Comments (3)
Hi! We used one transformer to enable fusion between modalities, but in our ablation, we also considered using different transformers for different modalities (Table 4 “single-mod. transformer per mod”). The config for this experiment is here. For t->va retrieval, we found it beneficial to use one transformer. With different transformers for different modalities, video and audio modalities are processed separately in corresponding transformers, and then video and audio embeddings are just summed up to obtain a video-audio representation. However, when we use one transformer (which is trained on all modality combinations) we forward tokens from both video and audio modalities together, allowing them to attend to each other, and therefore we obtain a fused video-audio representation.
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thx~
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I have tried your method on Ads risk recall situation, and got about 1% boost🎉. We use a lot of modals, so this is still more troublesome. Hope you have more and better solutions in the future~
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Related Issues (14)
- Some difference between the code and paper HOT 4
- Regarding feature computation for S3D and CLIP HOT 2
- Question about tokens HOT 2
- How are the inputs look like
- Code problem
- Problem
- Is this an unsupervised video retrieval method? HOT 1
- How many GPUs are used & how long is it for training HOT 1
- What hardware resources do I need to run your model and data? HOT 1
- code problem HOT 1
- Loss problem HOT 1
- data size HOT 1
- Token concatenate question HOT 4
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