We first propose a novel parameterized text shape method based on low-rank approximation. Unlike other shape representation methods that employ data-irrelevant parameterization, our approach utilizes singular value decomposition and reconstructs the text shape using a few eigenvectors learned from labeled text contours. By exploring the shape correlation among different text contours, our method achieves consistency, compactness, simplicity, and robustness in shape representation. Next, we propose a dual assignment scheme for speed acceleration. It adopts a sparse assignment branch to accelerate the inference speed, and meanwhile, provides ample supervised signals for training through a dense assignment branch. Building upon these designs, we implement an accurate and efficient arbitrary-shaped text detector named LRANet.

We propose a novel Conditional Variational Autoencoder (DCL-CVAE) framework for diverse image captioning by seamlessly integrating sequential variational autoencoder with contrastive learning. In the encoding stage, we first build conditional variational autoencoders to separately learn the sequential latent spaces for a pair of captions. Then, we introduce contrastive learning in the sequential latent spaces to enhance the discriminability of latent representations for both image-caption pairs and mismatched pairs. In the decoding stage, we leverage the captions sampled from the pre-trained Long Short-Term Memory (LSTM), LSTM decoder as the negative examples and perform contrastive learning with the greedily sampled positive examples, which can restrain the generation of common words and phrases induced by the cross entropy loss. By virtue of dual constrastive learning, DCL-CVAE is capable of encouraging the discriminability and facilitating the diversity, while promoting the accuracy of the generated captions.

We propose a novel arbitrary-shaped scene text detection framework named CT-Net by progressive contour regression with contour transformers. Specifically, we first employ a contour initialization module that generates coarse text contours without any post-processing. Then, we adopt contour refinement modules to adaptively refine text contours in an iterative manner, which are beneficial for context information capturing and progressive global contour deformation. Besides, we propose an adaptive training strategy to enable the contour transformers to learn more potential deformation paths, and introduce a re-score mechanism that can effectively suppress false positives.

We first build a fine-grained feature extraction (FFE) module to obtain the refined local features of image attributes to compensate for holistic features. The FFE module is then used to generate user-level features, which are combined with the image-level features to obtain user-preferred fine-grained feature representations. By training extensive PIAA tasks, the aesthetic distribution of most users can be transferred to the personalized scores of individual users. To enable our proposed model to learn more generalizable aesthetics among individual users, we incorporate the degree of dispersion between personalized scores and image aesthetic distribution as a coefficient in the loss function during model training.

We propose a novel adaptive attention and relation (AAR) framework for facial AU detection. Specifically, we propose an adaptive attention regression network to regress the global attention map of each AU under the constraint of attention predefinition and the guidance of AU detection, which is beneficial for capturing both specified dependencies by landmarks in strongly correlated regions and facial globally distributed dependencies in weakly correlated regions. Moreover, considering the diversity and dynamics of AUs, we propose an adaptive spatio-temporal graph convolutional network to simultaneously reason the independent pattern of each AU, the inter-dependencies among AUs, as well as the temporal dependencies.

We propose IterativePFN (iterative point cloud filtering network), which consists of multiple IterationModules that model the true iterative filtering process internally, within a single network. We train our IterativePFN network using a novel loss function that utilizes an adaptive ground truth target at each iteration to capture the relationship between intermediate filtering results during training. This ensures filtered results converge faster to the clean surfaces.

We propose a novel light-weight anchor-free text detection framework called TextDCT, which adopts the discrete cosine transform (DCT) to encode the text masks as compact vectors. Further, considering the imbalanced number of training samples among pyramid layers, we only employ a single-level head for top-down prediction. To model the multi-scale texts in a single-level head, we introduce a novel positive sampling strategy by treating the shrunk text region as positive samples, and design a feature awareness module (FAM) for spatial-awareness and scale-awareness by fusing rich contextual information and focusing on more significant features. Moreover, we propose a segmented non-maximum suppression (S-NMS) method that can filter low-quality mask regressions.

