Text-Conditional Visual-Language Alignment for Video Captioning

Implicit and explicit commonsense for multi-sentence video captioning

A portion of the data in video captioning datasets are noisy and unsuitable for models to learn at early stages, e.g., there could be a generic 4-word-long caption lacking distinctive details of video content and a 19-word-long description with rare words and complex structure given the average caption length of around 9. The conventional training method, i.e., learning by random sampling indiscriminately from the whole training set, may cause data bias problems and undermine the model performance. In this work, we present a novel learning strategy, Adaptive Curriculum Learning (ACL), to alleviate the adverse effect of such problems. The main idea of our approach is to allow a model to learn from the data within its competence. Specifically, a difficulty measurement is first defined to evaluate the learning difficulty of video-caption pairs, and training data can be ranked accordingly. Then, based on the learning difficulties and model competence, an adaptive sampling approach is developed to provide suitable training subsets for video captioning models in different training stages. Notably, our proposed ACL is applicable to most existing video captioning works as it requires no modifications of the model architecture. Extensive experiments are conducted on mainstream benchmarks, i.e., MSVD and MSR-VTT datasets. The results show that both RNN-based and Transformer-based models achieve consistent performance improvements with our ACL strategy.

In order to utilize the complementarity of different level features in different regions of videos effectively, and improve the accuracy of text description in videos, we propose a video captioning method based on the spatiotemporal information and attention mechanism. First, the Faster-RCNN and VGG-16 networks are used to extract the high-level features of the interesting regions and significant targets in the video frames, respectively. Second, combing the low-level dense traj ectory features of video frames to represent the spatial appearance feature of a video. Finally, we adopt the long short-term memory network to extract the temporal association features between frames and texts. In the hidden layer of the long short-term memory, an adaptive attention mechanism is introduced to enhance the representation of the salient features for video frames, and then the text description is generated automatically. The experimental results show that our proposed method can effectively improve the accuracy of video captioning, and its performance is better than several the state-of-the-art approaches.

Video captioning is a challenging task as it needs to accurately transform visual understanding into natural language description. To date, state-of-the-art methods inadequately model global-local vision representation for sentence generation, leaving plenty of room for improvement. In this work, we approach the video captioning task from a new perspective and propose a GLR framework, namely a global-local representation granularity. Our GLR demonstrates three advantages over the prior efforts. First, we propose a simple solution, which exploits extensive vision representations from different video ranges to improve linguistic expression. Second, we devise a novel global-local encoder, which encodes different video representations including long-range, short-range and local-keyframe, to produce rich semantic vocabulary for obtaining a descriptive granularity of video contents across frames. Finally, we introduce the progressive training strategy which can effectively organize feature learning to incur optimal captioning behavior. Evaluated on the MSR-VTT and MSVD dataset, we outperform recent state-of-the-art methods including a well-tuned SA-LSTM baseline by a significant margin, with shorter training schedules. Because of its simplicity and efficacy, we hope that our GLR could serve as a strong baseline for many video understanding tasks besides video captioning. Code will be available.

The study shows how the medical marketer can understand the current market and jumpstart creativity through a deconstruction exercise. That is followed by conjoint measurement, which identifies strengths and weaknesses of the deconstructed elements. Text elements were created by deconstructing messages appearing in current websites, thus providing a competitive assessment. Visual elements were taken from appropriate stock photographs. Respondents evaluated short concepts created by systematically varying subsets of text and visual elements dealing with arthritis. On a total panel level, no elements performed very well or very poorly. Greater differentiation and patterns emerged from segmentation based upon the pattern of utility values. Four concept response segments emerged: Segment 1 (interested but not differentiating beyond basic interest), Segment 2 (seeking a healthy life), Segment 3 (seeking a social and support network), and Segment 4 (seeking to treat an actual condition). Segments 3 and 4 both showed strong responses to a variety of text and visual elements. Responses to visual elements were consistent with response patterns to text elements.

This essay sets out to show how, in some types of poster, the textual and visual elements that make up the image tend to reproduce certain ludic forms of presentation in which a clear reading by the receiver depends not only on a grasp of the play between word and image, but also between these same components within the phrase itself. The effect created is that of a rebus, a form the reading of which involves a quasi-simultaneous apprehension of the words and images which make up the word or phrase in question. The rebus, being an essentially mixed form, incorporates at least two logics of reading: the first, essentially linear, follows the horizontal direction of syntax and the successive order of letters which make up a word; the second, essentially spatial or pictorial, grasps the visual image. In the standard rebus, the operation of the first logic depends on the second, in a process in which the identification and correct pronunciation of the word for the object that the image designates provides the element needed by the word or phrase to complete its overall meaning. In the poster, the form of the rebus is often complicated by the disproportion in size between the visual and textual elements of the message: sometimes the visual element is so big that it becomes, in effect, both the background and a component of the phrase; its pervasive presence has the effect of making its translation by the reader/viewer into a word or syllable unnecessary: its very presence suffices for the overall meaning of the image to become clear. And very often, since the visual component of the rebus in the poster constitutes the principal object of the message, it succeeds in being both clarified and contextualized by the phrase in which it appears. This sort of configuration underlines the double nature of the rebus in which a visual reading — a rapid overview of the word/image nexus — is followed by a second reading which interprets the various visual and textual components of the whole to discover a further or deeper meaning. This process is further complicated by the play between these two modes of reading and the resulting effect — or illusion — of simultaneity and dynamism created. The particular challenge of rebus form is that it proposes a model of verbal/visual interaction in which components of both areas of expression are combined in the same syntax.

