Clarification on Mask Hinting vs Mask Prompting During Tracking

[tal-grossman]

First, I’d like to thank you for the well-documented and well-structured repository. It has been incredibly helpful in integrating SAM2 into my own model.

🔍 My Use Case

I’m working on a hybrid model that combines SAM2 with an additional segmentation network. The goal is to assist object tracking by providing segmentation predictions from another model as a "mask hint."

❓ My Questions

1️⃣ Mask Hinting vs Mask Prompting in Tracking

• I want to understand the difference between mask hinting and mask prompting during tracking (not just initialization).
• I am currently passing my predicted mask as a hint to a customized step_video_masking_w_hint function, but the results don’t seem as “strongly enforced” as I expected.
• In the documentation, you mentioned that hinting is similar to mask prompting in SAM2’s prompt encoder.
• Does mask hinting behave the same way as prompting in terms of "forcing" the segmentation?

2️⃣ Enforcing the Predicted Mask in the Mask Decoder

• If I want to force my predicted mask into the SAM2 mask decoder, should I follow the approach in video_segmentation_from_mask.py (where initialize_from_mask is used to start tracking)?
• Would that be a better alternative than mask hinting for enforcing segmentation?
• What are the advantages of hinting instead of directly modifying the mask decoder?

🛠 Summary of What I’m Trying to Achieve

• Segment frames during tracking using another model (which processes projected image encodings).
• Pass its predicted mask as a hint to SAM2’s segmentation.
• Ensure that the hint is strongly enforced, or explore whether mask prompting is better.

Would appreciate any clarification on how mask hinting interacts with the mask decoder compared to direct mask prompting.

Thank you in advance! 🙌

[heyoeyo]

Thanks for the kind words!

Mask Hinting vs Mask Prompting

Yes 'mask hinting' in muggledsam is more or less the same as 'mask prompting' in the original SAMv1/SAMv2 models (e.g. providing a mask_hint is the same as the masks input on the SAM prompt encoder). The renaming is based on the poorer performance compared to the other prompt types. It's mainly included for feature parity with the original implementation.

Does mask hinting behave the same way as prompting in terms of "forcing" the segmentation?

At least in theory, yes, but it doesn't work well (I don't think it was a focus of the model training). The run_image.py script has support for providing a mask hint (using the --mask_path flag) which can help give a sense of what it does. Here's an example comparing a point prompt to a mask prompt using the v2.1 tiny model:

Mask from a single point prompt:

Mask from mask hint/prompt only (using the mask above as input):

In general you can create 'nice' masks using point/box prompts and then re-run the script using the results from those prompts as mask hints to see what it does. You can also still provide FG/BG/box prompts when doing this, to see how they 'combine' with the mask hint. It works ok for some images, but most of the time the mask comes out messy (and it varies a lot by model).

how mask hinting interacts with the mask decoder compared to direct mask prompting.

I think it might help to clarify the sequence of steps followed by the model during tracking. It's something like:

A) Encode image B) Update image encoding using prior memory encodings C) Predict masks (based on modified image encodings and prompts) D) Encode memory (based on the predicted mask & image encoding) E) (optional) Store memory encoding for use in step (B) on next frame

Normally during tracking there are no prompts provided in step (C), so the main way to influence tracking is through the memory encodings provided in step (B). These are the memory/pointer inputs given to the stepping code.

However, if you try to use mask hints, it would be like influencing the results through step (C). Giving prompts during tracking could help if it improves the predictions, but would require modifying the stepping code (maybe these are the custom changes you mentioned?). However the mask hints don't always/often improve the predictions, so I wouldn't expect them to (consistently) help with tracking.

The initialize_from_mask function (which I think is what you mean by 'direct mask prompting') just does step (A), then step (D) using the provided mask, so it actually skips prompting altogether. The memory encoding it generates can be used to influence the model through step (B).

Enforcing the Predicted Mask in the Mask Decoder

There are two options I'd consider:

1. The initialize_from_mask approach you mentioned. You could use this to create prompt or 'previous frame' memory using masks from the other model to bias the SAM predictions towards what the other model is predicting.

2. Have SAM generate masks, but use the masks from the other model to make point and/or box prompts for SAM (e.g. the bounding box of the mask or it's center as a point prompt). The advantage here is that it would leave the mask generation to the SAM model, and since it's trained on working with it's own masks, it might give better tracking? I'd mainly consider this for making memory encodings (e.g. using initialize_video_masking), but I guess it could also be used to provide prompts during tracking, though I've never tried this so not sure how well it works.

[tal-grossman]

I appreciate the detailed and thoughtful answer! The clarification on mask hinting vs mask prompting and the breakdown of the SAM2 tracking sequence was extremely helpful.

Both of the solutions you suggested make sense and they were about to be my next step. Maybe this could be an interesting experiment to include in the experiments directory? I think it would be beneficial for others to explore how different prompting strategies influence tracking.

And if I have you here I'll take the opportunity to ask you regarding training of my model: As part of my project, I am training an auxiliary segmentation model to act as a kind of automatic prompter for the SAM2 mask decoder.

In your experience:

• Should the loss be applied to the segmentation model’s own output?
• Or should the loss be applied to the final mask-decoder output (after passing through SAM2)?
• Or both?

I appreciate any insights you might have on how to structure the loss function for optimal results.

Thanks again for your amazing work and support! 🚀

[heyoeyo]

Maybe this could be an interesting experiment to include in the experiments directory?

Potentially ya! I try to keep the whole repo dependent on only a few basic dependencies, so I'm not sure how this could work without pulling in an entire separate model (and related dependencies). Though I am curious about how prompting during tracking behaves and I guess that's something that could be done manually... I'd have to think about how that could be implemented though.

Should the loss be applied to the segmentation model’s own output? Or should the loss be applied to the final mask-decoder output (after passing through SAM2)?

Assuming the auxiliary model is generating a full mask prediction, then training just on the aux model output makes sense. I think the SAM mask decoder wouldn't even need to run if the other model generates good enough masks. It might end up similar to a promptless finetuning project that was posted for SAMv1, which could be a useful reference.