We propose a weakly supervised few-shot semantic segmentation model based on the meta learning framework, which utilizes prior knowledge and adjusts itself according to new tasks. Thereupon then, the proposed network is capable of both high efficiency and generalization ability to new tasks. In the pseudo mask generation stage, we develop a WRCAM method with the channel-spatial attention mechanism to refine the coverage size of targets in pseudo masks. In the few-shot semantic segmentation stage, the optimization based meta learning method is used to realize few-shot semantic segmentation by virtue of the refined pseudo masks.

We propose an end-to-end deep learning based attention and relation learning framework for AU detection with only AU labels, which has not been explored before. In particular, multi-scale features shared by each AU are learned firstly, and then both channel-wise and spatial attentions are adaptively learned to select and extract AU-related local features. Moreover, pixel-level relations for AUs are further captured to refine spatial attentions so as to extract more relevant local features. Without changing the network architecture, our framework can be easily extended for AU intensity estimation.

Expression action unit (AU) recognition based on deep learning is a hot topic in the fields of computer vision and affective computing. Each AU describes a facial local expression action, and the combinations of AUs can quantitatively represent any expression. Current AU recognition mainly faces three challenging factors: scarcity of labels, difficulty of feature capture, and imbalance of labels. On the basis of this, this paper categorizes the existing researches into transfer learning based, region learning based, and relation learning based methods, and comments and summarizes each category of representative methods. Finally, this paper compares and analyzes different methods, and further discusses the future research directions of AU recognition.

We propose an end-to-end unconstrained facial AU detection framework based on domain adaptation, which transfers accurate AU labels from a constrained source domain to an unconstrained target domain by exploiting labels of AU-related facial landmarks. Specifically, we map a source image with label and a target image without label into a latent feature domain by combining source landmark-related feature with target landmark-free feature. Due to the combination of source AU-related information and target AU-free information, the latent feature domain with transferred source label can be learned by maximizing the target-domain AU detection performance. Moreover, we introduce a novel landmark adversarial loss to disentangle the landmark-free feature from the landmark-related feature by treating the adversarial learning as a multi-player minimax game.

We first use Structural Causal Models (SCMs) to show how two confounders damage the image captioning. Then we apply the backdoor adjustment to propose a novel causal inference based image captioning (CIIC) framework, which consists of an interventional object detector (IOD) and an interventional transformer decoder (ITD) to jointly confront both confounders. In the encoding stage, the IOD is able to disentangle the region-based visual features by deconfounding the visual confounder. In the decoding stage, the ITD introduces causal intervention into the transform decoder and deconfounds the visual and linguistic confounders simultaneously. Two modules collaborate with each other to eliminate the spurious correlations caused by the unobserved confounders.

We propose a novel geodesic guided convolution (GeoConv) for AU recognition by embedding 3D manifold information into 2D convolutions. Specifically, the kernel of GeoConv is weighted by our introduced geodesic weights, which are negatively correlated to geodesic distances on a coarsely reconstructed 3D morphable face model. Moreover, based on GeoConv, we further develop an end-to-end trainable framework named GeoCNN for AU recognition.

We propose to explicitly transfer facial expression by directly mapping two unpaired input images to two synthesized images with swapped expressions. Specifically, considering AUs semantically describe fine-grained expression details, we propose a novel multi-class adversarial training method to disentangle input images into two types of fine-grained representations: AU-related feature and AU-free feature. Then, we can synthesize new images with preserved identities and swapped expressions by combining AU-free features with swapped AU-related features. Moreover, to obtain reliable expression transfer results of the unpaired input, we introduce a swap consistency loss to make the synthesized images and self-reconstructed images indistinguishable.

We propose a novel end-to-end deep learning framework for joint AU detection and face alignment, which has not been explored before. In particular, multi-scale shared feature is learned firstly, and high-level feature of face alignment is fed into AU detection. Moreover, to extract precise local features, we propose an adaptive attention learning module to refine the attention map of each AU adaptively. Finally, the assembled local features are integrated with face alignment feature and global feature for AU detection.