Learning to Generate Grounded Visual Captions without Localization Supervision (1906.00283v3)

Abstract: When automatically generating a sentence description for an image or video, it often remains unclear how well the generated caption is grounded, that is whether the model uses the correct image regions to output particular words, or if the model is hallucinating based on priors in the dataset and/or the LLM. The most common way of relating image regions with words in caption models is through an attention mechanism over the regions that are used as input to predict the next word. The model must therefore learn to predict the attentional weights without knowing the word it should localize. This is difficult to train without grounding supervision since recurrent models can propagate past information and there is no explicit signal to force the captioning model to properly ground the individual decoded words. In this work, we help the model to achieve this via a novel cyclical training regimen that forces the model to localize each word in the image after the sentence decoder generates it, and then reconstruct the sentence from the localized image region(s) to match the ground-truth. Our proposed framework only requires learning one extra fully-connected layer (the localizer), a layer that can be removed at test time. We show that our model significantly improves grounding accuracy without relying on grounding supervision or introducing extra computation during inference, for both image and video captioning tasks. Code is available at https://github.com/chihyaoma/cyclical-visual-captioning .

An Overview of "Learning to Generate Grounded Visual Captions without Localization Supervision"

The paper "Learning to Generate Grounded Visual Captions without Localization Supervision" presents a novel methodology for generating visually grounded captions for images and videos without relying on explicit grounding annotations. Grounding in this context refers to the model's ability to associate generated words with specific regions within the visual data. This paper introduces a cyclical training regimen that seeks to enhance grounding performance, leveraging self-supervised learning strategies, without incurring additional computational costs during inference.

Core Contributions

The primary contribution of this research is the cyclical training regimen designed to train visual captioning models. This cyclical approach is segmented into three stages: decoding, localization, and reconstruction. During decoding, a LLM generates words sequentially by attending to the regions within the input visual data. In the localization stage, the generated words serve as inputs to a localizer that identifies relevant image regions. Finally, reconstruction utilizes these regions to regenerate the sentence, ensuring consistency with the ground-truth.

The cyclical training strategy involves:

1. Decoding: Generating words using a state-of-the-art captioning model that leverages contextual information via attention mechanisms.
2. Localization: Mapping each generated word to specific image regions using a lightweight localizer, instantiated as a linear layer for simplicity.
3. Reconstruction: Ensuring the decoder reconstructs captions from localized regions accurately, iteratively refining the grounding quality by sharing parameters between decoding and reconstruction.

Experimental Design and Results

The methodology is evaluated on two datasets: Flickr30k Entities and ActivityNet-Entities, chosen for the challenges they present in terms of visual grounding and the diversity of visual inputs. The model's performance is compared to state-of-the-art techniques, both in terms of conventional captioning metrics—BLEU, METEOR, CIDEr, and SPICE—and grounding precision metrics such as F1-scores calculated on a per-object and per-sentence basis.

Quantitatively, the proposed cyclical framework achieves a significant enhancement in grounding accuracy, observing relative improvements of approximately 18% on average in the Flickr30k Entities dataset and 34% in the ActivityNet-Entities dataset when compared to unsupervised baselines. These enhancements are particularly notable for infrequent words, demonstrating the advantages afforded by the model's self-supervised nature.

Theoretical Implications and Future Directions

One of the intriguing theoretical implications of this research is how cyclical training, without any direct grounding supervision, can efficiently utilize self-supervised signals to improve the granularity of attention mechanisms. The paper illustrates that by backpropagating the reconstruction errors, the model inherently refines its understanding of word-region associations over successive iterations.

For future explorations, this unsupervised grounding technique could be integrated with other language-vision tasks, such as visual question answering, where cross-modal grounding could further elucidate the relations between object-centric knowledge and linguistic elements. Furthermore, exploring variants of the localizer—possibly integrating richer non-linear transformations—could yield further enhancements in grounding precision.

Conclusion

The presented research advances the field of visual captioning by demonstrating the efficacy of cyclical training methods in strengthening grounding mechanisms, absent any localization supervision. It lays a foundation for developing more interpretable and robust captioning systems that operate efficiently across diverse visual inputs while maintaining high precision in word-region grounding. This work not only broadens the understanding of unsupervised learning methodologies within AI but also provides a pathway toward more scalable and generalized approaches for vision-language tasks.