Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space (1711.07068v1)

Abstract: This paper explores image caption generation using conditional variational auto-encoders (CVAEs). Standard CVAEs with a fixed Gaussian prior yield descriptions with too little variability. Instead, we propose two models that explicitly structure the latent space around $K$ components corresponding to different types of image content, and combine components to create priors for images that contain multiple types of content simultaneously (e.g., several kinds of objects). Our first model uses a Gaussian Mixture model (GMM) prior, while the second one defines a novel Additive Gaussian (AG) prior that linearly combines component means. We show that both models produce captions that are more diverse and more accurate than a strong LSTM baseline or a "vanilla" CVAE with a fixed Gaussian prior, with AG-CVAE showing particular promise.

Overview of AG-CVAE for Diverse Image Captioning

The paper "Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space" illustrates a novel approach to tackling the challenges of image captioning through the use of Conditional Variational Auto-Encoders (CVAE). Specifically, the paper introduces an Additive Gaussian CVAE (AG-CVAE) which enhances the diversity and accuracy of generated captions by employing a data-dependent Gaussian prior. This method is evaluated on the MSCOCO dataset, demonstrating superior performance in generating diverse and precise image descriptions compared to conventional methods like LSTM baselines and other CVAE variants.

Methodology

Conventional image captioning tasks using Long-Short Term Memory (LSTM) networks tend to produce generic captions with limited diversity. The key advancement proposed in this paper is the adoption of CVAEs, with a special focus on varying the encoding space to accommodate the semantic depth and variability inherent in images.

1. Additive Gaussian Encoding Space: The authors propose enhancing the encoding space of the CVAE by using an additive Gaussian prior. Unlike the common fixed Gaussian prior, the method leverages a data-dependent Gaussian mixture model to provide a richer representation of image content. This approach allows the CVAE to generate captions that reflect multiple semantic aspects by linearly combining Gaussian priors associated with the detected objects in the image.
2. Sampled Representation: The AG-CVAE generates multiple captions for a given image by drawing samples from this enriched, multi-modal latent space. This results in a broader range of caption candidates which better capture the complexity and ambiguity of image interpretation.
3. Training and Evaluation: The model is trained using standard stochastic gradient descent methods, with a particular focus on optimizing the KL divergence term in the CVAE formulation to maintain a balance between the learned latent space and the prior distribution. The evaluation on the MSCOCO dataset shows that the AG-CVAE model notably outperforms standard models in both diversity and accuracy of captions.

Results

The paper reports several metrics to evaluate the effectiveness of the proposed AG-CVAE approach:

• Diversity and Accuracy: Numerical results demonstrate the AG-CVAE's ability to generate more diverse caption candidates compared to both LSTM and vanilla CVAE baselines. This is achieved without compromising accuracy, as evidenced by higher scoring on standard metrics such as BLEU, METEOR, and CIDEr.
• Upper-Bound Performance: Using oracle-based evaluations, AG-CVAE's diversity is highlighted as a distinct advantage, offering better performance bounds under optimal caption selection scenarios.

Implications and Future Directions

The implications of this research are twofold. Practically, it offers a sophisticated mechanism to improve the richness and reliability of automatic image captioning systems. Theory-wise, it invigorates the exploration of generative models with enhanced latent spaces that can model conditional diversity effectively.

Future research may explore unsupervised generation of semantic clusters, extending the AG-CVAE's applicability across various generative tasks such as question generation or more complex scene understanding applications. Additionally, enhancing the re-ranking strategies to leverage the full potential of generated diverse captions remains an area ripe for refinement.