With the rapid expansion of the Internet, online recruitment services are transforming traditional hiring practices. Human resource recommendation systems aim to reduce information overload by connecting job seekers with relevant opportunities. However, the evolving job market poses challenges such as cold-start and scalability. To address these challenges, we propose a Dual-Branch Mutual Learning (DBML) framework specifically designed for job recommendation in human resource management. At its core, DBML enhances a Bidirectional Long Short-Term Memory (BiLSTM) network by introducing a residual structure, resulting in an R-BiLSTM equipped with forward and backward residual gates. These R-BiLSTM units are deployed in parallel to encode unstructured textual features from job descriptions and resumes. The encoded representations are then processed through depthwise separable convolutions for efficient feature extraction, followed by a soft attention mechanism that captures fine-grained interactions between the text inputs. For cold-start scenarios, DBML integrates pre-trained GloVe embeddings into both job and resume encoders, allowing the model to generalize semantic similarities even for unseen entities. Experiments on large-scale datasets demonstrate that the proposed DBML achieves state-of-the-art results in accuracy, AUC, and F1 score for job-resume text matching. Moreover, DBML significantly improves scalability, reducing training time by 28.6% and parameter usage by 21.4% compared to a standard baseline model. In cold-start tests, DBML outperforms existing methods, achieving a 7.35% improvement in F1 score when applied to unseen job postings.

Digital image watermarking is a critical technology for image copyright protection. The concurrent evolution of watermarking attacks and defenses has spurred rapid advancements in the field. However, watermarking attack methods have lagged behind, often facing two primary challenges: limited watermark removal ability and quality degradation of the attacked image. In this paper, we introduce a Watermarking Attack method based on the Residual Diffusion Model, termed WARDM. Our WARDM treats watermark information as noise and leverages the powerful image reconstruction capabilities of the diffusion model to effectively remove the watermark. Specifically, we construct a Markov chain based on the residuals between the host and watermarked images, and employ reverse propagation to reconstruct the original host image. To optimally balance watermark removal ability and image quality, we incorporate a noise schedule into WARDM that controls both the velocity and intensity of noise at each stage of the Markov chain. Extensive experiments demonstrate the superior performance of WARDM in both watermark removal capability and visual quality preservation, achieving an improvement of 5.39% in PSNR over state-of-the-art methods. Moreover, our method demonstrates strong generalization, effectively executing attacks across a variety of watermarking techniques.

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.

Face deepfake detection is a critical technology for verifying the authenticity of facial media content and has long been a focal point in multimedia forensics. However, existing methods face significant challenges, primarily due to their limited ability to generalize across domains. Consequently, the growing variety of forgery techniques, combined with the degradation of visual quality in forged images, makes reliable detection even more difficult. To address these challenges, we propose WKD, a proactive deepfake detection framework based on Watermarking and Knowledge Distillation. The key insights of WKD are twofold: First, we embed watermark information into the Fractional-order Quaternion Radial Harmonic Fourier Moments (FrQRHFMs) space of the host image, achieving a robust balance between imperceptibility and robustness. Second, we design a dual-task learning framework consisting of a watermark extractor and a forgery discriminator, where learnable Low-Rank Adaptation (LoRA) layers are used to transfer knowledge from the extractor to the discriminator, thereby providing additional clues for deepfake detection. Specifically, the integrity of the watermark is compromised only when the host image undergoes a deepfake forgery, while it remains unaffected by conventional attacks. Experimental results on benchmark datasets demonstrate that WKD achieves state-of-the-art performance in both intra-domain and cross-domain deepfake detection, particularly when images are subjected to various conventional attacks.

Large-scale pre-training has proven to be an effective method for improving performance across different tasks. Current person search methods use ImageNet pre-trained models for feature extraction, yet it is not an optimal solution due to the gap between the pre-training task and person search task (as a downstream task). Therefore, in this paper, we focus on pre-training for person search, which involves detecting and re-identifying individuals simultaneously. Although labeled data for person search is scarce, datasets for two sub-tasks person detection and re-identification are relatively abundant. To this end, we propose a hybrid pre-training framework specifically designed for person search using sub-task data only. It consists of a hybrid learning paradigm that handles data with different kinds of supervisions, and an intra-task alignment module that alleviates domain discrepancy under limited resources. To the best of our knowledge, this is the first work that investigates how to support full-task pre-training using sub-task data. Extensive experiments demonstrate that our pre-trained model can achieve significant improvements across diverse protocols, such as person search method, fine-tuning data, pre-training data and model backbone. For example, our model improves ResNet50 based NAE by 10.3% relative improvement w.r.t. mAP. Our code and pre-trained models are released for plug-and-play usage to the person search community (https://github.com/personsearch/PretrainPS).

