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Set Prediction Guided by Semantic Concepts for Diverse Video Captioning
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Formulates diverse video captioning as semantic-concept-guided set prediction (SCG-SP), using concept-driven queries and a set-level Hungarian-matched loss with a concept-classification auxiliary task and diversity regularization.
Problem
Diverse video captioning generates low-diversity caption sets because existing CVAE- and control-based methods train on independent video-caption pairs and ignore the intra-set relationship.
Approach
Treat the caption set as a set prediction problem: concept detection produces conceptual queries that yield semantics-specific encodings, each decoded into a caption plus a concept-combination label, and trained with a Hungarian-matched set loss that adds a concept classification cost and a concept-based diversity regularizer.
Key results
- On MSRVTT with 20 captions, SCG-SP-Prefix reaches oracle CIDEr 67.7 and D-2 43.1, outperforming STR (67.2 CIDEr).
- With 10 captions on MSRVTT, SCG-SP-Prefix reaches D-2 54.0 and s-C 70.3 versus STR's D-2 58.4 (STR higher on D-2) and s-C not reported for STR.
- On VATEX, SCG-SP-Prefix achieves CIDEr 61.2 and D-1 24.4 with the best overall relevance-diversity balance.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SCG-SP-LSTM instead of STR | diverse video captioning · MSVD, top-1 over 20 sentences | CIDEr: 115.2 -> 120.2 | Helped |
| Use SCG-SP-LSTM instead of STR | diverse video captioning · MSRVTT, top-1 over 20 sentences | ROUGE-L: 71.0 -> 72.4 | Helped |
| Use SCG-SP-Prefix instead of STR | diverse video captioning · MSVD, 10 sentences | Div-2: 48.5 -> 52.4 | Helped |
| Use SCG-SP-Prefix instead of STR | diverse video captioning · MSRVTT, 10 sentences | Div-2: 58.4 -> 54.0 | Hurt |
| Add conceptual queries (ablation) | SCG-SP-LSTM · MSRVTT | CIDEr: 63.5 -> 64.7 | Helped |
| Add conceptual queries (ablation) | SCG-SP-Prefix · MSRVTT | CIDEr: 66.2 -> 65.9 | No clear effect |
| Add the classification head (ablation) | SCG-SP-LSTM · MSRVTT | CIDEr: 56.7 -> 64.7 | Helped |
| Add the classification head (ablation) | SCG-SP-Prefix · MSRVTT | CIDEr: 64.3 -> 65.9 | Helped |
| Add diversity regularization (ablation) | SCG-SP-LSTM · MSRVTT | self-CIDEr: 53.9 -> 62.6 | Helped |
| Add diversity regularization (ablation) | SCG-SP-Prefix · MSRVTT | self-CIDEr: 64.4 -> 70.3 | Helped |
| Fine-tune pretrained GPT-2 instead of prefix-tuning (ablation) | SCG-SP-Prefix · MSRVTT | CIDEr: 65.9 -> 66.3 | Helped |
| Use prefix-tuning instead of fine-tuning (ablation) | SCG-SP-Prefix · MSRVTT | m-BLEU: 68.3 -> 63.1 | Helped |
Surprising findings
- Removing the classification head drops self-CIDEr sharply for the LSTM version (from 53.9-based base to 19.8 on MSRVTT ablation), indicating concept supervision is crucial for diversity.
- Prefix-tuning GPT-2 with frozen weights outperforms training from scratch and approaches full fine-tuning on CIDEr while being more diverse.
Limitations
- Oracle relevance scores are used for comparison with single-caption methods, which is not a like-for-like comparison.
- Some diversity metrics are best achieved by Div-BS or STR rather than SCG-SP, and LSTM variant transfers poorly across datasets.
Experimental setup
Datasets: MSVD MSRVTT VATEX
Benchmarks: MSVD MSRVTT VATEX
Metrics: BLEU@4 METEOR ROUGE-L CIDEr oracle top-1 Div-1 Div-2 m-BLEU self-CIDEr consensus reranking
Baselines: Div-BS SeqCVAE COSCVAE DML STR VSLAN FLIP SMCG BLIP BLIP-2
Builds on: DETR PDVC GPT-2 STR Div-BS
Closest papers by meaning
- OwlCap: Harmonizing Motion-Detail for Video Captioning via HMD-270K and Caption Set Equivalence Reward (AAAI 2026 · similarity 0.87)
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