Source-linked AI summary
Recurrent Topic-Transition GAN for Visual Paragraph Generation
Xiaodan Liang, Zhiting Hu, Hao Zhang, Chuang Gan, Eric P. Xing
TL;DR
Visual paragraph generation is limited by small, biased paragraph datasets and the need to capture fine-grained semantics while maintaining coherence. RTT-GAN addresses this with semi-supervised adversarial generation using region-based visual and language attention and discriminators for sentence plausibility and topic-transition coherence. It is effective in supervised and semi-supervised settings, with synthesized paragraphs also supporting personalized, diverse descriptions.
Problem
Existing visual description systems rely on small, biased paragraph datasets and must capture richer semantic content while maintaining coherent long-term visual and language reasoning.
Method
RTT-GAN uses a structured paragraph generator with recurrent region-based visual and language attention, trained adversarially against sentence-plausibility and topic-transition discriminators.
Results
RTT-GAN is effective in both fully supervised and semi-supervised settings, while synthesized paragraphs enable diverse personalized descriptions and improve paragraph generation when used for augmentation.
Takeaways & Limitations
Adversarial multi-level assessment supports realistic paragraphs with coherent topic transitions, and the model can generate diverse descriptions from limited sentence-level annotations.
Abstract
from arXiv · showhide
A natural image usually conveys rich semantic content and can be viewed from different angles. Existing image description methods are largely restricted by small sets of biased visual paragraph annotations, and fail to cover rich underlying semantics. In this paper, we investigate a semi-supervised paragraph generative framework that is able to synthesize diverse and semantically coherent paragraph descriptions by reasoning over local semantic regions and exploiting linguistic knowledge. The proposed Recurrent Topic-Transition Generative Adversarial Network (RTT-GAN) builds an adversarial framework between a structured paragraph generator and multi-level paragraph discriminators. The paragraph generator generates sentences recurrently by incorporating region-based visual and language attention mechanisms at each step. The quality of generated paragraph sentences is assessed by multi-level adversarial discriminators from two aspects, namely, plausibility at sentence level and topic-transition coherence at paragraph level. The joint adversarial training of RTT-GAN drives the model to generate realistic paragraphs with smooth logical transition between sentence topics. Extensive quantitative experiments on image and video paragraph datasets demonstrate the effectiveness of our RTT-GAN in both supervised and semi-supervised settings. Qualitative results on telling diverse stories for an image also verify the interpretability of RTT-GAN.
1. Introduction
Visual paragraph generation addresses the limits of single-sentence descriptions by producing detailed, coherent accounts while handling diverse viewpoints, limited annotations, and long-term reasoning. RTT-GAN uses semi-supervised adversarial generation to create diverse paragraphs grounded in local regions and paragraph context.
- Single-sentence captioning often captures scene-level gist rather than fine-grained entities, limiting applications such as blind navigation, video retrieval, and automatic subtitling.
- Paragraph generation must support diverse personalized descriptions, but deterministic optimization over single annotations can lose information expressed in the image.The paper motivates simple manipulations that enable diverse generation from different viewpoints and objects.
- Small image-paragraph datasets and the need for fine-grained semantic coverage make long-term visual and language reasoning necessary for coherent paragraphs.
- RTT-GAN combines a structured paragraph generator with multi-level discriminators in a semi-supervised adversarial framework for diverse, semantically coherent generation.The generator reasons over local semantic regions and global paragraph context.
- The generator uses dense semantic regions, recurrent paragraph-, sentence-, and word-level states, plus spatial visual and language attention to construct paragraph content.
- Experiments compare RTT-GAN on image- and video-paragraph datasets in supervised and semi-supervised settings, while qualitative results demonstrate personalized generation flexibility.Using only single-sentence COCO annotations, the model generates plausible multi-sentence paragraphs that can enlarge the paragraph dataset.
2. Related Work
Visual paragraph generation extends captioning toward coherent, fine-grained descriptions spanning multiple sentences. Prior work commonly uses hierarchical recurrent networks to model long-term linguistic structure.
- Visual Captioning: Visual captioning has shifted from ranking and template retrieval toward direct generation because language is compositional and difficult to enumerate exhaustively.
- Visual Paragraph Generation: Visual paragraph generation addresses shortcomings of standard and dense captioning by producing coherent, fine-grained natural-language descriptions.
