A new type of eye movement model based on recurrent neural networks for simulating the gaze behavior of human reading.

Traditional eye movement models are based on psychological assumptions and empirical data that are not able to simulate eye movement on previously unseen text data. To address this problem, a new type of eye movement model is presented and tested in this paper. In contrast to conventional psychology-based eye movement models, ours is based on a recurrent neural network (RNN) to generate a gaze point prediction sequence, by using the combination of convolutional neural networks (CNN), bidirectional long short-term memory networks (LSTM), and conditional random fields (CRF). The model uses the eye movement data of a reader reading some texts as training data to predict the eye movements of the same reader reading a previously unseen text. A theoretical analysis of the model is presented to show its excellent convergence performance. Experimental results are then presented to demonstrate that the proposed model can achieve similar prediction accuracy while requiring fewer features than current machine learning models

Usage of Blogging Software for Laboratory Management to Support Weekly Seminars Using Research Activity Reports

AbstractThis paper reports the design and use of blogging software in laboratory management to support weekly seminars, in which activity reports are an important resource for checking participants’ research activity. The software has three basic functions to support seminars: a report editing, comment, and chat. In order to support knowledge management, we added an evaluation function corresponding to each seminar report and a To-Do-List function to support driven objects as sub-goals. The blogging system was installed in a laboratory seminar, in which a teacher, a doctoral student, and seven students pursuing their master's degree participated over the course of five months. Results show that seminars conducted using the blogging software were evaluated more highly than paper-based seminars. However, only a few participants used the comment function, and the chat function was minimally used

Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic Segmentation

Impressive performance on point cloud semantic segmentation has been achieved by fully-supervised methods with large amounts of labelled data. As it is labour-intensive to acquire large-scale point cloud data with point-wise labels, many attempts have been made to explore learning 3D point cloud segmentation with limited annotations. Active learning is one of the effective strategies to achieve this purpose but is still under-explored. The most recent methods of this kind measure the uncertainty of each pre-divided region for manual labelling but they suffer from redundant information and require additional efforts for region division. This paper aims at addressing this issue by developing a hierarchical point-based active learning strategy. Specifically, we measure the uncertainty for each point by a hierarchical minimum margin uncertainty module which considers the contextual information at multiple levels. Then, a feature-distance suppression strategy is designed to select important and representative points for manual labelling. Besides, to better exploit the unlabelled data, we build a semi-supervised segmentation framework based on our active strategy. Extensive experiments on the S3DIS and ScanNetV2 datasets demonstrate that the proposed framework achieves 96.5% and 100% performance of fully-supervised baseline with only 0.07% and 0.1% training data, respectively, outperforming the state-of-the-art weakly-supervised and active learning methods. The code will be available at https://github.com/SmiletoE/HPAL.Comment: International Conference on Computer Vision (ICCV) 202

CatVersion: Concatenating Embeddings for Diffusion-Based Text-to-Image Personalization

We propose CatVersion, an inversion-based method that learns the personalized concept through a handful of examples. Subsequently, users can utilize text prompts to generate images that embody the personalized concept, thereby achieving text-to-image personalization. In contrast to existing approaches that emphasize word embedding learning or parameter fine-tuning for the diffusion model, which potentially causes concept dilution or overfitting, our method concatenates embeddings on the feature-dense space of the text encoder in the diffusion model to learn the gap between the personalized concept and its base class, aiming to maximize the preservation of prior knowledge in diffusion models while restoring the personalized concepts. To this end, we first dissect the text encoder's integration in the image generation process to identify the feature-dense space of the encoder. Afterward, we concatenate embeddings on the Keys and Values in this space to learn the gap between the personalized concept and its base class. In this way, the concatenated embeddings ultimately manifest as a residual on the original attention output. To more accurately and unbiasedly quantify the results of personalized image generation, we improve the CLIP image alignment score based on masks. Qualitatively and quantitatively, CatVersion helps to restore personalization concepts more faithfully and enables more robust editing.Comment: For the project page, please visit https://royzhao926.github.io/CatVersion-page

What were we all looking at? Identifying objects of collective visual attention

The file attached to this record is the authors final peer reviewed version. The publisher's final version can be found by following the DOI link below.We aim to identify the salient objects in an image by applying a model of visual attention. We automate the process by predicting those objects in an image that are most likely to be the focus of someone’s visual attention. Concretely, we first generate fixation maps from the eye tracking data, which express the ground truth of people’s visual attention for each training image. Then, we extract the high-level features based on the bag-of-visual-words image representation as input attributes along with the fixation maps to train a support vector regression model. With this model, we can predict a new query image’s saliency. Our experiments show that the model is capable of providing a good estimate for human visual attention in test images sets with one salient object and multiple salient objects. In this way, we seek to reduce the redundant information within the scene, and thus provide a more accurate depiction of the scene