Camouflaged object detection (COD) is a promising yet challenging task that aims to segment objects concealed within intricate surroundings, a capability crucial for modern industrial applications. Current COD methods primarily focus on the direct fusion of high-level and low-level information, without considering their differences and inconsistencies. Consequently, accurately segmenting highly camouflaged objects in challenging scenarios presents a considerable problem. To mitigate this concern, we propose a novel framework called visual and semantic separation network (VSSNet), which separately extracts low-level visual and high-level semantic cues and adaptively combines them for accurate predictions. Specifically, it features the information extractor module for capturing dimension-aware visual or semantic information from various perspectives. The complementary information-selected module leverages the complementary nature of visual and semantic information for adaptive selection and fusion. In addition, the region disparity weighting strategy encourages the model to prioritize the boundaries of highly camouflaged and difficult-to-predict objects. Experimental results on benchmark datasets show the VSSNet significantly outperforms State-of-the-Art COD approaches without data augmentations and multiscale training techniques. Furthermore, our method demonstrates satisfactory cross-domain generalization performance in real-world industrial environments.

Recent unsupervised anomaly detection methods often rely on feature extractors pretrained with auxiliary datasets or on well-crafted anomaly-simulated samples. However, this might limit their adaptability to an increasing set of anomaly detection tasks due to the priors in the selection of auxiliary datasets or the strategy of anomaly simulation. To tackle this challenge, we first introduce a prior-less anomaly generation paradigm and subsequently develop an innovative unsupervised anomaly detection framework named GRAD, grounded in this paradigm. GRAD comprises three essential components: (1) a diffusion model (PatchDiff) to generate contrastive patterns by preserving the local structures while disregarding the global structures present in normal images, (2) a self-supervised reweighting mechanism to handle the challenge of long-tailed and unlabeled contrastive patterns generated by PatchDiff, and (3) a lightweight patch-level detector to efficiently distinguish the normal patterns and reweighted contrastive patterns. The generation results of PatchDiff effectively expose various types of anomaly patterns, e.g. structural and logical anomaly patterns. In addition, extensive experiments on both MVTec AD and MVTec LOCO datasets also support the aforementioned observation and demonstrate that GRAD achieves competitive anomaly detection accuracy and superior inference speed.

Although great efforts have been devoted to developing lightweight models for age estimation in recent works, the robustness is still unsatisfactory in unconstrained environments. This paper proposes a Group‐aware Contrastive Network (GACN), a robust lightweight model, which extracts discriminative features by leveraging contrastive learning rather than increasing model parameters. Specifically, with a carefully designed contrastive loss function, GACN minimizes intra‐class distances and maximizes inter‐class distances between different age groups in feature space. Thus, faces belonging to the same age group are pulled together, while clusters of faces from different age groups are pushed apart. Unlike existing contrastive learning methods, which are separated from the downstream tasks, GACN integrates contrastive learning into age regression and jointly optimizes them for age representation learning. This allows to achieve robust age estimation using a lightweight network that is 1/662 of the model size of VGGNet. Extensive experiments on IMDB‐WIKI, Morph II, and FG‐NET demonstrate that the proposed method has a significant improvement over the baseline model and performs comparably to existing compact and bulky methods.

In facial landmark detection, extracting shape-indexed features is widely applied in existing methods to impose shape constraint over landmarks. Commonly, these methods crop shape-indexed patches surrounding landmarks of a given initial shape. All landmarks are then detected jointly based on these patches, with shape constraint naturally embedded in the regressor. However, there are still two remaining challenges that cause the degradation of these methods. First, the initial shape may seriously deviate from the ground truth when presented with a large pose, resulting in considerable noise in the shape-indexed features. Second, extracting local patch features is vulnerable to occlusions due to missing facial context information under severe occlusion. To address the issues above, this paper proposes a facial landmark detection algorithm named Sparse-To-Dense Network (STDN). First, STDN employs a lightweight network to detect sparse facial landmarks and forms a reinitialized shape, which can efficiently improve the quality of cropped patches when presented with large poses. Then, a group-relational module is used to exploit the inherent geometric relations of the face, which further enhances the shape constraint against occlusion. Our method achieves $4.64\%$ mean error with $1.97\%$ failure rate on COFW68 dataset, $3.48\%$ mean error with $0.43\%$ failure rate on 300 W dataset and $7.12\%$ mean error with $11.61\%$ failure rate on Masked 300 W dataset. The results demonstrate that STDN achieves outstanding performance in comparison to state-of-the-art methods, especially on occlusion datasets.

Action recognition involves capturing temporal information from video clips where the duration varies with videos for the same action. Due to the diverse scale of temporal context, uniform size kernels utilized in convolutional neural networks (CNNs) limit the capability of multiple-scale temporal modeling. In this paper, we propose a novel dilated multi-temporal (DMT) module that provides a solution for modeling multi-temporal information in action recognition. By using dilated convolutions with different dilation rates in different feature map channels, the DMT module captures information at multiple scales without the need for costly multi-branch networks, input-level frame pyramids, or feature map stacking that previous works have usually incurred. Therefore, this approach enables the integration of temporal information from multiple scales. In addition, the DMT module can be integrated into existing 2D CNNs, making it a straightforward and intuitive solution for addressing the challenge of multi-temporal modeling. Our proposed method has demonstrated promising results in performance and has achieved about 2% and 1% accuracy improvement on FineGym99 and SthV1. We conducted an empirical analysis that demonstrates how DMT improves the classification accuracy for action classes with varying durations.

Recent unsupervised anomaly detection methods often rely on feature extractors pretrained with auxiliary datasets or on well-crafted anomaly-simulated samples. However, this might limit their adaptability to an increasing set of anomaly detection tasks due to the priors in the selection of auxiliary datasets or the strategy of anomaly simulation. To tackle this challenge, we first introduce a prior-less anomaly generation paradigm and subsequently develop an innovative unsupervised anomaly detection framework named GRAD, grounded in this paradigm. GRAD comprises three essential components: (1) a diffusion model (PatchDiff) to generate contrastive patterns by preserving the local structures while disregarding the global structures present in normal images, (2) a self-supervised reweighting mechanism to handle the challenge of long-tailed and unlabeled contrastive patterns generated by PatchDiff, and (3) a lightweight patch-level detector to efficiently distinguish the normal patterns and reweighted contrastive patterns. The generation results of PatchDiff effectively expose various types of anomaly patterns, e.g. structural and logical anomaly patterns. In addition, extensive experiments on both MVTec AD and MVTec LOCO datasets also support the aforementioned observation and demonstrate that GRAD achieves competitive anomaly detection accuracy and superior inference speed.