Unsupervised domain adaptive object detection methods enhance model robustness in the target domain without requiring target-domain annotations. Despite notable progress, existing methods face two major challenges: 1) insufficient and inefficient learning of holistic feature consistency due to cumbersome pixel-level style matching and semantic discrepancy elimination between domains as well as the overlooking of their collaborative effect; and 2) unreliable learning of category feature compactness caused by poor-quality target-domain samples, inaccurate pseudo-labels and noisy cross-domain contrast paradigms. To address these challenges, we propose a novel Semantic Consistency and Compactness Learning (SCCL) network. For consistency learning, we introduce a Visual Adaptation-guided Semantic Alignment (VSA) module that achieves style matching through simple feature adaptation and incorporates a novel adversarial-free self-supervised method for feature disentanglement. The collaboration between these two aspects enables sufficient and efficient consistency learning. For reliable compactness learning, we develop a plug-and-play Instance Center-Contrastive (ICC) head that, for the first time, comprehensively addresses all three potential causes of unreliable learning through three integrated innovations, concerning sample pseudo-label quality enhancement, reliable sample storage and updating, and a robust sample contrast paradigm. Besides, the mutual reinforcement effect of VSA and ICC simultaneously enhances feature transferability and discriminability. Extensive experiments across four UDA object detection benchmarks with two baselines show that SCCL achieves superior adaptability and robustness. Code will be available at https://github.com/TooZE23/SCCL.

Mimicking the real interaction trajectory in the inference of the world model has been shown to improve the sample efficiency of model-based reinforcement learning (MBRL) algorithms. Many methods directly use known state sequences for reasoning. However, this approach fails to enhance the quality of reasoning by capturing the subtle variation between states. Much like how humans infer trends in event development from this variation, in this work, we introduce Global-Local variation Awareness Mamba-based world model (GLAM) that improves reasoning quality by perceiving and predicting variation between states. GLAM comprises two Mamba-based parallel reasoning modules, GMamba and LMamba, which focus on perceiving variation from global and local perspectives, respectively, during the reasoning process. GMamba focuses on identifying patterns of variation between states in the input sequence and leverages these patterns to enhance the prediction of future state variation. LMamba emphasizes reasoning about unknown information, such as rewards, termination signals, and visual representations, by perceiving variation in adjacent states. By integrating the strengths of the two modules, GLAM accounts for higher-value variation in environmental changes, providing the agent with more efficient imagination-based training. We demonstrate that our method outperforms existing methods in normalized human scores on the Atari 100k benchmark.

Polarization provides valuable physical information, making it beneficial for various computer vision tasks. However, haze reduces both the color and polarization information of a scene. While existing single-image dehazing methods can restore color information, they are poor at recovering polarization information. Furthermore, current polarization-based dehazing approaches neglect the physical mechanisms of polarization degradation, resulting in inaccurate reconstruction of polarization information. In this paper, we propose a novel polarization dehazing algorithm, along with a polarization degradation model, to accurately recover both polarization and color information. First, we combine two key characteristics (the polarization achromatism prior and polarization attenuation prior) with the polarization degradation model to precisely reconstruct the scene's polarization. Then, we utilize the reconstructed polarization information to recover the color information of the scene. Finally, a multi-scale fusion optimization framework is introduced to further enhance the image quality. Our method shows excellent performance on both real-world indoor and outdoor polarized images, outperforming existing dehazing algorithms in both objective evaluation metrics and subjective visual assessment.

Single-source domain generalization (SDG) for object detection is a challenging yet essential task as the distribution bias of the unseen domain degrades the algorithm per-formance significantly. However, existing methods attempt to extract domain-invariant features, neglecting that the bi-ased data leads the network to learn biased features that are non-causal and poorly generalizable. To this end, we pro-pose an Unbiased Faster R-CNN (UFR) for generalizable feature learning. Specifically, we formulate SDG in object detection from a causal perspective and construct a Struc-tural Causal Model (SCM) to analyze the data bias andfeature bias in the task, which are caused by scene confounders and object attribute confounders. Based on the SCM, we de-sign a Global-Local Transformation module for data aug-mentation, which effectively simulates domain diversity and mitigates the data bias. Additionally, we introduce a Causal Attention Learning module that incorporates a designed at-tention invariance loss to learn image-level features that are robust to scene confounders. Moreover, we develop a Causal Prototype Learning module with an explicit instance constraint and an implicit prototype constraint, which fur-ther alleviates the negative impact of object attribute con-founders. Experimental results on five scenes demonstrate the prominent generalization ability of our method, with an improvement of 3.9% mAP on the Night-Clear scene.

This paper addresses a very important question—how to select the right products to promote in order to maximize promotional benefit. We set up a framework to incorporate promotion decisions into the data-mining process, formulate the profit maximization problem as an optimization problem, and propose a heuristic search solution to discover the right products to promote. Moreover, we are able to get access to real supermarket data and apply our solution to help achieve higher profits. Our experimental results on both synthetic data and real supermarket data demonstrate that our framework and method are highly effective and can potentially bring huge profit gains to a marketing campaign.

In this paper, we apply artificial psychology (AP) to study consumer psychology, which is the indispensable factor for humanizing designs of commodity and commodity selection. Methods of statistical clustering such as factor analysis and quantification theory I, are adopted to construct the AP model considering consumer psychology. Finally, we design an expert system for commodity selection based on this model with VB6.0 and Access.