Re-calibrated Contrastive Loss for Transformation-Aware Prompt Conditioning in Vision-Language Models
Paper โข 2609.06967 โข Published
Checkpoints for Re-calibrated Contrastive Loss for Transformation-Aware Prompt Conditioning in Vision-Language Models (BMVC 2026). Paper ยท Code
Each folder holds model.safetensors (the full model state dict, fp32) and config.json, which names the released
config to evaluate it with.
hf download SoongE/ReCalCon --include "distribution_shift/imagenet_b16/*" --local-dir checkpoints
python -m scripts.eval --config imagenet_b16 \
--eval.checkpoint checkpoints/distribution_shift/imagenet_b16/model.safetensors
Distribution shift (top-1 accuracy, %)
| Checkpoint | Row | ImageNet | -R | -A | -V2 | -Sketch | ObjectNet |
|---|---|---|---|---|---|---|---|
distribution_shift/imagenet_b16 |
ViT-B/16, Update, ImageNet | 83.284 | 71.607 | 54.893 | 74.1 | 51.781 | 58.388 |
distribution_shift/imagenet_b32 |
ViT-B/32, Update, ImageNet | 79.354 | 62.41 | 32.573 | 68.15 | 44.503 | 49.203 |
distribution_shift/imagenet_petl_b16 |
ViT-B/16, Freeze (PETL), ImageNet | 80.206 | 73.163 | 50.867 | 70.13 | 49.614 | 55.691 |
distribution_shift/imagenet_petl_b32 |
ViT-B/32, Freeze (PETL), ImageNet | 76.12 | 63.563 | 30.453 | 64.52 | 42.267 | 46.662 |
iWildCam (macro-F1, %). The released evaluation reports the OOD split.
| Checkpoint | Row | ID (macro-F1) | OOD (macro-F1) |
|---|---|---|---|
distribution_shift/iwildcam_b16 |
ViT-B/16, Update, iWildCam | 51.253 | 37.803 |
distribution_shift/iwildcam_b32 |
ViT-B/32, Update, iWildCam | 42.116 | 29.275 |
distribution_shift/iwildcam_petl_b16 |
ViT-B/16, Freeze (PETL), iWildCam | 45.49 | 30.327 |
distribution_shift/iwildcam_petl_b32 |
ViT-B/32, Freeze (PETL), iWildCam | 34.709 | 24.502 |
Transfer learning (top-1 accuracy, %)
| Checkpoint | Dataset | Accuracy |
|---|---|---|
transfer/caltech101 |
Caltech101 | 97.85 |
transfer/flowers102 |
Flowers102 | 98.894 |
transfer/pcam |
PCam | 89.413 |
transfer/stanfordcars |
StanfordCars | 91.419 |
Apache License 2.0. The models are fine-tuned from OpenAI CLIP (MIT License); each evaluation dataset keeps its own terms.
@inproceedings{oh2026recalibrated,
title = {Re-calibrated Contrastive Loss for Transformation-Aware Prompt Conditioning in Vision-Language Models},
author = {Oh, Seungmin and Kang, Seunghun and Ryu, Jongbin},
booktitle = {Proceedings of the British Machine Vision Conference},
year = {2026},
publisher = {BMVA},
url = {https://arxiv.org/abs/2609.06967}
}
Base model
openai/clip-vit-base-patch16