- Visual Paragraph Generation: Hierarchical recurrent networks are widely used to model the hierarchy of language across multiple sentences.
- Visual Paragraph Generation: Prior video-description work models strong temporal dependencies in multi-sentence cooking-video descriptions.
3. Recurrent Topic-Transition GAN
RTT-GAN combines a structured recurrent paragraph generator with adversarial sentence- and paragraph-level discriminators. It uses region-aware visual and language reasoning, recurrent context, and semi-supervised objectives to generate realistic, coherent paragraphs.
- Adversarial Framework: RTT-GAN establishes an adversarial game between a paragraph generator and sentence and topic-transition discriminators.The discriminators assess generated text against linguistic characteristics of real paragraph descriptions.
- Paragraph Generator: The generator recurrently produces one sentence at a time from local semantic regions, preceding sentences, and paragraph context.Each sentence consists of a sequence of words generated by a word-level recurrent network.
- Paragraph Generator: Language attention incorporates regional text phrases during word prediction, complementing visual attention over region features.The architecture therefore uses both visual and language cues at different recurrent levels.
- Training Objective: Reconstruction loss is used for paragraph-annotated supervised examples and single-sentence semi-supervised examples, with T = 1 in the latter case.Monte Carlo rollouts address the difficulty of back-propagating through discrete text samples during adversarial training.
- Paragraph Generator: A paragraph RNN summarizes preceding sentences, while sentence-level visual attention selects relevant semantic regions to construct the next sentence topic.The sentence RNN encodes the attended topic before word generation.
- Paragraph Discriminators: The sentence discriminator scores individual-sentence plausibility, whereas the topic-transition discriminator evaluates coherence across sentences generated so far.Both discriminators are recurrent models operating at different linguistic granularities.
4. Experiments
Experiments evaluate RTT-GAN on image and video paragraph generation, including supervised, semi-supervised, ablation, attention, and personalization settings. The model outperforms baselines, benefits from adversarial and attention components, and generates diverse topic-coherent paragraphs.
- Comparison with the State-of-the-arts: RTT-GAN significantly outperforms all baselines on every reported metric in the fully supervised setting, including CIDEr gains over Region-Hierarchical and Image-Flat.The reported CIDEr improvements are 3.35% over Region-Hierarchical and 5.81% over Image-Flat.
- Comparison with the State-of-the-arts: Using only single-sentence MS COCO annotations, RTT-GAN (Semi-) achieves comparable performance with fully supervised Region-Hierarchical without image-paragraph pairs.Adversarial discriminators trained on standalone paragraph data impose linguistic characteristics on later sentence predictions.
- Comparison with the State-of-the-arts: Augmenting the image-paragraph dataset with RTT-GAN (Semi-) synthetic paragraphs yields a 6.84% CIDEr increase over Region-Hierarchical.The augmented RTT-GAN (Semi+Fully) also dramatically outperforms RTT-GAN (Fully-) and other baselines.
- The Importance of Adversarial Training: Removing adversarial discriminators causes CIDEr drops of 1.80% in the fully supervised model and 4.11% in the semi-supervised model.The ablation supports the importance of sentence plausibility and topic coherence with preceding sentences for long paragraph generation.
- The Importance of Region-based Attention: Spatial and language attention improve paragraph prediction by selectively incorporating appropriate visual and language cues.The region-based visualization shows distinct semantic regions and corresponding local-phrase words receiving high attention during sentence generation.
- Personalized Paragraph Generation and Extension to Video Domain: RTT-GAN generates diverse, topic-coherent personalized paragraphs by manipulating the first sentence and significantly outperforms state-of-the-art methods on video paragraph generation.For video, the model incorporates semantic regions and motion features, demonstrating generalization to the TACoS-MultiLevel domain.
5. Conclusion and Future Work
RTT-GAN generates diverse visual paragraphs by combining adversarial modeling with recurrent visual-language reasoning. Experiments show effectiveness in fully supervised and semi-supervised settings, while future work targets broader vision tasks.
- RTT-GAN generates diverse paragraphs when trained with only first-sentence annotations.
- The generator recurrently reasons over fine-grained semantic regions using visual and language attention mechanisms.
- Two discriminators evaluate sentence plausibility and topic-transition coherence.
- Extensive experiments demonstrate effectiveness in fully supervised and semi-supervised settings.
- Future work will extend the generative model to vision tasks requiring joint visual and language modeling.