We consider the problem of joint modeling of videos and their corresponding textual descriptions (e.g. sentences or phrases). Our approach consists of three components: the video representation, the textual representation, and a joint model that links videos and text. Our video representation uses the state-of-the-art deep 3D ConvNet to capture the semantic information in the video. Our textual representation uses the recent advancement in learning word and sentence vectors from large text corpus. The joint model is learned to score the correct (video, text) pairs higher than the incorrect ones. We demonstrate our approach in several applications: 1) retrieving sentences given a video; 2) retrieving videos given a sentence; 3) zero-shot action recognition in videos.

Video content is multifaceted, consisting of objects, scenes, interactions or actions. The existing datasets mostly label only one of the facets for model training, resulting in the video representation that biases to only one facet depending on the training dataset. There is no study yet on how to learn a video representation from multifaceted labels, and whether multifaceted information is helpful for video representation learning. In this paper, we propose a new learning framework, MUlti-Faceted Integration (MUFI), to aggregate facets from different datasets for learning a representation that could reflect the full spectrum of video content. Technically, MUFI formulates the problem as visual-semantic embedding learning, which explicitly maps video representation into a rich semantic embedding space, and jointly optimizes video representation from two perspectives. One is to capitalize on the intra-facet supervision between each video and its own label descriptions, and the second predicts the "semantic representation" of each video from the facets of other datasets as the inter-facet supervision. Extensive experiments demonstrate that learning 3D CNN via our MUFI framework on a union of four large-scale video datasets plus two image datasets leads to superior capability of video representation. The prelearnt 3D CNN with MUFI also shows clear improvements over other approaches on several downstream video applications. More remarkably, MUFI achieves 98.1%/80.9% on UCF101/HMDB51 for action recognition and 101.5% in terms of CIDEr-D score on MSVD for video captioning.

We present an approach to effectively use millions of images with noisy annotations in conjunction with a small subset of cleanly-annotated images to learn powerful image representations. One common approach to combine clean and noisy data is to first pre-train a network using the large noisy dataset and then fine-tune with the clean dataset. We show this approach does not fully leverage the information contained in the clean set. Thus, we demonstrate how to use the clean annotations to reduce the noise in the large dataset before fine-tuning the network using both the clean set and the full set with reduced noise. The approach comprises a multi-task network that jointly learns to clean noisy annotations and to accurately classify images. We evaluate our approach on the recently released Open Images dataset, containing ~9 million images, multiple annotations per image and over 6000 unique classes. For the small clean set of annotations we use a quarter of the validation set with ~40k images. Our results demonstrate that the proposed approach clearly outperforms direct fine-tuning across all major categories of classes in the Open Image dataset. Further, our approach is particularly effective for a large number of classes with wide range of noise in annotations (20-80% false positive annotations).

In the field of educational research, the ability to present data clearly and accurately plays an important role in ensuring that research results can be interpreted correctly. However, many novice researchers still face difficulties in choosing data presentation methods that can effectively communicate their research results. This study aims to analyze the advantages and disadvantages of several data presentation methods commonly used in educational research, with a focus on how each method supports clarity, accuracy, and interpretability. Using a qualitative descriptive approach, this study examines various examples of visual and textual data presentation, including tables, graphs, and narrative descriptions, to evaluate their effectiveness in conveying clear and easily understood information. The results of the analysis show that visual representations can improve clarity and attract readers' attention, but they can also oversimplify complex data. Conversely, presenting data in textual or tabular form may maintain accuracy, but may reduce accessibility for a wider readership. This study concludes that effective data presentation requires a balanced integration of visual and textual elements, based on the principles of transparency and reader-centered communication. These findings imply that it is important to develop data literacy and presentation skills among education researchers in order to strengthen the credibility of research, encourage collaboration, and empower the academic community in the ever-changing world of education and innovation.

Video summarization and captioning condense content by selecting keyframes and generating language descriptions, integrating both visual and textual perspectives. Existing video-and-language learning models typically select multiple frames as proxies rather than analyzing all frames, which improves computational efficiency but may not adequately represent the original content without redundancy. In this paper, we propose an adaptive dual video summarization framework and demonstrate its effectiveness within the context of video captioning. Given the video frames, we extract visual representations using a video-domain fine-tuned ViT model to narrow the domain shift. The keyframes are summarized based on the frame-level scores. To minimize the number of keyframes while ensuring captioning quality, we introduce a cross-modal video summarizer that selects the most semantically consistent frames according to pseudo score labels. Furthermore, we incorporate an adaptive keyframe selector that determines the optimal number of keyframes based on the video's complexity and content, enhancing the framework's adaptability and generalization. The proposed adaptive keyframe selector enables the framework to handle diverse video content, making it more generalizable and applicable to real-world scenarios.We designed a ranking scheme to assess the video's static appearance and temporal dynamics from score-based and time-based perspectives. To conclude, we use a lightweight LSTM decoder to generate descriptions. Experimental results on the MSR-VTT, MSVD and VATEX benchmarks demonstrate that our adaptive dual video summarization framework can effectively convey the same semantic information as the original video while using a significantly reduced number of keyframes, leading to improved video captioning performance.

