Papers › Supervised Fine-tuning in turn Improves Visual Foundation Models

Supervised Fine-tuning in turn Improves Visual Foundation Models

18 Jan 2024arXiv:2401.10222archive 2025-07-28

Xiaohu Jiang, Yixiao Ge, Yuying Ge, Dachuan Shi, Chun Yuan, Ying Shan

Image-text training like CLIP has dominated the pretraining of vision foundation models in recent years. Subsequent efforts have been made to introduce region-level visual learning into CLIP's pretraining but face scalability challenges due to the lack of large-scale region-level datasets. Drawing inspiration from supervised fine-tuning (SFT) in natural language processing such as instruction tuning, we explore the potential of fine-grained SFT in enhancing the generation of vision foundation models after their pretraining. Thus a two-stage method ViSFT (Vision SFT) is proposed to unleash the fine-grained knowledge of vision foundation models. In ViSFT, the vision foundation model is enhanced by performing visual joint learning on some in-domain tasks and then tested on out-of-domain benchmarks. With updating using ViSFT on 8 V100 GPUs in less than 2 days, a vision transformer with over 4.4B parameters shows improvements across various out-of-domain benchmarks including vision and vision-linguistic scenarios.

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accuracy tencentarc/visft/mmf/models/visft/caption_decoder.py official repository ran fingerprinted Apache-2.0 (permissive) · a7f346bb7a69c595 · report
get_abs_pos tencentarc/visft/mmf/models/visft/eva_vit_g.py official repository ran Apache-2.0 (permissive) · a3619d3754ec34c3 · report
get_norm tencentarc/visft/mmf/models/visft/misc.py official repository ran Apache-2.0 (permissive) · cd9b01650559d0be · report
get_uncertain_point_coords_with_randomness tencentarc/visft/mmf/models/visft/point_sampler.py official repository ran Apache-2.0 (permissive) · 9dbedd9018e1a9ad · report
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nested_tensor_from_tensor_list tencentarc/visft/mmf/models/visft/misc.py official repository ran Apache-2.0 (permissive) · e8c3f53b24248086 · report
batch_dice_loss tencentarc/visft/mmf/models/visft/segment_matcher.py official repository unverified Apache-2.0 (permissive) · bc2cb481a75c370d · report
batch_sigmoid_ce_loss tencentarc/visft/mmf/models/visft/segment_matcher.py official repository unverified Apache-2.0 (permissive) · 1edd24985036b0bf · report
calculate_uncertainty tencentarc/visft/mmf/models/visft/segment_criterion.py official repository unverified Apache-2.0 (permissive) · 2dcb8123d89bb1ff · report
dice_loss tencentarc/visft/mmf/models/visft/segment_criterion.py official repository unverified Apache-2.0 (permissive) · 89f75e54ff128be0 · report
point_sample tencentarc/visft/mmf/models/visft/point_sampler.py official repository unverified Apache-2.0 (permissive) · 90469906eb7f6e8f · report
sigmoid_ce_loss tencentarc/visft/mmf/models/visft/segment_criterion.py official repository unverified Apache-2.0 (permissive) · d0c61e8dba511aa3 · report

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Methods

AttentionCLIPDense ConnectionsLayer NormalizationLinear LayerMulti-Head AttentionResidual ConnectionSFTSoftmaxVision Transformer

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