Video description is the automatic generation of natural language sentences that describe the contents of a given video. It has applications in human-robot interaction, helping the visually impaired and video subtitling. The past few years have seen a surge of research in this area due to the unprecedented success of deep learning in computer vision and natural language processing. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, calling the need for a comprehensive survey to focus research efforts in this flourishing new direction. This article fills the gap by surveying the state-of-the-art approaches with a focus on deep learning models; comparing benchmark datasets in terms of their domains, number of classes, and repository size; and identifying the pros and cons of various evaluation metrics, such as SPICE, CIDEr, ROUGE, BLEU, METEOR, and WMD. Classical video description approaches combined subject, object, and verb detection with template-based language models to generate sentences. However, the release of large datasets revealed that these methods cannot cope with the diversity in unconstrained open domain videos. Classical approaches were followed by a very short era of statistical methods that were soon replaced with deep learning, the current state-of-the-art in video description. Our survey shows that despite the fast-paced developments, video description research is still in its infancy due to the following reasons: Analysis of video description models is challenging, because it is difficult to ascertain the contributions towards accuracy or errors of the visual features and the adopted language model in the final description. Existing datasets neither contain adequate visual diversity nor complexity of linguistic structures. Finally, current evaluation metrics fall short of measuring the agreement between machine-generated descriptions with that of humans. We conclude our survey by listing promising future research directions.

Adversarial susceptibility of neural image captioning is still under-explored due to the complex multi-model nature of the task. We introduce a GAN-based adversarial attack to effectively fool encoder-decoder based image captioning frameworks. Unique to our attack is the systematic disruption of the internal representation of an image at the encoder stage which allows control over the captions generated at the decoder stage. We cause the desired disruption with an input perturbation that promotes similarity between the features of the input image with a target image of our choice. The target image provides a convenient handle to control the incorrect captions in our method. We do not assume any knowledge of the decoder module, which makes our attack ‘gray-box’. Moreover, our attack remains agnostic to the type of decoder module, thereby proving effective for RNNs as well as Transformers as the language models. This makes our attack highly pragmatic.

Dense captioning methods generally detect events in videos first and then generate captions for the individual events. Events are localized solely based on the visual cues while ignoring the associated linguistic information and context. Whereas end-to-end learning may implicitly take guidance from language, these methods still fall short of the power of explicit modeling. In this paper, we propose a Visual-Semantic Embedding (ViSE) Framework that models the word(s)-context distributional properties over the entire semantic space and computes weights for all the n-grams such that higher weights are assigned to the more informative n-grams. The weights are accounted for in learning distributed representations of all the captions to construct a semantic space. To perform the contextualization of visual information and the constructed semantic space in a supervised manner, we design Visual-Semantic Joint Modeling Network (VSJM-Net). The learned ViSE embeddings are then temporally encoded with a Hierarchical Descriptor Transformer (HDT). For caption generation, we exploit a transformer architecture to decode the input embeddings into natural language descriptions. Experiments on the large-scale ActivityNet Captions dataset and YouCook-II dataset demonstrate the efficacy of our method.

Dense video captioning requires localization and description of multiple events in long videos. Prior works detect events in videos solely relying on the visual content and completely ignore the semantics (captions) related to the events. This is undesirable because human-provided captions often also describe events that are visually nonpresent or subtle to detect. In this research, we propose to capitalize on this natural kinship between events and their human-provided descriptions. We propose a semantic contextualization network to encode the visual content of videos by representing it in a semantic space. The representation is further refined to incorporate temporal information and transformed into event descriptors using a hierarchical application of short Fourier transform. Our proposal network exploits the fusion of semantic and visual content enabling it to generate semantically meaningful event proposals. For each proposed event, we attentively fuse its hidden state and descriptors to compute discriminative representation for the subsequent captioning network. Thorough experiments on the standard large-scale ActivityNet Captions dataset and additionally on the YouCook-II dataset show that our method achieves competitive or better performance on multiple popular metrics for the problem.