The training losses in the GCG task about groundinglmm HOT 1 CLOSED

Hi @wxpqq826615304,

Thank you for your interest in or work. The GCG task incorporates two forms of loss: the Autoregressive Cross-Entropy loss, which is applied to the output of the LMM, and the segmentation loss, which is a combination of the per-pixel BCE loss and the DICE loss. Although there is no loss explicitly designed to facilitate the matching between specific phrases and their corresponding segmentation masks, the integration of these two loss components indirectly addresses this alignment.

Consider a scenario where the expected output for a GCG task is given as "<p>The man</p> [SEG] sitting on the <p>bench</p>[SEG]," which serves as the Ground Truth (GT). In this context, the BCE loss is used to evaluate the match between the predicted mask associated with the first [SEG] token and the GT mask corresponding to "the man." Consequently, the positioning of tokens, refined by the LMM's Cross-Entropy loss, along with the embeddings associated with each [SEG] token's position (as refined by the mask loss), collaboratively contribute to enhancing the matching between phrases and segmentation masks implicitly. This process ensures that the tasks of text generation and segmentation, though distinct, are cohesively integrated through the applied losses, thus facilitating the implicit alignment of phrases with their respective masks without the need for a separate, explicit matching loss.

Hope this helps. Thank you.

from groundinglmm.