Edge Modeling Activation Free Fourier Network for Spacecraft Image Denoising

Spacecraft image denoising is a crucial basic technology closely related to aerospace research. However, the existing deep learning-based image denoising methods lack deep consideration of the characteristics of spacecraft image. To address the aforementioned shortcomings, we analyses spacecraft noise image and identifies two main characteristics. One is that there are a large number of low-light images in the obtained spacecraft noise image dataset. Another is there are a lot of repetitive periodic features in spacecraft image. According to the above mentioned characteristics, we propose a Edge modeling Activation Free Fourier Network (EAFFN), which is an efficient spacecraft image denoising method including Edge Modeling Block (EMB) and Activation Free Fourier Block (AFFB). We present EMB to effectively model edge and extract structural information and better identify the spacecraft components from dark regions in spacecraft noise image. We present AFFB and utilize an improved fast fourier block to extract repetitive periodic features and long-range information in noisy spacecraft image. In addition, Simple Gate is designed in our AFFB to reduce the computational complexity. Extensive experimental results demonstrate our EAFFN performs competitively to the state-of-the-art on spacecraft noise image datasets.

Norface: Improving Facial Expression Analysis by Identity Normalization

Facial Expression Analysis remains a challenging task due to unexpected task-irrelevant noise, such as identity, head pose, and background. To address this issue, this paper proposes a novel framework, called Norface, that is unified for both Action Unit (AU) analysis and Facial Emotion Recognition (FER) tasks. Norface consists of a normalization network and a classification network. First, the carefully designed normalization network struggles to directly remove the above task-irrelevant noise, by maintaining facial expression consistency but normalizing all original images to a common identity with consistent pose, and background. Then, these additional normalized images are fed into the classification network. Due to consistent identity and other factors (e.g. head pose, background, etc.), the normalized images enable the classification network to extract useful expression information more effectively. Additionally, the classification network incorporates a Mixture of Experts to refine the latent representation, including handling the input of facial representations and the output of multiple (AU or emotion) labels. Extensive experiments validate the carefully designed framework with the insight of identity normalization. The proposed method outperforms existing SOTA methods in multiple facial expression analysis tasks, including AU detection, AU intensity estimation, and FER tasks, as well as their cross-dataset tasks. For the normalized datasets and code please visit {https://norface-fea.github.io/}.

Open-Vocabulary X-ray Prohibited Item Detection via Fine-tuning CLIP

X-ray prohibited item detection is an essential component of security check and categories of prohibited item are continuously increasing in accordance with the latest laws. Previous works all focus on close-set scenarios, which can only recognize known categories used for training and often require time-consuming as well as labor-intensive annotations when learning novel categories, resulting in limited real-world applications. Although the success of vision-language models (e.g. CLIP) provides a new perspectives for open-set X-ray prohibited item detection, directly applying CLIP to X-ray domain leads to a sharp performance drop due to domain shift between X-ray data and general data used for pre-training CLIP. To address aforementioned challenges, in this paper, we introduce distillation-based open-vocabulary object detection (OVOD) task into X-ray security inspection domain by extending CLIP to learn visual representations in our specific X-ray domain, aiming to detect novel prohibited item categories beyond base categories on which the detector is trained. Specifically, we propose X-ray feature adapter and apply it to CLIP within OVOD framework to develop OVXD model. X-ray feature adapter containing three adapter submodules of bottleneck architecture, which is simple but can efficiently integrate new knowledge of X-ray domain with original knowledge, further bridge domain gap and promote alignment between X-ray images and textual concepts. Extensive experiments conducted on PIXray and PIDray datasets demonstrate that proposed method performs favorably against other baseline OVOD methods in detecting novel categories in X-ray scenario. It outperforms previous best result by 15.2 AP50 and 1.5 AP50 on PIXray and PIDray with achieving 21.0 AP50 and 27.8 AP50 respectively.