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.

Semantic segmentation in nighttime scenes presents a significant challenge in autonomous driving. Unsupervised domain adaptation (UDA) offers an effective solution by learning domain-invariant features to transfer models from the source domain (daytime scenes) to the target domain (nighttime scenes). Many methods introduce a latent domain to reduce the difficulty of UDA. However, they often build only a single adaptation pair of “latent-to-target”, which limits the effectiveness of knowledge transfer across different domains. In this letter, we propose a Masked Guided Meta-Learning (MGML) framework for domain-adaptive nighttime semantic segmentation. Within the MGML framework, we explore two key issues: how to generate the latent domain, and how to leverage the latent domain to assist meta-learning in reducing domain discrepancy. For the first issue, we employ the fast Fourier transform along with a complementary masking strategy to generate masked latent images that resemble the target scenes in the latent domain without adding to the training burden. For the second issue, we nest a mask-based consistency constraint within a bi-level meta-learning framework, enabling cross-domain knowledge acquired from the pair of “source-to-latent” to enhance the “latent-to-target” adaptation. Experiments on benchmark datasets demonstrate that our MGML achieves state-of-the-art performance, demonstrating the effectiveness of our approach in nighttime semantic segmentation.

Despite significant progress in medical image analysis using deep learning, predicting incidental prostate cancer (iPCa) remains challenging due to subtle differences in multiparametric magnetic resonance imaging (mpMRI) and a lower incidence rate. To address these challenges, we propose iPCa-Former, a transformer-based framework designed to enhance iPCa prediction within prostate mpMRI slices. Firstly, built on an encoder-decoder architecture, our iPCa-Former facilitates the simultaneous optimization of two tasks through mutual learning: prostate transition zone segmentation and iPCa prediction. Secondly, we introduce a joint optimization function that combines focal loss and boundary-based mutual information (BMI) loss, effectively addressing the imbalance of positive and negative samples in classification and the challenge posed by a small proportion of the foreground region in segmentation. Moreover, we construct an iPCa mpMRI dataset comprising 10,276 prostate mpMRI slices from 485 patients clinically diagnosed with benign prostatic hyperplasia, however, 27 out of these patients are identified as iPCa. When evaluated on this benchmark dataset, our iPCa-Former outperforms state-of-the-art methods, demonstrating the superior performance of our approach.

Weakly supervised semantic segmentation (WSSS) has received considerable interest since it relies only on image-level annotations rather than fine-grained pixel-wise annotations, which require vast human labor. Generating pseudo-masks (a.k.a. seeds) is arguably the most standard step for WSSS. The main difficulty is that seeds are usually sparse and incomplete. In this paper, we propose a dual branch framework by positive and negative learning for WSSS, which distills more accurate semantic information from multiple seeds instead of struggling to refine a single seed. First, we integrate different classification networks with class activation maps to generate multiple seeds. Then, considering that richer information exists in different seeds, we perform multi-source information distillation to obtain aggregated seeds that include clean labels and noisy labels, which are more comprehensive and reliable to train a segmentation model. Furthermore, we construct a dual branch segmentation network, which makes full use of correct information while eliminating incorrect information from distilled seeds that are further acquired by aggregated seeds. When evaluated on two benchmark datasets, our method outperforms state-of-the-art methods, demonstrating the superior performance.

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.

While thermal imaging technology on unmanned aerial vehicles (UAVs) has made significant progress, the widespread issue of insufficient resolution poses a serious challenge to comprehending the content of thermal images. Moreover, deploying super-resolution (SR) models on resource-limited UAVs presents considerable difficulties. In an effort to address these challenges, we propose a lightweight thermal image super-resolution (LTSR) model that efficiently extracts multiscale features and learns latent representations. First, we construct a multiscale knowledge distillation (MSKD) network to extract discriminative features from low-resolution (LR) inputs. To achieve this, we use convolution with varying dilation rates to extract features from diverse receptive fields and compress these features through knowledge distillation. Second, to effectively establish continuous relationships among features in the latent space, we develop a forward Markovian restoration process involving multiple diffusion iterations. In each iteration, we integrate the lightweight MSKD network and latent neural representation into a unified end-to-end framework. When evaluated on the challenging benchmark dataset, our method not only has fewer parameters but also outperforms state-of-the-art methods in SR accuracy. Extensive ablation analysis validates the effectiveness of each component in our LTSR.

