Towards Diverse and Natural Image Descriptions via a Conditional GAN (1703.06029v3)

Abstract: Despite the substantial progress in recent years, the image captioning techniques are still far from being perfect.Sentences produced by existing methods, e.g. those based on RNNs, are often overly rigid and lacking in variability. This issue is related to a learning principle widely used in practice, that is, to maximize the likelihood of training samples. This principle encourages high resemblance to the "ground-truth" captions while suppressing other reasonable descriptions. Conventional evaluation metrics, e.g. BLEU and METEOR, also favor such restrictive methods. In this paper, we explore an alternative approach, with the aim to improve the naturalness and diversity -- two essential properties of human expression. Specifically, we propose a new framework based on Conditional Generative Adversarial Networks (CGAN), which jointly learns a generator to produce descriptions conditioned on images and an evaluator to assess how well a description fits the visual content. It is noteworthy that training a sequence generator is nontrivial. We overcome the difficulty by Policy Gradient, a strategy stemming from Reinforcement Learning, which allows the generator to receive early feedback along the way. We tested our method on two large datasets, where it performed competitively against real people in our user study and outperformed other methods on various tasks.

• The paper presents a novel CGAN framework that significantly enhances the diversity and naturalness of generated image captions.
• The model employs reinforcement learning with Policy Gradient and Monte Carlo rollouts to overcome limitations of traditional MLE-based RNN outputs.
• Experimental results indicate human evaluators prefer the CGAN-generated captions, improving semantic richness and retrieval performance.

Towards Diverse and Natural Image Descriptions via a Conditional GAN

This paper explores a novel framework for generating image captions by leveraging Conditional Generative Adversarial Networks (CGANs). Traditional image captioning methods, often based on Recurrent Neural Networks (RNNs) trained via Maximum Likelihood Estimation (MLE), tend to produce outputs that are rigid and lack variability. This research proposes an alternative approach designed to enhance the naturalness and diversity of generated descriptions, two features intrinsic to human language expression.

Methodology

The authors introduce a CGAN framework that includes a generator for producing image descriptions conditioned on input images and an evaluator that assesses the suitability of these descriptions. Unlike traditional models, which depend heavily on n-gram patterns from the training data, this framework uses Policy Gradient, a Reinforcement Learning (RL) strategy, to train the generator. This technique enables the generation of more varied captions by providing early feedback and approximating future rewards through Monte Carlo rollouts.

The framework comprises:

• Generator (G): Utilizes a CNN-derived image feature and a random vector to conditionally generate captions using an LSTM network.
• Evaluator (E): Functions as a discriminator distinguishing between human-like and machine-generated captions through a dot product scoring system.

The training objective synchronizes G and E through a minimax problem, driving G to produce indistinguishable descriptions from human-generated ones and E to accurately distinguish them.

Experiments

The framework was evaluated against state-of-the-art methods on the MSCOCO and Flickr30k datasets. Various metrics, including BLEU, METEOR, and CIDEr, were used to compare the effectiveness of the generated captions. Notably, the G-GAN model produced scores more consistent with human evaluation, unlike models trained with MLE that scored higher based on traditional metrics focusing on n-gram overlap.

Results and Findings

Key results indicate that G-GAN generated more natural and diverse descriptions compared to G-MLE. For instance, human evaluation preferred the G-GAN generated descriptions in 61% of cases. Furthermore, the experimental setup demonstrated that G-GAN facilitates retrieval tasks better by providing more semantically rich and diverse descriptions.

Despite the GN-based approach, certain challenges persist, such as accurately capturing colors and counts in generated captions, suggesting areas for potential refinement.

Implications and Future Work

This research suggests that diversifying the learning objectives in image captioning models can result in more human-like text production, advancing the potential for real-world language applications. Future developments could focus on refining reward structures and further integrating nuanced language features into the CGAN framework. Moreover, extensions to paragraph generation demonstrated the scalability of this framework, opening avenues for complex descriptive tasks.

Overall, the paper provides profound insights into the use of GANs for text generation, showcasing a significant step towards more natural and diverse automated image descriptions.