get_code("39eace7e2822504f") |
| video_loader | Ran | katerynaCh/multimodal-emotion-recognition/datasets/ravdess.py code served (permissive licence) · get_code("ed1d9f245b263267") |
| depthwise_conv | Not yet run | katerynaCh/multimodal-emotion-recognition/models/efficientface.py code served (permissive licence) · get_code("a3cf9b5f7f40034b") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this paper, we consider the problem of multimodal data analysis with a use case of audiovisual emotion recognition. We propose an architecture capable of learning from raw data and describe three variants of it with distinct modality fusion mechanisms. While most of the previous works consider the ideal scenario of presence of both modalities at all times during inference, we evaluate the robustness of the model in the unconstrained settings where one modality is absent or noisy, and propose a method to mitigate these limitations in a form of modality dropout. Most importantly, we find that following this approach not only improves performance drastically under the absence/noisy representations of one modality, but also improves the performance in a standard ideal setting, outperforming the competing methods.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2201.11095")
get_code_for_paper("2201.11095")
have("2201.11095")
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