Current 3D gaze estimation methods struggle to generalize across diverse data domains, primarily due to i) the scarcity of annotated datasets, and ii) the insufficient diversity of labeled data. In this work, we present OMNIGAZE, a semi-supervised framework for 3D gaze estimation, which utilizes large-scale unlabeled data collected from diverse and unconstrained real-world environments to mitigate domain bias and generalize gaze estimation in the wild. First, we build a diverse collection of unlabeled facial images, varying in facial appearances, background environments, illumination conditions, head poses, and eye occlusions. In order to leverage unlabeled data spanning a broader distribution, OMNIGAZE adopts a standard pseudo-labeling strategy and devises a reward model to assess the reliability of pseudo labels. Beyond pseudo labels as 3D direction vectors, the reward model also incorporates visual embeddings extracted by an off-the-shelf visual encoder and semantic cues from gaze perspective generated by prompting a Multimodal Large Language Model to compute confidence scores. Then, these scores are utilized to select high-quality pseudo labels and weight them for loss computation. Extensive experiments demonstrate that OMNIGAZE achieves state-of-the-art performance on five datasets under both in-domain and cross-domain settings. Furthermore, we also evaluate the efficacy of OMNIGAZE as a scalable data engine for gaze estimation, which exhibits robust zero-shot generalization on four unseen datasets.

This paper addresses the problem of learning avoidance behavior within the context of offline imitation learning. In contrast to conventional methodologies that prioritize the replication of expert or near-expert demonstrations, our work investigates a setting where expert (or desirable) data is absent, and the objective is to learn to eschew undesirable actions by leveraging demonstrations of such behavior (i.e., learning from negative examples). To address this challenge, we propose a novel training objective grounded in the maximum entropy principle. We further characterize the fundamental properties of this objective function, reformulating the learning process as a cooperative inverse Q-learning task. Moreover, we introduce an efficient strategy for the integration of unlabeled data (i.e., data of indeterminate quality) to facilitate unbiased and practical offline training. The efficacy of our method is evaluated across standard benchmark environments, where it consistently outperforms state-of-the-art baselines.

A prominent example of label-only privacy is in online advertising, where the goal is to predict conversionofanadimpression(thelabel)givenauser'sprofileandthespot'scontext(thefeatures).

With the development of social media, rumors spread quickly, cause great harm to society and economy. Thereby, many effective rumor detection methods have been developed, among which the rumor propagation structure learning based methods are particularly effective compared to other methods. However, the existing methods still suffer from many issues including the difficulty to obtain large-scale labeled rumor datasets, which leads to the low generalization ability and the performance degeneration on new events since rumors are time-critical and usually appear with hot topics or newly emergent events. In order to solve the above problems, in this study, we used large-scale unlabeled topic datasets crawled from the social media platform Weibo and Twitter with claim propagation structure to improve the semantic learning ability of a graph reprentation learing model on various topics. We use three typical graph self-supervised methods, InfoGraph, JOAO and GraphMAE in two commonly used training strategies, to verify the performance of general graph semi-supervised methods in rumor detection tasks. In addition, for alleviating the time and topic difference between unlabeled topic data and rumor data, we also collected a rumor dataset covering a variety of topics over a decade (10-year ago from 2022) from the Weibo rumor-refuting platform. Our experiments show that these general graph self-supervised learning methods outperform previous methods specifically designed for rumor detection tasks and achieve good performance under few-shot conditions, demonstrating the better generalization ability with the help of our massive unlabeled topic dataset.