Bidirectional Captioning for Clinically Accurate and Interpretable Models

Vision-language pretraining has been shown to produce high-quality visual encoders which transfer efficiently to downstream computer vision tasks. While generative language models have gained widespread attention, image captioning has thus far been mostly overlooked as a form of cross-modal pretraining in favor of contrastive learning, especially in medical image analysis. In this paper, we experiment with bidirectional captioning of radiology reports as a form of pretraining and compare the quality and utility of learned embeddings with those from contrastive pretraining methods. We optimize a CNN encoder, transformer decoder architecture named RadTex for the radiology domain. Results show that not only does captioning pretraining yield visual encoders that are competitive with contrastive pretraining (CheXpert competition multi-label AUC of 89.4%), but also that our transformer decoder is capable of generating clinically relevant reports (captioning macro-F1 score of 0.349 using CheXpert labeler) and responding to prompts with targeted, interactive outputs.Comment: 12 pages, 7 figures. Code release to follo

Excitation Backprop for RNNs

Deep models are state-of-the-art for many vision tasks including video action recognition and video captioning. Models are trained to caption or classify activity in videos, but little is known about the evidence used to make such decisions. Grounding decisions made by deep networks has been studied in spatial visual content, giving more insight into model predictions for images. However, such studies are relatively lacking for models of spatiotemporal visual content - videos. In this work, we devise a formulation that simultaneously grounds evidence in space and time, in a single pass, using top-down saliency. We visualize the spatiotemporal cues that contribute to a deep model's classification/captioning output using the model's internal representation. Based on these spatiotemporal cues, we are able to localize segments within a video that correspond with a specific action, or phrase from a caption, without explicitly optimizing/training for these tasks.Comment: CVPR 2018 Camera Ready Versio

Reasoning About Pragmatics with Neural Listeners and Speakers

We present a model for pragmatically describing scenes, in which contrastive behavior results from a combination of inference-driven pragmatics and learned semantics. Like previous learned approaches to language generation, our model uses a simple feature-driven architecture (here a pair of neural "listener" and "speaker" models) to ground language in the world. Like inference-driven approaches to pragmatics, our model actively reasons about listener behavior when selecting utterances. For training, our approach requires only ordinary captions, annotated _without_ demonstration of the pragmatic behavior the model ultimately exhibits. In human evaluations on a referring expression game, our approach succeeds 81% of the time, compared to a 69% success rate using existing techniques

Distinctive-attribute Extraction for Image Captioning

Image captioning, an open research issue, has been evolved with the progress of deep neural networks. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are employed to compute image features and generate natural language descriptions in the research. In previous works, a caption involving semantic description can be generated by applying additional information into the RNNs. In this approach, we propose a distinctive-attribute extraction (DaE) which explicitly encourages significant meanings to generate an accurate caption describing the overall meaning of the image with their unique situation. Specifically, the captions of training images are analyzed by term frequency-inverse document frequency (TF-IDF), and the analyzed semantic information is trained to extract distinctive-attributes for inferring captions. The proposed scheme is evaluated on a challenge data, and it improves an objective performance while describing images in more detail.Comment: 14 main pages, 4 supplementary page

Transform, Contrast and Tell: Coherent Entity-Aware Multi-Image Captioning

Coherent entity-aware multi-image captioning aims to generate coherent captions for neighboring images in a news document. There are coherence relationships among neighboring images because they often describe same entities or events. These relationships are important for entity-aware multi-image captioning, but are neglected in entity-aware single-image captioning. Most existing work focuses on single-image captioning, while multi-image captioning has not been explored before. Hence, this paper proposes a coherent entity-aware multi-image captioning model by making use of coherence relationships. The model consists of a Transformer-based caption generation model and two types of contrastive learning-based coherence mechanisms. The generation model generates the caption by paying attention to the image and the accompanying text. The caption-caption coherence mechanism aims to render entities in the caption of the image be also in captions of neighboring images. The caption-image-text coherence mechanism aims to render entities in the caption of the image be also in the accompanying text. To evaluate coherence between captions, two coherence evaluation metrics are proposed. The new dataset DM800K is constructed that has more images per document than two existing datasets GoodNews and NYT800K, and is more suitable for multi-image captioning. Experiments on three datasets show the proposed captioning model outperforms 7 baselines according to BLUE, Rouge, METEOR, and entity precision and recall scores. Experiments also show that the generated captions are more coherent than that of baselines according to caption entity scores, caption Rouge scores, the two proposed coherence evaluation metrics, and human evaluations.Comment: 32 pages, 11 tables, 3 figure