Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units Detection

This paper presents our Facial Action Units (AUs) recognition submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence; (ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation; and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. This graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. The experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition

Boosting Adversarial Transferability across Model Genus by Deformation-Constrained Warping

Adversarial examples generated by a surrogate model typically exhibit limited transferability to unknown target systems. To address this problem, many transferability enhancement approaches (e.g., input transformation and model augmentation) have been proposed. However, they show poor performances in attacking systems having different model genera from the surrogate model. In this paper, we propose a novel and generic attacking strategy, called Deformation-Constrained Warping Attack (DeCoWA), that can be effectively applied to cross model genus attack. Specifically, DeCoWA firstly augments input examples via an elastic deformation, namely Deformation-Constrained Warping (DeCoW), to obtain rich local details of the augmented input. To avoid severe distortion of global semantics led by random deformation, DeCoW further constrains the strength and direction of the warping transformation by a novel adaptive control strategy. Extensive experiments demonstrate that the transferable examples crafted by our DeCoWA on CNN surrogates can significantly hinder the performance of Transformers (and vice versa) on various tasks, including image classification, video action recognition, and audio recognition. Code is made available at https://github.com/LinQinLiang/DeCoWA.Comment: AAAI 202

Scene Consistency Representation Learning for Video Scene Segmentation

A long-term video, such as a movie or TV show, is composed of various scenes, each of which represents a series of shots sharing the same semantic story. Spotting the correct scene boundary from the long-term video is a challenging task, since a model must understand the storyline of the video to figure out where a scene starts and ends. To this end, we propose an effective Self-Supervised Learning (SSL) framework to learn better shot representations from unlabeled long-term videos. More specifically, we present an SSL scheme to achieve scene consistency, while exploring considerable data augmentation and shuffling methods to boost the model generalizability. Instead of explicitly learning the scene boundary features as in the previous methods, we introduce a vanilla temporal model with less inductive bias to verify the quality of the shot features. Our method achieves the state-of-the-art performance on the task of Video Scene Segmentation. Additionally, we suggest a more fair and reasonable benchmark to evaluate the performance of Video Scene Segmentation methods. The code is made available.Comment: Accepted to CVPR 202

Joint Prediction of Audio Event and Annoyance Rating in an Urban Soundscape by Hierarchical Graph Representation Learning

Sound events in daily life carry rich information about the objective world. The composition of these sounds affects the mood of people in a soundscape. Most previous approaches only focus on classifying and detecting audio events and scenes, but may ignore their perceptual quality that may impact humans’ listening mood for the environment, e.g. annoyance. To this end, this paper proposes a novel hierarchical graph representation learning (HGRL) approach which links objective audio events (AE) with subjective annoyance ratings (AR) of the soundscape perceived by humans. The hierarchical graph consists of fine-grained event (fAE) embeddings with single-class event semantics, coarse-grained event (cAE) embeddings with multi-class event semantics, and AR embeddings. Experiments show the proposed HGRL successfully integrates AE with AR for AEC and ARP tasks, while coordinating the relations between cAE and fAE and further aligning the two different grains of AE information with the AR

Dynamic Hydrogel-Metal-Organic Framework System Promotes Bone Regeneration in Periodontitis Through Controlled Drug Delivery

Periodontitis is a prevalent chronic inflammatory disease, which leads to gradual degradation of alveolar bone. The challenges persist in achieving effective alveolar bone repair due to the unique bacterial microenvironment\u27s impact on immune responses. This study explores a novel approach utilizing Metal-Organic Frameworks (MOFs) (comprising magnesium and gallic acid) for promoting bone regeneration in periodontitis, which focuses on the physiological roles of magnesium ions in bone repair and gallic acid\u27s antioxidant and immunomodulatory properties. However, the dynamic oral environment and irregular periodontal pockets pose challenges for sustained drug delivery. A smart responsive hydrogel system, integrating Carboxymethyl Chitosan (CMCS), Dextran (DEX) and 4-formylphenylboronic acid (4-FPBA) was designed to address this problem. The injectable self-healing hydrogel forms a dual-crosslinked network, incorporating the MOF and rendering its on-demand release sensitive to reactive oxygen species (ROS) levels and pH levels of periodontitis. We seek to analyze the hydrogel\u27s synergistic effects with MOFs in antibacterial functions, immunomodulation and promotion of bone regeneration in periodontitis. In vivo and in vitro experiment validated the system\u27s efficacy in inhibiting inflammation-related genes and proteins expression to foster periodontal bone regeneration. This dynamic hydrogel system with MOFs, shows promise as a potential therapeutic avenue for addressing the challenges in bone regeneration in periodontitis