Quantifying the Impact of Complementary Visual and Textual Cues Under Image Captioning
Bibliographic record
Abstract
Describing an image with natural sentence without human involvement requires knowledge of both image processing and Natural Language Processing (NLP). Most of the existing works are based on unimodal representations of the visual and textual contents using an Encoder-Decoder (EnDec) Deep Neural Network (DNN), where the input images are encoded using Convolutional Neural Network (CNN) and the caption is generated by a Recurrent Neural Network (RNN). This paper dives into a basic image captioning model to quantify the impact of multimodal representation of the visual and textual cues. The multimodal representation is carried out via an early fusion of encoded visual cues from different CNNs, along with combined textual features from different word embedding techniques. The resultant of the multimodal representation of the visual and textual cues are employed to train a Long Short-Term Memory (LSTM)-based baseline caption generator to quantify the impact of various levels of complementary feature mutations. The ablation study involves two different CNN feature extractors and two types of textual feature extractors, shows that exploitation of the complementary information outperforms the unimodal representations significantly with endurable timing overhead.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".