#trilite — Public Fediverse posts

Accurate object localization at reduced annotation/computational cost?
#TriLite achieves this by pairing a pre-trained backbone, with our #TriHead component which separates ambiguous, foreground and background features.

The main highlights of #TriLite include:
• State-of-the-art performance
• < 1M trainable parameters
• single-stage training

We will be glad to get in touch at #CVPR2026 to discuss further.

We thank #DEFRA for supporting this research

#WSOL #CV
#sqIRL #IDLab #UAntwerp

Accurate object localization at reduced annotation/computational cost?
#TriLite achieves this by pairing a pre-trained backbone, with our #TriHead component which separates ambiguous, foreground and background features.

The main highlights of #TriLite include:
• State-of-the-art performance
• < 1M trainable parameters
• single-stage training

We will be glad to get in touch at #CVPR2026 to discuss further.

We thank #DEFRA for supporting this research

#WSOL #CV
#sqIRL #IDLab #UAntwerp

Accurate object localization at reduced annotation/computational cost?
#TriLite achieves this by pairing a pre-trained backbone, with our #TriHead component which separates ambiguous, foreground and background features.

The main highlights of #TriLite include:
• State-of-the-art performance
• < 1M trainable parameters
• single-stage training

We will be glad to get in touch at #CVPR2026 to discuss further.

We thank #DEFRA for supporting this research

#WSOL #CV
#sqIRL #IDLab #UAntwerp

Accurate object localization at reduced annotation/computational cost?
#TriLite achieves this by pairing a pre-trained backbone, with our #TriHead component which separates ambiguous, foreground and background features.

The main highlights of #TriLite include:
• State-of-the-art performance
• < 1M trainable parameters
• single-stage training

We will be glad to get in touch at #CVPR2026 to discuss further.

We thank #DEFRA for supporting this research

#WSOL #CV
#sqIRL #IDLab #UAntwerp