Summary
IcoCap changes the content density seen by a video captioner: Image-Video Compounding Strategy (ICS) injects concise image semantics into video samples, while Visual-Semantic Guided Captioning (VGC) adapts caption supervision to the resulting ambiguous compound content.
Research paths
How this paper contributes to the site's broader research map.
- Video Generation and World ModelsFoundation
IcoCap establishes an early supervision lesson for video-language learning: when visual content is compounded, the target caption must be reconsidered as part of the same operation.
- Trustworthy Visual Generation Post-TrainingFoundation
IcoCap shows that modifying visual inputs requires corresponding changes to text supervision, an early instance of treating target construction as part of the learning system.
Research question
Video captioners must learn from redundant visual streams whose content does not align cleanly with a single ground-truth sentence; improving only the captioner architecture can leave this content-density problem unaddressed.
What the paper contributes
- Introduces Image-Video Compounding Strategy (ICS) to combine easy-to-learn image semantics with video content.
- Uses the compounded samples to make captioners identify useful video cues despite added concise image semantics.
- Introduces Visual-Semantic Guided Captioning (VGC) to mitigate caption–visual mismatch in ambiguous samples.
Evidence and evaluation scope
Experiments on MSVD, MSR-VTT, and VATEX report competitive or superior captioning results. Exact metrics, backbones, and ablations should be cited from the IEEE article.
Scope and limitations
The method assumes access to suitable image semantics and caption supervision, and its reported evidence is on three established captioning datasets. It should not be summarized merely as generic noise injection or as treating all images as distractions.
Positioning for related work
IcoCap is a data- and supervision-design method for video captioning. Its distinctive pair is ICS for content compounding and VGC for adapting caption learning to the compounded visual semantics.
Related-work context
Liang et al. propose IcoCap, which combines an Image-Video Compounding Strategy with Visual-Semantic Guided Captioning to train video captioners against redundant and ambiguous visual content.
A concise, neutral description of how this paper can be situated in related work.
Official abstract
Video captioning is a more challenging task compared to image captioning, primarily due to differences in content density. Video data contains redundant visual content, making it difficult for captioners to generalize diverse content and avoid being misled by irrelevant elements. Moreover, redundant content is not well-trimmed to match the corresponding visual semantics in the ground truth, further increasing the difficulty of video captioning. Current research in video captioning predominantly focuses on captioner design, neglecting the impact of content density on captioner performance. Considering the differences between videos and images, there exists an another line to improve video captioning by leveraging concise and easily-learned image samples to further diversify video samples. This modification to content density compels the captioner to learn more effectively against redundancy and ambiguity. In this paper, we propose a novel approach called Image-Compounded learning for video Captioners (IcoCap) to facilitate better learning of complex video semantics. IcoCap comprises two components: the Image-Video Compounding Strategy (ICS) and Visual-Semantic Guided Captioning (VGC). ICS compounds easily-learned image semantics into video semantics, further diversifying video content and prompting the network to generalize contents in a more diverse sample. Besides, learning with the sample compounded with image contents, the captioner is compelled to better extract valuable video cues in the presence of straightforward image semantics. This helps the captioner further focus on relevant information while filtering out extraneous content. Then, VGC guides the network in flexibly learning ground truth captions based on the compounded samples, helping to mitigate the mismatch between the ground truth and ambiguous semantics in video samples. Our experimental results demonstrate the effectiveness of IcoCap in improving the learning of video captioners. Applied to the widely-used MSVD, MSR-VTT, and VATEX datasets, our approach achieves competitive or superior results compared to state-of-the-art methods, illustrating its capacity to handle redundant and ambiguous video data.
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Evidence references
| What to verify | Location in the paper |
|---|---|
| Problem statement | Abstract |
| Method and contributions | Abstract; Image-Video Compounding Strategy and Visual-Semantic Guided Captioning sections |
| Evaluation statement | Abstract; MSVD, MSR-VTT, and VATEX experiments |
Primary source: IEEE version of record (IEEE early access 2023-10-05; volume 26, 2024).
How to cite
Cite the paper—not this explainer—for scientific claims. Cite this page only when reusing its original commentary.
Yuanzhi Liang, Linchao Zhu, Xiaohan Wang, and Yi Yang. “IcoCap: Improving Video Captioning by Compounding Images.” IEEE Transactions on Multimedia (2024), 26, 4389-4400. https://doi.org/10.1109/TMM.2023.3322329.
Reuse policy
Original explanatory text on this page is licensed under CC BY 4.0 with attribution and a link to this page. Paper title, abstract, figures, and bibliographic metadata are excluded and retain their original rights. Creative Commons Attribution 4.0 International.