Implicit degradation modeling-based blind super-resolution (SR) has attracted more increasing attention in the community due to its excellent generalization to complex degradation scenarios and wide application range. How to extract more discriminative degradation representations and fully adapt them to specific image features is the key to this task. In this paper, we propose a new Content-decoupled Contrastive Learning-based blind image super-resolution (CdCL) framework following the typical blind SR pipeline. This framework introduces negative-free contrastive learning technique for the first time to model the implicit degradation representation, in which a new cyclic shift sampling strategy is designed to ensure decoupling between content features and degradation features from the data perspective, thereby improving the purity and discriminability of the learned implicit degradation space. In addition, we propose a detail-aware implicit degradation adapting module that can better adapt degradation representations to specific LR features by enhancing the basic adaptation unit's perception of image details, significantly reducing the overall SR model complexity. Extensive experiments on synthetic and real data show that our method achieves highly competitive quantitative and qualitative results in various degradation settings while obviously reducing parameters and computational costs, validating the feasibility of designing practical and lightweight blind SR tools. Codes and models will be available at https://github.com/Fieldhunter/CdCL.

Face forgery detection becomes increasingly crucial due to the serious security issues caused by face manipulation techniques. Various methods primarily concentrate on the features specific to certain generation techniques, potentially resulting in overfitting to the distinctive fingerprint characteristics of those manipulation techniques, thus undermining their generalizability. In contrast, our investigation reveals a prevalent phenomenon wherein regional noise consistency is disrupted during the integration of synthesized faces into source images, regardless of specific manipulation techniques. Motivated by this observation, we introduce the Regional Noise Consistency Learning Framework (RNCL), a novel approach designed to discern manipulated faces. Central to RNCL are two pivotal modules: Noise Consistency Enhancement (NCE) and Pyramidal Noise Consistency Learning (PNCL). The NCE module facilitates channel-wise and spatial-wise feature enhancement by exploiting noise inconsistencies between facial and non-facial regions. Complementarily, the PNCL module constructs a noise consistency pyramid to analyze enhanced features across multiple scales, enabling adaptive multi-scale feature integration. Leveraging the NCE and PNCL modules, our framework effectively transforms noise information into useful forgery cues, significantly enhancing forgery detection performance. Experimental results demonstrate that our method achieves state-of-the-art performance on standard benchmarks. The code will be publicly available.

Seismic random noise suppression presents two main challenges: achieving thorough noise suppression while simultaneously ensuring complete restoration of effective signal content. However, due to the complexity of random noise, existing denoising methods often only achieve an awkward balance between removing random noise and restoring effective signals. In this paper, we propose a novel random noise suppression model based on downsampling and super-resolution. By decoupling the denoising and signal restoration processes, our method reduces the difficulty of addressing these two challenges and mitigates the likelihood of suboptimal results. On the one hand, the high fitting-capacity Downsampling network uses non-linear transformations to separate random noise and effective signals while purifying the high-order features of effective signals. On the other hand, the Super-resolution network expands the low-dimensional seismic signal content containing the high-order features of the signal to restore the signal structure. Moreover, we propose a new adversarial loss by introducing the gradient between the generated data and the real data, which enhances the perceptual quality of the super-resolution results and recovers the content of effective signals better. Because both subnetworks are not affected by signal/noise features during processing, the model exhibits strong fitting and generalization abilities. The experimental evaluation on four different types of seismic data demonstrates the superiority of our method in suppressing random noise and restoring the content of effective signals.

Unsupervised image segmentation is a challenge task, since a high-quality segmented image should perceive not only local object structures but also certain semantics without any annotations. In this paper, we propose a novel encoder-decoder pixel clustering framework with dual constraints to incorporate local structure and global semantic information for guiding pixel feature learning in a self-supervised manner. On one hand, a Local Structure Constraint (LStC) is constructed based on fine-grained superpixels, which improves the boundary perception of pixel features by keeping intra-superpixel feature consistency and largening inter-superpixel feature distance. On the other hand, a new Global Semantic Constraint (GSeC) is proposed via adapting the mutual information maximization technique to the single-image setting, and it strengthens the global semantic perception of pixel features and thus improves the segmenting integrity of objects. Finally, based on the learned pixel features, a smoothing component is employed to achieve semantically meaningful pixel clustering. The experimental evaluation on BSDS500 and PASCAL Context datasets show the superiority of our method on region and boundary qualities.

The generalization of face swapping detectors against unseen face manipulation methods is important to practical applications. Most existing methods based on convolutional neural networks (CNN) simply map the facial images to real/fake binary labels and achieve high performance on the known forgeries, but they almost fail to detect new manipulation methods. In order to improve the generalization of face swapping detection, this work concentrates on a practical scenario to protect specific persons by proposing a novel face swapping detector requiring a reference image. To this end, we design a new detection framework based on identity spatial constraints (DISC), which consists of a backbone network and an identity semantic encoder (ISE). When inspecting an image of a particular person, the ISE utilizes a real facial image of that person as the reference to constrain the backbone to focus on the identity-related facial areas, so as to exploit the intrinsic discriminative clues to the forgery in the query image. Cross-dataset evaluations on five large-scale face forgery datasets show that DISC significantly improves the performance against unseen manipulation methods and is robust against the distortions. Compared to the existing detection methods, the AUC scores achieve 10%~40% performance improvements.

For weakly supervised object localization (WSOL), how to avoid the network focusing only on some small discriminative parts is a main challenge needed to solve. The widely-used Class Activation Mapping (CAM) based paradigm usually employs Adversarial Learning (AL) strategy to search more object parts by constantly hiding discovered object features, but the adversarial process is difficult to control. In this paper, we propose a novel CAM-based framework with Multi-scale Low-Discriminative Feature Reactivation (mLDFR) for WSOL. The mLDFR framework reactivates the low-discriminative object parts via bottom-up continuous feature maps recalibration and multi-scale object category mapping. Compared with the AL-based methods, our method fully improves the localization power of the network without damaging the classification power and can perform multi-instance localization, which are hard to achieve under the AL-based framework. Moreover, the mLDFR framework is flexible, and can be built on the top of various classical CNN backbones. Experimental results demonstrate the superiority of our method. With VGG16 as backbone, we achieve 46.96% Cls-Loc top1 err and 66.12% CorLoc on ILSVRC2014, 38.07% Cls-Loc top1 err and 75.04% CorLoc on CUB200-2011, surpassing the state-of-the-arts by a large margin.

In image annotation, one image is often modeled as a bag of regions (“instances”) associated with multiple labels, which is a typical application of multi-instance multi-label learning (MIML). Although lots of research has shown that the interplay embedded among instances and labels can largely boost the image annotation accuracy, most existing MIML methods consider none or partial context cues. In this paper, we propose a novel context-aware MIML model to integrate the instance context and label context into a general framework. Specially, the instance context is constructed with multiple graphs, while the label context is built up through a linear combination of several common latent conceptions that link low level features and high level semantic labels. Comparison with other leading methods on several benchmark datasets in terms of image annotation shows that our proposed method can get better performance than the state-of-the-art approaches.