MMCL: Boosting Deformable DETR-Based Detectors with Multi-Class Min-Margin Contrastive Learning for Superior Prohibited Item Detection

Prohibited Item detection in X-ray images is one of the most effective security inspection methods.However, differing from natural light images, the unique overlapping phenomena in X-ray images lead to the coupling of foreground and background features, thereby lowering the accuracy of general object detectors.Therefore, we propose a Multi-Class Min-Margin Contrastive Learning (MMCL) method that, by clarifying the category semantic information of content queries under the deformable DETR architecture, aids the model in extracting specific category foreground information from coupled features.Specifically, after grouping content queries by the number of categories, we employ the Multi-Class Inter-Class Exclusion (MIE) loss to push apart content queries from different groups. Concurrently, the Intra-Class Min-Margin Clustering (IMC) loss is utilized to attract content queries within the same group, while ensuring the preservation of necessary disparity. As training, the inherent Hungarian matching of the model progressively strengthens the alignment between each group of queries and the semantic features of their corresponding category of objects. This evolving coherence ensures a deep-seated grasp of category characteristics, consequently bolstering the anti-overlapping detection capabilities of models.MMCL is versatile and can be easily plugged into any deformable DETR-based model with dozens of lines of code. Extensive experiments on the PIXray and OPIXray datasets demonstrate that MMCL significantly enhances the performance of various state-of-the-art models without increasing complexity. The code has been released at https://github.com/anonymity0403/MMCL.

Emphasizing Crucial Features for Efficient Image Restoration

Image restoration is a challenging ill-posed problem which estimates latent sharp image from its degraded counterpart. Although the existing methods have achieved promising performance by designing novelty architecture of module, they ignore the fact that different regions in a corrupted image undergo varying degrees of degradation. In this paper, we propose an efficient and effective framework to adapt to varying degrees of degradation across different regions for image restoration. Specifically, we design a spatial and frequency attention mechanism (SFAM) to emphasize crucial features for restoration. SFAM consists of two modules: the spatial domain attention module (SDAM) and the frequency domain attention module (FDAM). The SFAM discerns the degradation location through spatial selective attention and channel selective attention in the spatial domain, while the FDAM enhances high-frequency signals to amplify the disparities between sharp and degraded image pairs in the spectral domain. Additionally, to capture global range information, we introduce a multi-scale block (MSBlock) that consists of three scale branches, each containing multiple simplified channel attention blocks (SCABlocks) and a multi-scale feed-forward block (MSFBlock). Finally, we propose our ECFNet, which integrates the aforementioned components into a U-shaped backbone for recovering high-quality images. Extensive experimental results demonstrate the effectiveness of ECFNet, outperforming state-of-the-art (SOTA) methods on both synthetic and real-world datasets.

AO-DETR: Anti-Overlapping DETR for X-Ray Prohibited Items Detection

Prohibited item detection in X-ray images is one of the most essential and highly effective methods widely employed in various security inspection scenarios. Considering the significant overlapping phenomenon in X-ray prohibited item images, we propose an Anti-Overlapping DETR (AO-DETR) based on one of the state-of-the-art general object detectors, DINO. Specifically, to address the feature coupling issue caused by overlapping phenomena, we introduce the Category-Specific One-to-One Assignment (CSA) strategy to constrain category-specific object queries in predicting prohibited items of fixed categories, which can enhance their ability to extract features specific to prohibited items of a particular category from the overlapping foreground-background features. To address the edge blurring problem caused by overlapping phenomena, we propose the Look Forward Densely (LFD) scheme, which improves the localization accuracy of reference boxes in mid-to-high-level decoder layers and enhances the ability to locate blurry edges of the final layer. Similar to DINO, our AO-DETR provides two different versions with distinct backbones, tailored to meet diverse application requirements. Extensive experiments on the PIXray and OPIXray datasets demonstrate that the proposed method surpasses the state-of-the-art object detectors, indicating its potential applications in the field of prohibited item detection. The source code will be released at https://github.com/Limingyuan001/AO-DETR-test.

