You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
I am using pre-trained VL-T5 to generate captions for Flickr30K images off-the-shelf i.e. without any finetuning. I modified the captioning scripts to predict directly. I observe very short captions through, almost like noun phrases. I am including some examples below. I have played with the '--gen_max_length' and '--num_beams' parameters but I still get very short outputs. Do you have any ideas why this may be happening? Or any suggestions for how to potentially generate longer captions?
Thank you in advance!
Shruti
purple shirt
cutting cake
smiling
large group of people
skier
The text was updated successfully, but these errors were encountered:
It's probably because the pretraining objective for text generation (span prediction) always involves short target text. I guess zero-shot captioning might now work well. You would need to tune the parameters at least slightly, through few-shot or full fine-tuning.
Hello,
I am using pre-trained VL-T5 to generate captions for Flickr30K images off-the-shelf i.e. without any finetuning. I modified the captioning scripts to predict directly. I observe very short captions through, almost like noun phrases. I am including some examples below. I have played with the '--gen_max_length' and '--num_beams' parameters but I still get very short outputs. Do you have any ideas why this may be happening? Or any suggestions for how to potentially generate longer captions?
Thank you in advance!
Shruti
The text was updated successfully, but these errors were encountered: