Action classification in still images has been a popular research topic in computer vision. Labelling large scale datasets for action classification requires tremendous manual work, which is hard to scale up. Besides, the action categories in such datasets are pre-defined and vocabularies are fixed. However humans may describe the same action with different phrases, which leads to the difficulty of vocabulary expansion for traditional fully-supervised methods. We observe that large amounts of images with sentence descriptions are readily available on the Internet. The sentence descriptions can be regarded as weak labels for the images, which contain rich information and could be used to learn flexible expressions of action categories. We propose a method to learn an Action Concept Tree (ACT) and an Action Semantic Alignment (ASA) model for classification from image-description data via a two-stage learning process. A new dataset for the task of learning actions from descriptions is built. Experimental results show that our method outperforms several baseline methods significantly.
@cite_30: With nearly one billion online videos viewed everyday, an emerging new frontier in computer vision research is recognition and search in video. While much effort has been devoted to the collection and annotation of large scalable static image datasets containing thousands of image categories, human action datasets lag far behind. Current action recognition databases contain on the order of ten different action categories collected under fairly controlled conditions. State-of-the-art performance on these datasets is now near ceiling and thus there is a need for the design and creation of new benchmarks. To address this issue we collected the largest action video database to-date with 51 action categories, which in total contain around 7,000 manually annotated clips extracted from a variety of sources ranging from digitized movies to YouTube. We use this database to evaluate the performance of two representative computer vision systems for action recognition and explore the robustness of these methods under various conditions such as camera motion, viewpoint, video quality and occlusion.
@cite_7: Humans connect language and vision to perceive the world. How to build a similar connection for computers? One possible way is via visual concepts, which are text terms that relate to visually discriminative entities. We propose an automatic visual concept discovery algorithm using parallel text and visual corpora, it filters text terms based on the visual discriminative power of the associated images, and groups them into concepts using visual and semantic similarities. We illustrate the applications of the discovered concepts using bidirectional image and sentence retrieval task and image tagging task, and show that the discovered concepts not only outperform several large sets of manually selected concepts significantly, but also achieves the state-of-the-art performance in the retrieval task.
@cite_28: Online instructional videos have become a popular way for people to learn new skills encompassing art, cooking and sports. As watching instructional videos is a natural way for humans to learn, analogously, machines can also gain knowledge from these videos. We propose to utilize the large amount of instructional videos available online to harvest examples of various actions in an unsupervised fashion. The key observation is that in instructional videos, the instructor's action is highly correlated with the instructor's narration. By leveraging this correlation, we can exploit the timing of action corresponding terms in the speech transcript to temporally localize actions in the video and harvest action examples. The proposed method is scalable as it requires no human intervention. Experiments show that the examples harvested are of reasonably good quality, and action detectors trained on data collected by our unsupervised method yields comparable performance with detectors trained with manually collected data on the TRECVID Multimedia Event Detection task.
@cite_29: Human action and role recognition play an important part in complex event understanding. State-of-the-art methods learn action and role models from detailed spatio temporal annotations, which requires extensive human effort. In this work, we propose a method to learn such models based on natural language descriptions of the training videos, which are easier to collect and scale with the number of actions and roles. There are two challenges with using this form of weak supervision: First, these descriptions only provide a high-level summary and often do not directly mention the actions and roles occurring in a video. Second, natural language descriptions do not provide spatio temporal annotations of actions and roles. To tackle these challenges, we introduce a topic-based semantic relatedness (SR) measure between a video description and an action and role label, and incorporate it into a posterior regularization objective. Our event recognition system based on these action and role models matches the state-of-the-art method on the TRECVID-MED11 event kit, despite weaker supervision.
@cite_32: Descriptive video service (DVS) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their peers. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed DVS, which is temporally aligned to full length HD movies. In addition we also collected the aligned movie scripts which have been used in prior work and compare the two different sources of descriptions. In total the Movie Description dataset contains a parallel corpus of over 54,000 sentences and video snippets from 72 HD movies. We characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing DVS to scripts, we find that DVS is far more visual and describes precisely what is shown rather than what should happen according to the scripts created prior to movie production.
@cite_3: In this work, we introduce a dataset of video annotated with high quality natural language phrases describing the visual content in a given segment of time. Our dataset is based on the Descriptive Video Service (DVS) that is now encoded on many digital media products such as DVDs. DVS is an audio narration describing the visual elements and actions in a movie for the visually impaired. It is temporally aligned with the movie and mixed with the original movie soundtrack. We describe an automatic DVS segmentation and alignment method for movies, that enables us to scale up the collection of a DVS-derived dataset with minimal human intervention. Using this method, we have collected the largest DVS-derived dataset for video description of which we are aware. Our dataset currently includes over 84.6 hours of paired video sentences from 92 DVDs and is growing.
@cite_6: Action recognition on large categories of unconstrained videos taken from the web is a very challenging problem compared to datasets like KTH (6 actions), IXMAS (13 actions), and Weizmann (10 actions). Challenges like camera motion, different viewpoints, large interclass variations, cluttered background, occlusions, bad illumination conditions, and poor quality of web videos cause the majority of the state-of-the-art action recognition approaches to fail. Also, an increased number of categories and the inclusion of actions with high confusion add to the challenges. In this paper, we propose using the scene context information obtained from moving and stationary pixels in the key frames, in conjunction with motion features, to solve the action recognition problem on a large (50 actions) dataset with videos from the web. We perform a combination of early and late fusion on multiple features to handle the very large number of categories. We demonstrate that scene context is a very important feature to perform action recognition on very large datasets. The proposed method does not require any kind of video stabilization, person detection, or tracking and pruning of features. Our approach gives good performance on a large number of action categories; it has been tested on the UCF50 dataset with 50 action categories, which is an extension of the UCF YouTube Action (UCF11) dataset containing 11 action categories. We also tested our approach on the KTH and HMDB51 datasets for comparison.
@cite_11: Automatic assistants could guide a person or a robot in performing new tasks, such as changing a car tire or repotting a plant. Creating such assistants, however, is non-trivial and requires understanding of visual and verbal content of a video. Towards this goal, we here address the problem of automatically learning the main steps of a task from a set of narrated instruction videos. We develop a new unsupervised learning approach that takes advantage of the complementary nature of the input video and the associated narration. The method sequentially clusters textual and visual representations of a task, where the two clustering problems are linked by joint constraints to obtain a single coherent sequence of steps in both modalities. To evaluate our method, we collect and annotate a new challenging dataset of real-world instruction videos from the Internet. The dataset contains videos for five different tasks with complex interactions between people and objects, captured in a variety of indoor and outdoor settings. We experimentally demonstrate that the proposed method can automatically discover, learn and localize the main steps of a task in input videos.
Weakly supervised action concept learning relies on weakly-labelled data, such as video-caption stream data @cite_32 @cite_3 and focuses on automatically discovering and learning action concepts. @cite_11 designs a method to automatically discover the main steps for specific tasks, such as make coffee" and change tire", from narrated instructional videos. Their method solves two clustering problems, one in text and one in video, applied one after each other and linked by joint constraints to obtain a single coherent sequence of steps in both modalities. Ramanathan @cite_29 propose a method to learn action and role recognition models based on natural language descriptions of the training videos. Yu @cite_28 discover Verb-Object (VO) pairs from the captions of the instructional videos and use the associated video clips as training samples. The learned classifiers are evaluated in event classification, compared with well defined action categories in HMDB51 @cite_30 and UCF50 @cite_6 . @cite_7 proposes a general concept discovery method from image-sentence corpora and apply the concepts on image-sentence retrieval tasks.