Towards a Simultaneous and Granular Identity-Expression Control in Personalized Face Generation

In human-centric content generation, the pre-trained text-to-image models struggle to produce user-wanted portrait images, which retain the identity of individuals while exhibiting diverse expressions. This paper introduces our efforts towards personalized face generation. To this end, we propose a novel multi-modal face generation framework, capable of simultaneous identity-expression control and more fine-grained expression synthesis. Our expression control is so sophisticated that it can be specialized by the fine-grained emotional vocabulary. We devise a novel diffusion model that can undertake the task of simultaneously face swapping and reenactment. Due to the entanglement of identity and expression, it's nontrivial to separately and precisely control them in one framework, thus has not been explored yet. To overcome this, we propose several innovative designs in the conditional diffusion model, including balancing identity and expression encoder, improved midpoint sampling, and explicitly background conditioning. Extensive experiments have demonstrated the controllability and scalability of the proposed framework, in comparison with state-of-the-art text-to-image, face swapping, and face reenactment methods.

FlowFace++: Explicit Semantic Flow-supervised End-to-End Face Swapping

This work proposes a novel face-swapping framework FlowFace++, utilizing explicit semantic flow supervision and end-to-end architecture to facilitate shape-aware face-swapping. Specifically, our work pretrains a facial shape discriminator to supervise the face swapping network. The discriminator is shape-aware and relies on a semantic flow-guided operation to explicitly calculate the shape discrepancies between the target and source faces, thus optimizing the face swapping network to generate highly realistic results. The face swapping network is a stack of a pre-trained face-masked autoencoder (MAE), a cross-attention fusion module, and a convolutional decoder. The MAE provides a fine-grained facial image representation space, which is unified for the target and source faces and thus facilitates final realistic results. The cross-attention fusion module carries out the source-to-target face swapping in a fine-grained latent space while preserving other attributes of the target image (e.g. expression, head pose, hair, background, illumination, etc). Lastly, the convolutional decoder further synthesizes the swapping results according to the face-swapping latent embedding from the cross-attention fusion module. Extensive quantitative and qualitative experiments on in-the-wild faces demonstrate that our FlowFace++ outperforms the state-of-the-art significantly, particularly while the source face is obstructed by uneven lighting or angle offset.

TalkCLIP: Talking Head Generation with Text-Guided Expressive Speaking Styles

In order to produce facial-expression-specified talking head videos, previous audio-driven one-shot talking head methods need to use a reference video with a matching speaking style (i.e., facial expressions). However, finding videos with a desired style may not be easy, potentially restricting their application. In this work, we propose an expression-controllable one-shot talking head method, dubbed TalkCLIP, where the expression in a speech is specified by the natural language. This would significantly ease the difficulty of searching for a video with a desired speaking style. Here, we first construct a text-video paired talking head dataset, in which each video has alternative prompt-alike descriptions. Specifically, our descriptions involve coarse-level emotion annotations and facial action unit (AU) based fine-grained annotations. Then, we introduce a CLIP-based style encoder that first projects natural language descriptions to the CLIP text embedding space and then aligns the textual embeddings to the representations of speaking styles. As extensive textual knowledge has been encoded by CLIP, our method can even generalize to infer a speaking style whose description has not been seen during training. Extensive experiments demonstrate that our method achieves the advanced capability of generating photo-realistic talking heads with vivid facial expressions guided by text descriptions.

Facial Affective Analysis based on MAE and Multi-modal Information for 5th ABAW Competition

Human affective behavior analysis focuses on analyzing human expressions or other behaviors, which helps improve the understanding of human psychology. CVPR 2023 Competition on Affective Behavior Analysis in-the-wild (ABAW) makes great efforts to provide the diversity data for the recognition of the commonly used emotion representations, including Action Units~(AU), basic expression categories and Valence-Arousal~(VA). In this paper, we introduce our submission to the CVPR 2023: ABAW5 for AU detection, expression classification, VA estimation and emotional reaction intensity (ERI) estimation. First of all, we introduce the vision information from an MAE model, which has been pre-trained on a large-scale face image dataset in a self-supervised manner. Then the MAE encoder part is finetuned on the ABAW challenges on the single frame of Aff-wild2 dataset. We also exploit the multi-modal and temporal information from the videos and design a transformer-based framework to fusion the multi-modal features. Moreover, we construct a novel two-branch collaboration training strategy to further enhance the model generalization by randomly interpolating the logits space. The extensive quantitative experiments, as well as ablation studies on the Aff-Wild2 dataset and Hume-Reaction dataset prove the effectiveness of our proposed method.