Drone-based vehicle detection is a critical task within intelligent transportation systems. The 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 article, we propose a mask-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). First, 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. Second, 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 intermodal correlations. When evaluated on the DroneVehicle dataset, our MGMF achieves an 80.24% with respect to 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.

Person search is an integrated task of multiple sub-tasks such as foreground/background classification, bounding box regression and person re-identification. Therefore, person search is a typical multi-task learning problem, especially when solved in an end-to-end manner. Recently, some works enhance person search features by exploiting various auxiliary information, e.g. person joint keypoints, body part position, attributes, etc., which brings in more tasks and further complexifies a person search model. The inconsistent convergence rate of each task could potentially harm the model optimization. A straightforward solution is to manually assign different weights to different tasks, compensating for the diverse convergence rates. However, given the special case of person search, i.e. with a large number of tasks, it is impractical to weight the tasks manually. To this end, we propose a Grouped Adaptive Loss Weighting (GALW) method which adjusts the weight of each task automatically and dynamically. Specifically, we group tasks according to their convergence rates. Tasks within the same group share the same learnable weight, which is dynamically assigned by considering the loss uncertainty. Experimental results on two typical benchmarks, CUHK-SYSU and PRW, demonstrate the effectiveness of our method.

Deep learning based human parsing methods usually require a large amount of training data to reach high performance. However, it is costly and time-consuming to obtain manually annotated high quality labels for a large scale dataset. To alleviate annotation efforts, we propose a new semi-supervised human parsing method for which we only need a small number of labels for training. First, we generate high quality pseudo labels on unlabeled images using a hierarchical information passing network (HIPN), which reasons human part segmentation in a coarse to fine manner. Furthermore, we develop a noise-tolerant hybrid learning method, which takes advantage of positive and negative learning to better handle noisy pseudo labels. When evaluated on standard human parsing benchmarks, our HIPN achieves a new state-of-the-art performance. Moreover, our noise-tolerant hybrid learning method further improves the performance and outperforms the state-of-the-art semi-supervised method (i.e. GRN) by 4.47 points w.r.t mIoU on the LIP dataset.

Domain adaptive semantic segmentation aims to transfer knowledge learned from labeled source domain to unlabeled target domain. To narrow down the domain gap and ease adaptation difficulty, some recent methods translate source images to target-like images (latent domains), which are used as supplement or substitute to the original source data. Nevertheless, these methods neglect to explicitly model the relationship of knowledge transferring across different domains. Alternatively, in this work we break through the standard “source-target” one pair adaptation framework and construct multiple adaptation pairs (e.g. “source-latent” and “latent-target”). The purpose is to use the meta-knowledge (how to adapt) learned from one pair as guidance to assist the adaptation of another pair under a meta-learning framework. Furthermore, we extend our method to a more practical setting of open compound domain adaptation (a.k.a multiple-target domain adaptation), where the target is a compound of multiple domains without domain labels. In this setting, we embed an additional pair of “latent-latent” to reduce the domain gap between the source and different latent domains, allowing the model to adapt well on multiple target domains simultaneously. When evaluated on standard benchmarks, our method is superior to the state-of-the-art methods in both the single target and multiple-target domain adaptation settings.

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.

In this paper, we propose a new method for human parsing, which effectively maintains high-resolution representations and leverages body edge details to improve the performance. First, we propose a hybrid resolution network (HyRN) for human parsing and body edge detection. In our HyRN, we adopt deconvolution operation and auxiliary supervision to increase the discrimination ability of features from each scale. Second, considering the close relationship between human parsing and body edge detection, we propose a dual-task cascaded framework (DTCF), which implicitly integrates parsing and edge features to progressively refine the parsing results. Third, we develop an edge guided region mutual information loss, which uses the edge detection results to explicitly maintain the high order consistency between parsing prediction and ground truth around body edge pixels. When evaluated on standard benchmarks, our proposed HyRN achieves competitive accuracy compared with state-of-the-art human parsing methods. Moreover, our DTCF further improves the performance and outperforms the established baseline approach by 3.42 points w.t.r mIoU on the LIP dataset.