Papers › Self-critical Sequence Training for Image Captioning

Self-critical Sequence Training for Image Captioning

2 Dec 2016CVPR 2017 7arXiv:1612.00563archive 2025-07-28

Steven J. Rennie, Etienne Marcheret, Youssef Mroueh, Jarret Ross, Vaibhava Goel

Recently it has been shown that policy-gradient methods for reinforcement learning can be utilized to train deep end-to-end systems directly on non-differentiable metrics for the task at hand. In this paper we consider the problem of optimizing image captioning systems using reinforcement learning, and show that by carefully optimizing our systems using the test metrics of the MSCOCO task, significant gains in performance can be realized. Our systems are built using a new optimization approach that we call self-critical sequence training (SCST). SCST is a form of the popular REINFORCE algorithm that, rather than estimating a "baseline" to normalize the rewards and reduce variance, utilizes the output of its own test-time inference algorithm to normalize the rewards it experiences. Using this approach, estimating the reward signal (as actor-critic methods must do) and estimating normalization (as REINFORCE algorithms typically do) is avoided, while at the same time harmonizing the model with respect to its test-time inference procedure. Empirically we find that directly optimizing the CIDEr metric with SCST and greedy decoding at test-time is highly effective. Our results on the MSCOCO evaluation sever establish a new state-of-the-art on the task, improving the best result in terms of CIDEr from 104.9 to 114.7.

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Syntology Ran 8 of 13 code samples harvested from 2 repositories linked to this paper; 5 have no recorded run. Of those that ran: 1 ran · violated contract; 7 ran · our draft was wrong.

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31 repositories listed; official and paper-mentioned ones first.

Dlut-lab-zmn/Image-Captioning-Attack mentioned on GitHubpytorch report
FJSam/SelfCritical_ImageCaptioning mentioned on GitHubpytorch report
Kumar-Tarun/summarization mentioned on GitHubpytorch report
SJTU-VLG/ImageCaption-LBPF-qy mentioned on GitHubpytorch report
Zhendong-Wang/arsm_image_captioning mentioned on GitHubpytorch report
arjung128/image-paragraph-captioning mentioned on GitHubpytorch report
hjjpku/dynamic_graph_caption mentioned on GitHubpytorch report
husthuaan/AAT mentioned on GitHubpytorchMIT report
husthuaan/AoANet mentioned on GitHubpytorchMIT report
jchenghu/sacreeos mentioned on GitHubpytorchApache-2.0 report
kangkang59812/GraphCaption mentioned on GitHubpytorch report
king-zark/self_critical mentioned on GitHubpytorch report
krasserm/fairseq-image-captioning mentioned on GitHubpytorch report
lukemelas/image-paragraph-captioning mentioned on GitHubpytorch report
mazm13/Image-to-Tree.pytorch mentioned on GitHubpytorch report
mykolesiko/nlp_projects mentioned on GitHubpytorch report
quantum-fusion/AI-deeplearning mentioned on GitHubpytorch report
ruotianluo/GoogleConceptualCaptioning mentioned on GitHubpytorch report
ruotianluo/ImageCaptioning.pytorch mentioned on GitHubpytorch report
ruotianluo/Transformer_Captioning mentioned on GitHubpytorch report
ruotianluo/neuraltalk2.pytorch mentioned on GitHubpytorch report
ruotianluo/self-critical.pytorch mentioned on GitHubpytorch report
s-52/ImageCaptions mentioned on GitHubpytorch report
sgondala/GoogleConceptualCaptioning mentioned on GitHubpytorch report
ukyh/switch_disc_caption mentioned on GitHubpytorch report
wtliao/ImageTransformer mentioned on GitHubpytorch report
xiaobai714/image_caption mentioned on GitHubpytorch report
ylqi/gl-rg mentioned on GitHubpytorchMIT report

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13 samples harvested; 8 ran; 0 honoured the contract we drafted; 5 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · violated contract
7ran · our draft was wrong
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build_vocab ruotianluo/GoogleConceptualCaptioning/scripts/prepro_labels.py community (archive-listed) ran · our draft was wrong MIT (permissive) · 41b9ec0c269d8a91 · report
encode_captions ruotianluo/GoogleConceptualCaptioning/scripts/prepro_labels.py community (archive-listed) ran · our draft was wrong MIT (permissive) · 4aad8242843ac47b · report
pack_wrapper husthuaan/AAT/models/AttModel.py community (archive-listed) ran · our draft was wrong MIT (permissive) · d2379d710eedc4ae · report
pad_unsort_packed_sequence husthuaan/AAT/models/AttModel.py community (archive-listed) ran · our draft was wrong MIT (permissive) · bfac58a04b6835f5 · report
sort_pack_padded_sequence husthuaan/AAT/models/AttModel.py community (archive-listed) ran · our draft was wrong fingerprinted MIT (permissive) · e56d2f9cbd9cb8b1 · report
count_bad husthuaan/AAT/eval_utils.py community (archive-listed) unverified MIT (permissive) · 0a4de8f498e3e70c · report
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resnet34 husthuaan/AAT/misc/resnet.py community (archive-listed) unverified MIT (permissive) · d206d964368eeb24 · report
resnet50 husthuaan/AAT/misc/resnet.py community (archive-listed) unverified MIT (permissive) · fade30a7dab7a039 · report
attention identical code first harvested elsewhere ran · our draft was wrong licence of this copy not recorded · 20bb0ff4a2d77a03 · report
clones identical code first harvested elsewhere ran · our draft was wrong licence of this copy not recorded · a3722169bbc81569 · report
subsequent_mask identical code first harvested elsewhere ran · violated contract licence of this copy not recorded · dd03765c7da9ca51 · report

Tasks

Image CaptioningPolicy Gradient MethodsReinforcement LearningReinforcement Learning (RL)reinforcement-learning

Results from the paper archive 2025-07-28

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Methods

Introduced by this paper: SCST

REINFORCESCST

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