In real-world environments, a well-designed model must be capable of handling dynamically evolving distributions, where both in-distribution (ID) and out-of-distribution (OOD) samples appear unpredictably and individually, making real-time adaptation particularly challenging. While open-set test-time adaptation has demonstrated effectiveness in adjusting to distribution shifts, existing methods often rely on batch processing and struggle to manage single-sample data stream in open-set environments. To address this limitation, we propose Open-IRT, a novel open-set Intermediate-Representation-based Test-time adaptation framework tailored for single-image test-time adaptation with vision-language models. Open-IRT comprises two key modules designed for dynamic, single-sample adaptation in open-set scenarios. The first is Polarity-aware Prompt-based OOD Filter module, which fully constructs the ID-OOD distribution, considering both the absolute semantic alignment and relative semantic polarity. The second module, Intermediate Domain-based Test-time Adaptation module, constructs an intermediate domain and indirectly decomposes the ID-OOD distributional discrepancy to refine the separation boundary during the test-time. Extensive experiments on a range of domain adaptation benchmarks demonstrate the superiority of Open-IRT. Compared to previous state-of-the-art methods, it achieves significant improvements on representative benchmarks, such as CIFAR-100C and SVHN — with gains of +8.45\% in accuracy, -10.80\% in FPR95, and +11.04\% in AUROC.

Deep learning has achieved remarkable progress in various applications, heightening the importance of safeguarding the intellectual property (IP) of well-trained models. It entails not only authorizing usage but also ensuring the deployment of models in authorized data domains, i.e., making models exclusive to certain target domains. Previous methods necessitate concurrent access to source training data and target unauthorized data when performing IP protection, making them risky and inefficient for decentralized private data. In this paper, we target a practical setting where only a well-trained source model is available and investigate how we can realize IP protection. To achieve this, we propose a novel MAsk Pruning (MAP) framework. MAP stems from an intuitive hypothesis, i.e., there are target-related parameters in a well-trained model, locating and pruning them is the key to IP protection. Technically, MAP freezes the source model and learns a target-specific binary mask to prevent unauthorized data usage while minimizing performance degradation on authorized data. Moreover, we introduce a new metric aimed at achieving a better balance between source and target performance degradation. To verify the effectiveness and versatility, we have evaluated MAP in a variety of scenarios, including vanilla source-available, practical source-free, and challenging data-free. Extensive experiments indicate that MAP yields new state-of-the-art performance. Code will be available at https://github.com/ispc-lab/MAP.

In this paper, we address the problem of personalized gaze estimation. Due to the anatomical differences between individuals, current personalized gaze models often rely on fine-tuning or fully-supervised methods with labeled calibration samples, which may not be practical in real-world applications. To tackle this limitation, we propose an approach called Self-Supervised Test-Time Adaptation for Personalized Gaze Estimation (TTAGaze), which enables adaptation with small unlabeled data at test time. Our goal is to develop a gaze estimation model specifically adapted to a target person using only a few unlabeled images. We call this setting as unsupervised few-shot personalized adaptation in gaze estimation, which is more aligned with real-world scenarios compared to existing approaches. Additionally, Our approach leverages self-supervised learning and meta-learning. The model consists of the main task (gaze estimation) and a self-supervised auxiliary task. During training, the two task are trained using a coupled method. At test time, adaptation is achieved by optimizing the self-supervised loss adapted to an unseen person with a few unlabeled data. The model parameters are learned via model-agnostic meta-learning (MAML) to facilitate effective unsupervised few-shot personalized adaptation in gaze estimation. Experimental results demonstrate that the proposed method outperforms alternative approaches on several widely-used benchmark datasets.

We consider a new problem of few-shot learning of com-pact models. Meta-learning is a popular approach for few-shot learning. Previous work in meta-learning typically assumes that the model architecture during meta-training is the same as the model architecture used for final deployment. In this paper, we challenge this basic assumption. For final deployment, we often need the model to be small. But small models usually do not have enough capacity to effectively adapt to new tasks. In the mean time, we often have access to the large dataset and extensive computing power during meta-training since meta-training is typically per-formed on a server. In this paper, we propose task-specific meta distillation that simultaneously learns two models in meta-learning: a large teacher model and a small student model. These two models are jointly learned during meta-training. Given a new task during meta-testing, the teacher model is first adapted to this task, then the adapted teacher model is used to guide the adaptation of the student model. The adapted student model is used for final deployment. We demonstrate the effectiveness of our approach in few-shot image classification using model-agnostic meta-learning (MAML). Our proposed method outperforms other alternatives on several benchmark datasets.