Learning procedural-aware video representations is a key step towards building agents that can reason about and execute complex tasks. Existing methods typically address this problem by aligning visual content with textual descriptions at the task and step levels to inject procedural semantics into video representations. However, due to their high level of abstraction, "task" and "step" descriptions fail to form a robust alignment with the concrete, observable details in visual data. To address this, we introduce "states", i.e., textual snapshots of object configurations, as a visually-grounded semantic layer that anchors abstract procedures to what a model can actually see. We formalize this insight in a novel Task-Step-State (TSS) framework, where tasks are achieved via steps that drive transitions between observable states. To enforce this structure, we propose a progressive pre-training strategy that unfolds the TSS hierarchy, forcing the model to first ground representations in states before associating them with steps and, ultimately, high-level tasks. Extensive experiments on the COIN and CrossTask datasets show that our method outperforms baseline models on multiple downstream tasks, including task recognition, step recognition, and next step prediction. Ablation studies show that introducing state supervision is a key driver of performance gains across all tasks. Additionally, our progressive pretraining strategy proves more effective than standard joint training, as it better enforces the intended hierarchical structure.

Integrating machine learning with the Internet of Things (IoT) enables many useful applications. For IoT applications that incorporate video content analysis (VCA), deep learning models are usually used due to their capacity to encode the high-dimensional spatial and temporal representations of videos. However, limited energy and computation resources present a major challenge. Video captioning is one type of VCA that describes a video with a sentence or a set of sentences. This work proposes an IoT-based deep learning-based framework for video captioning that can (1) Mine large open-domain video-to-text datasets to extract video-caption pairs that belong to a particular domain. (2) Preprocess the selected video-caption pairs including reducing the complexity of the captions’ language model to improve performance. (3) Propose two deep learning models: A transformer-based model and an LSTM-based model. Hyperparameter tuning is performed to select the best hyperparameters. Models are evaluated in terms of accuracy and inference time on different platforms. The presented framework generates captions in standard sentence templates to facilitate extracting information in later stages of the analysis. The two developed deep learning models offer a trade-off between accuracy and speed. While the transformer-based model yields a high accuracy of 97%, the LSTM-based model achieves near real-time inference.

Video captioning infers the process of generating textual description from videos which describes the objects and actions present in it. The multimodal information is available on each frame based on texture and time in the video. In video captioning, the tremendous task is generating the caption automatically related to video content precisely. By using advancement in the field of deep learning technology, a model was developed to generates natural-language descriptions for activities in the video is proposed. In our proposed work, the first stage is extracting the key features for machine understandable about the video content using 2D and 3D CNN. The convolutional neural network(CNN) of 2D and 3D is used to extract both the spatial and temporal features respectively for transferring the videos into key features. The extracted features are preprocessed using NetVLAD. After NetVLAD preprocessing, the features are concatenated and given as input into attention based Long-Short Term Memory(aLSTM). aLSTM generates sentences in a sequential manner by selecting the salient features. The expected output of the model is a sentence to describe the contents of the video. The evaluation is done by using Bilingual Evaluation Understudy (BLEU) metrics.


 
 
 This study examined content of YouTube videos on cassava production and processing posted in Nigeria between 2009 and 2019. Purposive sampling method was used in selecting 155 YouTube videos with cassava production and processing contents. Primary data on video source, content, duration, quality, number of views, subscribers, likes, comments, presentation format and year of upload were obtained by watching these videos. Data were analysed using frequency counts, percentages, means, standard deviation. Many (53.5%) of the videos were on processing of cassava, while most (80.0%) of the videos were relatively recent (2014 - 2019). Private individuals (38.7%) and media houses (34.2%) were the main sources of videos on cassava production and processing on YouTube. The major content of the video was on agronomic practices in cassava production (12.9%) and value addition (11.0%). Also, 66.0% of the videos had video description (descriptive texts), 36.8% had mobilizing information while 52.3% had a video quality of 720p. Furthermore, 72.9% of the videos had between 1-to-7-minute runtime and these videos had more likes than dislikes with mean values of 92.8 and 5.6 respectively. The mean number of views, subscribers and comments were 11,138.1, 179,537.6 and 13.6, respectively. The presentation patterns in the videos were in form of news (24.5%) and documentary (20.0%). YouTube videos on cassava production and processing were well viewed with a substantial number of subscribers. More YouTube videos on cassava production and processing should be produced with varied contents by different stakeholders in the agricultural sector.