2,035 research outputs found
A Closer Look at Temporal Ordering in the Segmentation of Instructional Videos
Understanding the steps required to perform a task is an important skill for
AI systems. Learning these steps from instructional videos involves two
subproblems: (i) identifying the temporal boundary of sequentially occurring
segments and (ii) summarizing these steps in natural language. We refer to this
task as Procedure Segmentation and Summarization (PSS). In this paper, we take
a closer look at PSS and propose three fundamental improvements over current
methods. The segmentation task is critical, as generating a correct summary
requires each step of the procedure to be correctly identified. However,
current segmentation metrics often overestimate the segmentation quality
because they do not consider the temporal order of segments. In our first
contribution, we propose a new segmentation metric that takes into account the
order of segments, giving a more reliable measure of the accuracy of a given
predicted segmentation. Current PSS methods are typically trained by proposing
segments, matching them with the ground truth and computing a loss. However,
much like segmentation metrics, existing matching algorithms do not consider
the temporal order of the mapping between candidate segments and the ground
truth. In our second contribution, we propose a matching algorithm that
constrains the temporal order of segment mapping, and is also differentiable.
Lastly, we introduce multi-modal feature training for PSS, which further
improves segmentation. We evaluate our approach on two instructional video
datasets (YouCook2 and Tasty) and observe an improvement over the
state-of-the-art of and for procedure segmentation and
summarization, respectively.Comment: Accepted at BMVC 202
Straight to the Point:Fast-forwarding Videos via Reinforcement Learning Using Textual Data
The rapid increase in the amount of published visual data and the limited time of users bring the demand for processing untrimmed videos to produce shorter versions that convey the same information. Despite the remarkable progress that has been made by summarization methods, most of them can only select a few frames or skims, which creates visual gaps and breaks the video context. In this paper, we present a novel methodology based on a reinforcement learning formulation to accelerate instructional videos. Our approach can adaptively select frames that are not relevant to convey the information without creating gaps in the final video. Our agent is textually and visually oriented to select which frames to remove to shrink the input video. Additionally, we propose a novel network, called Visually-guided Document Attention Network (VDAN), able to generate a highly discriminative embedding space to represent both textual and visual data. Our experiments show that our method achieves the best performance in terms of F1 Score and coverage at the video segment level
TAPER-WE: Transformer-Based Model Attention with Relative Position Encoding and Word Embedding for Video Captioning and Summarization in Dense Environment
In the era of burgeoning digital content, the need for automated video captioning and summarization in dense environments has become increasingly critical. This paper introduces TAPER-WE, a novel methodology for enhancing the performance of these tasks through the integration of state-of-the-art techniques. TAPER-WE leverages the power of Transformer-based models, incorporating advanced features such as Relative Position Encoding and Word Embedding. Our approach demonstrates substantial advancements in the domain of video captioning. By harnessing the contextual understanding abilities of Transformers, TAPER-WE excels in generating descriptive and contextually coherent captions for video frames. Furthermore, it provides a highly effective summarization mechanism, condensing lengthy videos into concise, informative summaries. One of the key innovations of TAPER-WE lies in its utilization of Relative Position Encoding, enabling the model to grasp temporal relationships within video sequences. This fosters accurate alignment between video frames and generated captions, resulting in superior captioning quality. Additionally, Word Embedding techniques enhance the model's grasp of semantics, enabling it to produce captions and summaries that are not only coherent but also linguistically rich. To validate the effectiveness of our proposed approach, we conducted extensive experiments on benchmark datasets, demonstrating significant improvements in captioning accuracy and summarization quality compared to existing methods. TAPER-WE not only achieves state-of-the-art performance but also showcases its adaptability and generalizability across a wide range of video content. In conclusion, TAPER-WE represents a substantial leap forward in the field of video captioning and summarization. Its amalgamation of Transformer-based architecture, Relative Position Encoding, and Word Embedding empowers it to produce captions and summaries that are not only informative but also contextually aware, addressing the growing need for efficient content understanding in the digital age
- …