Detecting infrared dim and small targets is one crucial step for many tasks such as early warning. It remains a continuing challenge since characteristics of infrared small targets, usually represented by only a few pixels, are generally not salient. Despite that many traditional methods have significantly advanced the community, their robustness or efficiency is still lacking. Most recently, CNN-based object detection has achieved remarkable performance and some researchers focus on it. However, these methods are not computationally efficient when implemented on some CPU-only machines and few datasets are available publicly. To promote the detection of infrared small targets in complex backgrounds, we propose a new lightweight CNN-based architecture. The network contains three modules: the feature extraction module is designed for representing multi-scale and multi-level features, the grid resample operation module is proposed to fuse features from all scales, and a decoupled head to distinguish infrared small targets from backgrounds. Moreover, we collect a brand-new infrared small target detection dedicated dataset which consists of 68311 practical captured images with complex backgrounds for alleviating the data dilemma. To validate the proposed model, 54758 images are used for training and 13553 images are used for testing respectively. Extensive experimental results demonstrate that the proposed method outperforms all traditional methods by a large margin and runs much faster than other CNN methods with high precision. The proposed model can be implemented on the Intel i7-10850H CPU (2.3GHz) platform and Jetson Nano for real-time infrared small target detection at 44 FPS and 27 FPS, respectively. It can be even deployed on an Atom x5-Z8500 (1.44GHz) machine at about 25 FPS with 128×128 local images. The source codes and the dataset have been made publicly available at https://github.com/SeaHifly/Infrared-Small-Target.

Detecting oriented objects along with estimating their rotation information is one crucial step for image analysis, especially for remote sensing images. Despite that many methods proposed recently have achieved remarkable performance, most of them directly learn to predict object directions under the supervision of only one (e.g., the rotation angle) or a few (e.g., several coordinates) groundtruth (GT) values individually. Oriented object detection would be more accurate and robust if extra constraints, with respect to proposal and rotation information regression, are adopted for joint supervision during training. To this end, we propose a mechanism that simultaneously learns the regression of horizontal proposals, oriented proposals, and rotation angles of objects in a consistent manner, via naive geometric computing, as one additional steady constraint . An oriented center prior guided label assignment strategy is proposed for further enhancing the quality of proposals, yielding better performance. Extensive experiments on six datasets demonstrate the model equipped with our idea significantly outperforms the baseline by a large margin and several new state-of-the-art results are achieved without any extra computational burden during inference. Our proposed idea is simple and intuitive that can be readily implemented. Source codes are publicly available at: https://github.com/wangWilson/CGCDet.git.

Lifelong person re-identification (LReID) aims to train a unified model from diverse data sources step by step. The severe domain gaps between different training steps result in catastrophic forgetting in LReID, and existing methods mainly rely on data replay and knowledge distillation techniques to handle this issue. However, the former solution needs to store historical exemplars which inevitably impedes data privacy. The existing knowledge distillation-based models usually retain all the knowledge of the learned old models without any selections, which will inevitably include erroneous and detrimental knowledge that severely impacts the learning performance of the new model. To address these issues, we propose an exemplar-free LReID method named LongShort Term Knowledge Consolidation (LSTKC) that contains a Rectification-based Short-Term Knowledge Transfer module (R-STKT) and an Estimation-based Long-Term Knowledge Consolidation module (E-LTKC). For each learning iteration within one training step, R-STKT aims to filter and rectify the erroneous knowledge contained in the old model and transfer the rectified knowledge to facilitate the short-term learning of the new model. Meanwhile, once one training step is finished, E-LTKC proposes to further consolidate the learned long-term knowledge via adaptively fusing the parameters of models from different steps. Consequently, experimental results show that our LSTKC exceeds the state-of-the-art methods by 6.3%/9.4% and 7.9%/4.5%, 6.4%/8.0% and 9.0%/5.5% average mAP/R@1 on seen and unseen domains under two different training orders of the challenging LReID benchmark respectively.

The high-speed railway, powered by the catenary, serves as a high-capacity and high-frequency transportation means due to its convenience. Insulators are integral components of the catenary. Once defects appear in insulators, they pose a risk of serious traffic accidents. Automatic detection of insulator defects proves to be an effective way to avoid further breakdowns and ensure the safety of the high-speed railway. To this end, many deep learning-based approaches have been developed. Though high performances have been achieved, they heavily rely on a large amount of high-quality annotated samples. Nevertheless, in practical scenarios, defect samples are difficult to collect. Moreover, annotating these samples is both labor-intensive and expert-requiring. In addition, to the best of our knowledge, there is currently no publicly available dataset specifically dedicated to insulator defect detection. To address the issue of data scarcity, in this work, we first collect catenary insulator images captured by the high-speed rail inspection device on real railway lines and construct the catenary insulator defect (CID) dataset. To break the dilemma of the lack of defect data and high-quality annotations, we further propose a simple but effective insulator defect detection framework in an unsupervised image reconstruction manner. Extensive experiments demonstrate that our proposed method demonstrates high accuracy in detecting various insulator defects without the need for manual annotations. The CID dataset and source codes have been made publicly available at https://github.com/LightZH/Insulator-Defect-Detection .

Lossy image compression is a fundamental technology in media transmission and storage. Variable-rate approaches have recently gained much attention to avoid the usage of a set of different models for compressing images at different rates. During the media sharing, multiple re-encodings with different rates would be inevitably executed. However, existing Variational Autoencoder (VAE)-based approaches would be readily corrupted in such circumstances, resulting in the occurrence of strong artifacts and the destruction of image fidelity. Based on the theoretical findings of preserving image fidelity via invertible transformation, we aim to tackle the issue of high-fidelity fine variable-rate image compression and thus propose the Invertible Continuous Codec (I2C). We implement the I2C in a mathematical invertible manner with the core Invertible Activation Transformation (IAT) module. I2C is constructed upon a single-rate Invertible Neural Network (INN) based model and the quality level (QLevel) would be fed into the IAT to generate scaling and bias tensors. Extensive experiments demonstrate that the proposed I2C method outperforms state-of-the-art variable-rate image compression methods by a large margin, especially after multiple continuous re-encodings with different rates, while having the ability to obtain a very fine variable-rate control without any performance compromise.

Facial landmark detection is one crucial step for face-based image/video analysis. Despite the fact that recently many facial landmark detection models have achieved remarkable performance, most state-of-the-art heatmap regression-based methods heavily rely on initialization of the face detector. However, there inevitably exists semantic gaps among different annotators or face detectors. An improper facial bounding box will tremendously drop off the performance of the facial landmark detection model. Facial landmark detection would be more practical if robust to face images cropped by diverse manners (see Figure 1, the col.1 shows face images cropped by a proper bounding box, col.2 and col.3 show face images cropped by an oversize and a small bounding boxes respectively). To this end, we present a “Unconstrained Facial Landmark Detection(UFLD)” mechanism, that aims at enhancing the robustness of facial landmark detection, to deal with the inconsistent cropping manner issue. UFLD consists of two aspects: a Transformation-Invariant Landmark Detector(TILD) and an Availability-Guided Solver(AGS). TILD gives the ability to detect consistent landmarks for face images cropped by diverse manners. And AGS can alleviate the by-effect of “landmarks outside the image” caused by improper cropping results or TILD, and further promote the performance. The proposed mechanism achieved above 6.5% improvement in standard normalized landmarks mean error reduction on face images cropped by diverse manners compared to baselines.