Deep summarization models have succeeded in the video summarization field based on the development of gated recursive unit (GRU) and long and short-term memory (LSTM) technology. However, for some long videos, GRU and LSTM cannot effectively capture long-term dependencies. This paper proposes a deep summarization network with auxiliary summarization losses to address this problem. We introduce an unsupervised auxiliary summarization loss module with LSTM and a swish activation function to capture the long-term dependencies for video summarization, which can be easily integrated with various networks. The proposed model is an unsupervised framework for deep reinforcement learning that does not depend on any labels or user interactions. Additionally, we implement a reward function (R(S)) that jointly considers the consistency, diversity, and representativeness of generated summaries. Furthermore, the proposed model is lightweight and can be successfully deployed on mobile devices and enhance the experience of mobile users and reduce pressure on server operations. We conducted experiments on two benchmark datasets and the results demonstrate that our proposed unsupervised approach can obtain better summaries than existing video summarization methods. Furthermore, the proposed algorithm can generate higher F scores with a nearly 6.3% increase on the SumMe dataset and a 2.2% increase on the TVSum dataset compared to the DR-DSN model.
Gaze behavior is important and non-invasive human–computer interaction information that plays an important role in many fields—including skills transfer, psychology, and human–computer interaction. Recently, improving the performance of appearance-based gaze estimation, using deep learning techniques, has attracted increasing attention: however, several key problems in these deep-learning-based gaze estimation methods remain. Firstly, the feature fusion stage is not fully considered: existing methods simply concatenate the different obtained features into one feature, without considering their internal relationship. Secondly, dynamic features can be difficult to learn, because of the unstable extraction process of ambiguously defined dynamic features. In this study, we propose a novel method to consider feature fusion and dynamic feature extraction problems. We propose the static transformer module (STM), which uses a multi-head self-attention mechanism to fuse fine-grained eye features and coarse-grained facial features. Additionally, we propose an innovative recurrent neural network (RNN) cell—that is, the temporal differential module (TDM)—which can be used to extract dynamic features. We integrated the STM and the TDM into the static transformer with a temporal differential network (STTDN). We evaluated the STTDN performance, using two publicly available datasets (MPIIFaceGaze and Eyediap), and demonstrated the effectiveness of the STM and the TDM. Our results show that the proposed STTDN outperformed state-of-the-art methods, including that of Eyediap (by 2.9%).
Gaze estimation, which is a method to determine where a person is looking at given the person’s full face, is a valuable clue for understanding human intention. Similarly to other domains of computer vision, deep learning (DL) methods have gained recognition in the gaze estimation domain. However, there are still gaze calibration problems in the gaze estimation domain, thus preventing existing methods from further improving the performances. An effective solution is to directly predict the difference information of two human eyes, such as the differential network (Diff-Nn). However, this solution results in a loss of accuracy when using only one inference image. We propose a differential residual model (DRNet) combined with a new loss function to make use of the difference information of two eye images. We treat the difference information as auxiliary information. We assess the proposed model (DRNet) mainly using two public datasets (1) MpiiGaze and (2) Eyediap. Considering only the eye features, DRNet outperforms the state-of-the-art gaze estimation methods with angular-error of 4.57 and 6.14 using MpiiGaze and Eyediap datasets, respectively. Furthermore, the experimental results also demonstrate that DRNet is extremely robust to noise images.
The direction of human eye gaze is an important human behavior information that reflects the level of attention and cognitive state of the gazer towards various visual information in the environment. Eye gaze estimation has wide application value in multiple fields such as medical care, market research, and human-computer interaction. In recent years, some studies have introduced Transformer into the task of eye gaze estimation and achieved advanced performance. Although Transformer has better global modeling ability, its structural characteristics are not suitable for multi-scale feature learning in visual tasks. In addition, the global self-attention calculation for images has high complexity. This paper introduces Swin Transformer into the field of eye gaze estimation, using self-attention mechanism to perform more flexible and effective global modeling of images. The self-attention calculation uses Windows Multi-head Self-Attention(W-MSA) and Shifted Windows Multi-head Self-Attention (SW-MSA), which greatly reduces the calculation of image self-attention. The experimental results demonstrate that the Swin Transformer can obtain good results in the task of eye gaze estimation
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.