Human parsing has attracted considerable research interest due to its broad potential applications in the computer vision community. In this paper, we explore several useful properties, including high-resolution representation, auxiliary guidance, and model robustness, which collectively contribute to a novel method for accurate human parsing in both simple and complex scenes. Starting from simple scenes: we propose the boundary-aware hybrid resolution network (BHRN), an advanced human parsing network. BHRN utilizes deconvolutional layers and multi-scale supervision to generate rich high-resolution representations. Additionally, it includes an edge perceiving branch designed to enhance the fineness of part boundaries. Building on BHRN, we construct a dual-task mutual learning (DTML) framework. It not only provides implicit guidance to assist the parser by incorporating boundary features, but also explicitly maintains the high-order consistency between the parsing prediction and the ground truth. Toward complex scenes: we develop a domain transform method to enhance the model robustness. By transforming the input space from the spatial domain to the polar harmonic Fourier moment domain, the mapping relationship to the output semantic space is highly stable. This transformation yields robust representations for both clean and corrupted data. When evaluated on standard benchmark datasets, our method achieves superior performance compared to state-of-the-art human parsing methods. Furthermore, our domain transform strategy significantly improves the robustness of DTML dramatically in most complex scenes.

Drone-based vehicle detection is a critical task within intelligent transportation systems. Existing methods that rely solely on single visible or infrared modalities often struggle to achieve both precise and robust detection. Effectively integrating cross-modal information to assist in vehicle detection remains a significant challenge. In this paper, we propose a Masked-Guided Mamba Fusion (MGMF) method for visible-infrared vehicle detection in aerial scenes. The proposed MGMF framework consists of two key components: the Masked Regularization Constraint Module (MRCM) and the State Space Fusion Module (SSFM). (1) In MAEM, we use candidate regions from one modality to cover corresponding regions of intermediate-level features from another modality, while a regularization constraint extracts cross-modal guidance. This design allows cross-modal features focused on vehicle areas to be extracted from both modalities for fusion. (2) In SSFM, we propose mapping cross-modal features into a shared hidden state for interaction. This reduces disparities between the cross-modal features and enhances the representation, enabling better perception of inter-modal correlations. When evaluated on the DroneVehicle dataset, our MGMF achieves an 80.24% w.r.t. mAP, establishing a new benchmark for state-of-the-art performance. Ablation studies further demonstrate the effectiveness of our MAEM and SSFM in enhancing visible-infrared fusion for vehicle detection.

In this paper, we propose a new method to super-resolve low resolution human body images by learning efficient multi-scale features and exploiting useful human body prior. Specifically, we propose a lightweight multi-scale block (LMSB) as basic module of a coherent framework, which contains an image reconstruction branch and a prior estimation branch. In the image reconstruction branch, the LMSB aggregates features of multiple receptive fields so as to gather rich context information for low-to-high resolution mapping. In the prior estimation branch, we adopt the human parsing maps and nonsubsampled shearlet transform (NSST) sub-bands to represent the human body prior, which is expected to enhance the details of reconstructed human body images. When evaluated on the newly collected HumanSR dataset, our method outperforms state-of-the-art image super-resolution methods with ~8× fewer parameters; moreover, our method significantly improves the performance of human image analysis tasks (e.g. human parsing and pose estimation) for low-resolution inputs.

Deepfake detection remains a challenging research topic, especially when the quality of forged images degrades, leading to unreliable detection results. In this paper, we propose a watermarking-based proactive method for robust proactive deepfake detection. First, we embed a watermark into the Fractional-order Quaternion Exponent Moments (FrQEMs) space of the host face image, achieving a balance between imperceptibility and robustness of the watermarking algorithm. Then, we introduce the Frequency Mamba (FreMamba) block to enhance feature extraction by leveraging correlations between frequency-domain subbands, thereby enabling the extraction of more discriminative feature representations. Finally, at the detection stage, we construct a dual-branch framework comprising a watermark extractor and a forgery discriminator. Through knowledge distillation, the watermark extractor guides the forgery discriminator to perceive forgery traces. Specifically, the integrity of the extracted watermark is compromised only when the host image is subjected to a deepfake attack, while conventional attacks do not affect the integrity. Experimental results on benchmark datasets demonstrate that the proposed method achieves superior deepfake detection accuracy. In particular, when images are subjected to conventional attacks, our method surpasses state-of-the-art approaches by more than 5.3% in terms of ACC.

Deep learning based object detection methods have made significant progress in recent years. However, these methods often suffer from a substantial performance drop when domain shifts occur, making it difficult to generalize a source domain trained object detector to a new target domain. To address this problem, we propose an Online Meta Learning Framework (OMLF) for unsupervised domain adaptive object detection. In our proposed framework, we adopt the Polar Harmonic Fourier Moment (PHFM) to generate target-like intermediate data. The purpose is to construct a two-pair framework that learns meta knowledge (i.e. model initial parameters) from the pair of “source-to-intermediate” to assist another pair of “intermediate-to-target”. Moreover, the optimizing process requires a heavy computational load due to triggering higher-order gradients. To alleviate this problem, we introduce a shortest-path update strategy that accelerates optimization. When evaluated on several benchmark adaptation scenarios (i.e. normal-to-foggy weather, cross cameras, synthetic-to-real, and real-to-artistic), our OMLF achieves state-of-the-art results, demonstrating its effectiveness.