Given a face image, most of previous works in gaze estimation infer the gaze via a well-trained model with supervised training. However, the distribution of test data may be very different compared to that of training data since samples might be corrupted in real-world scenarios (e.g., taking a photo in strong light). This will lead to a gap between source domain (i.e., training data) and target domain (i.e., test data). In this paper, we first introduce self-supervised learning into our method for addressing challenging situations in gaze estimation. Moreover, existing appearance-based gaze estimation methods focus on directing towards the development of powerful regressors, which mainly utilize face and eye images simultaneously or face (eye) images only. However, the problem of inter cues between face and eye features has been largely overlooked. To this end, we propose a novel Modulation-based Adaptive Network (MANet) for gaze estimation, which uses high-level knowledge to filter the distractive information and bridges the intrinsic relationship between face and eye features. Further, we combine self-supervised learning and MANet to learn to adapt to challenging cases, such as abnormal lighting conditions and poor-quality images, by minimizing a self-supervised loss and a supervised loss jointly. The experimental results on several datasets demonstrate the effectiveness of our proposed approach with a real-time speed of 900fpson a PC with an NVIDIA Titan RTX GPU.

As a natural way for human-computer interaction, fixation provides a promising solution for interactive image segmentation. In this paper, we focus on Personal Fixations-based Object Segmentation (PFOS) to address issues in previous studies, such as the lack of appropriate dataset and the ambiguity in fixations-based interaction. In particular, we first construct a new PFOS dataset by carefully collecting pixel-level binary annotation data over an existing fixation prediction dataset, such dataset is expected to greatly facilitate the study along the line. Then, considering characteristics of personal fixations, we propose a novel network based on Object Localization and Boundary Preservation (OLBP) to segment the gazed objects. Specifically, the OLBP network utilizes an Object Localization Module (OLM) to analyze personal fixations and locates the gazed objects based on the interpretation. Then, a Boundary Preservation Module (BPM) is designed to introduce additional boundary information to guard the completeness of the gazed objects. Moreover, OLBP is organized in the mixed bottom-up and top-down manner with multiple types of deep supervision. Extensive experiments on the constructed PFOS dataset show the superiority of the proposed OLBP network over 17 state-of-the-art methods, and demonstrate the effectiveness of the proposed OLM and BPM components. The constructed PFOS dataset and the proposed OLBP network are available at https://github.com/MathLee/OLBPNet4PFOS.

Summary In the past few years, executing high‐concurrency queries with interactive SQL query engines on Hadoop has become an important activity for many organizations. However, these systems do not adopt Multi‐Query Optimization (MQO) to accelerate the process. There are two major concerns. Firstly, traditional MQO researches assume that multiple queries have high similarity. However, these systems usually serve a variety of applications. Although queries from the same application have high similarity, queries from different applications may have low similarity, so using traditional MQO will be inefficient and time consuming. Secondly, integrating MQO may lead to lots of system modifications. To integrate MQO into interactive SQL query engines on Hadoop efficiently, a query grouping–based MQO framework is proposed. A lightweight mechanism is used to represent SQL queries, on which a grouping method is exploited to speed up the optimization process. A cost model is integrated to estimate the execution cost of interactive SQL query engines on Hadoop. By using the proposed framework, we modify Impala system to support MQO, and the experimental results on TPC‐DS show significant performance improvements.

Summary Impala system is an open source, analytic MPP database for Apache Hadoop. Impala system uses a query execution scheduling scheme that assigns near‐equal bytes retrieval tasks for different hosts to ensure system load balance. However, such “load balance” cannot guarantee a short response time for Impala system, when there are original loads in the system. Traditional query execution scheduling methods require either some assumptions or particular architecture, which cannot be directly used in Impala system. In this paper, we present a query execution scheduling scheme for Impala system. If the query fetches data from a single table, the scheme exploits the maximum flow algorithm. If the query fetches data from multiple tables, the scheme employs a cost‐based algorithm with heuristic pruning rules. In addition, we propose a cost model for Impala system, which considers parallel execution, communication cost, and cluster load. The performance of the proposed scheme is evaluated by the TPC‐DS benchmark, and experimental results show that the scheme can reduce the query response time by 10%‐30%.