Image captioning generates written descriptions of an image. In recent image captioning research, attention regions seldom cover all objects, and generated captions may lack the details of objects and may remain far from reality. In this paper, we propose a word guided attention (WGA) method for image captioning. First, WGA extracts word information using the embedded word and memory cell by applying transformation and multiplication. Then, WGA applies word information to the attention results and obtains the attended feature vectors via elementwise multiplication. Finally, we apply WGA with the words from different time steps to obtain previous word guided attention (PW) and current word attention (CW) in the decoder. Experiments on the MSCOCO dataset show that our proposed WGA can achieve competitive performance against state-of-the-art methods, with PW results of a 39.1 Bilingual Evaluation Understudy score (BLEU-4) and a 127.6 Consensus-Based Image Description Evaluation score (CIDEr-D); and CW results of a 39.1 BLEU-4 score and a 127.2 CIDER-D score on a Karpathy test split.

Compressive light field cameras have attracted notable attention over the past few years because they can efficiently determine redundancy from light fields. However, much of the research has only concentrated on reconstructing the entire light field from compressed sampling, which ignores the possibility of directly extracting information such as depth from it. In this paper, we introduce a light field camera configuration with a random color-coded microlens array. Considering the color-coded light fields, we propose a novel attention-based encoder–decoder network. Specifically, the encoder part compresses the coded measurement into a low-dimensional representation that removes most redundancy, and the decoder part constructs the depth map directly from the latent representation. The attention mechanism enables the network to process spatial and angular features dynamically and effectively, thus significantly improving performance. Extensive experiments on synthetic and real-world datasets show that our method outperforms the state-of-the-art light field depth estimation method designed for non-coded light fields. To our knowledge, this is the first study that combines the color-coded light field with the attention-based deep learning approach, which provides a crucial insight into the design of enhanced light field photography systems.