Frame- and Segment-Level Features and Candidate Pool Evaluation for Video Caption Generation

We present our submission to the Microsoft Video to Language Challenge of generating short captions describing videos in the challenge dataset. Our model is based on the encoder--decoder pipeline, popular in image and video captioning systems. We propose to utilize two different kinds of video features, one to capture the video content in terms of objects and attributes, and the other to capture the motion and action information. Using these diverse features we train models specializing in two separate input sub-domains. We then train an evaluator model which is used to pick the best caption from the pool of candidates generated by these domain expert models. We argue that this approach is better suited for the current video captioning task, compared to using a single model, due to the diversity in the dataset. Efficacy of our method is proven by the fact that it was rated best in MSR Video to Language Challenge, as per human evaluation. Additionally, we were ranked second in the automatic evaluation metrics based table

Move Forward and Tell: A Progressive Generator of Video Descriptions

We present an efficient framework that can generate a coherent paragraph to describe a given video. Previous works on video captioning usually focus on video clips. They typically treat an entire video as a whole and generate the caption conditioned on a single embedding. On the contrary, we consider videos with rich temporal structures and aim to generate paragraph descriptions that can preserve the story flow while being coherent and concise. Towards this goal, we propose a new approach, which produces a descriptive paragraph by assembling temporally localized descriptions. Given a video, it selects a sequence of distinctive clips and generates sentences thereon in a coherent manner. Particularly, the selection of clips and the production of sentences are done jointly and progressively driven by a recurrent network -- what to describe next depends on what have been said before. Here, the recurrent network is learned via self-critical sequence training with both sentence-level and paragraph-level rewards. On the ActivityNet Captions dataset, our method demonstrated the capability of generating high-quality paragraph descriptions for videos. Compared to those by other methods, the descriptions produced by our method are often more relevant, more coherent, and more concise.Comment: Accepted by ECCV 201

Video Captioning via Hierarchical Reinforcement Learning

Video captioning is the task of automatically generating a textual description of the actions in a video. Although previous work (e.g. sequence-to-sequence model) has shown promising results in abstracting a coarse description of a short video, it is still very challenging to caption a video containing multiple fine-grained actions with a detailed description. This paper aims to address the challenge by proposing a novel hierarchical reinforcement learning framework for video captioning, where a high-level Manager module learns to design sub-goals and a low-level Worker module recognizes the primitive actions to fulfill the sub-goal. With this compositional framework to reinforce video captioning at different levels, our approach significantly outperforms all the baseline methods on a newly introduced large-scale dataset for fine-grained video captioning. Furthermore, our non-ensemble model has already achieved the state-of-the-art results on the widely-used MSR-VTT dataset.Comment: CVPR 2018, with supplementary materia

Natural Language Description of Images and Videos

Understanding visual media, i.e. images and videos, has been a cornerstone topic in computer vision research for a long time. Recently, a new task within the purview of this research area, that of automatically captioning images and videos, has garnered wide-spread interest. The task involves generating a short natural language description of an image or a video. This thesis studies the automatic visual captioning problem in its entirety. A baseline visual captioning pipeline is examined, including its two constituent blocks, namely visual feature extraction and language modeling. We then discuss the challenges involved and the methods available to evaluate a visual captioning system. Building on this baseline model, several enhancements are proposed to improve the performance of both the visual feature extraction and the language modeling. Deep convolutional neural network based image features used in the baseline model are augmented with explicit object and scene detection features. In the case of videos, a combination of action recognition and static frame-level features are used. The long-short term memory network based language model used in the baseline is extended by introduction of an additional input channel and residual connections. Finally, an efficient ensembling technique based on a caption evaluator network is presented. Results from extensive experiments conducted to evaluate each of the above mentioned enhancements are reported. The image and video captioning architectures proposed in this thesis achieve state-of-the-art performance on the corresponding tasks. To support these claims, results from two video captioning challenges organized over the last year are reported, both of which were won by the models presented in the thesis. We also quantitatively analyze the automatic captions generated and identify several shortcomings of the current system. After having identified the deficiencies, we briefly look at a few interesting problems which could take the automatic visual captioning research forward