Multi-Task Multi-Modal Self-Supervised Learning for Facial Expression Recognition

Human communication is multi-modal; e.g., face-to-face interaction involves auditory signals (speech) and visual signals (face movements and hand gestures). Hence, it is essential to exploit multiple modalities when designing machine learning-based facial expression recognition systems. In addition, given the ever-growing quantities of video data that capture human facial expressions, such systems should utilize raw unlabeled videos without requiring expensive annotations. Therefore, in this work, we employ a multitask multi-modal self-supervised learning method for facial expression recognition from in-the-wild video data. Our model combines three self-supervised objective functions: First, a multi-modal contrastive loss, that pulls diverse data modalities of the same video together in the representation space. Second, a multi-modal clustering loss that preserves the semantic structure of input data in the representation space. Finally, a multi-modal data reconstruction loss. We conduct a comprehensive study on this multimodal multi-task self-supervised learning method on three facial expression recognition benchmarks. To that end, we examine the performance of learning through different combinations of self-supervised tasks on the facial expression recognition downstream task. Our model ConCluGen outperforms several multi-modal self-supervised and fully supervised baselines on the CMU-MOSEI dataset. Our results generally show that multi-modal self-supervision tasks offer large performance gains for challenging tasks such as facial expression recognition, while also reducing the amount of manual annotations required. We release our pre-trained models as well as source code publicly

Decomposer: Semi-supervised Learning of Image Restoration and Image Decomposition

We present Decomposer, a semi-supervised reconstruction model that decomposes distorted image sequences into their fundamental building blocks - the original image and the applied augmentations, i.e., shadow, light, and occlusions. To solve this problem, we use the SIDAR dataset that provides a large number of distorted image sequences: each sequence contains images with shadows, lighting, and occlusions applied to an undistorted version. Each distortion changes the original signal in different ways, e.g., additive or multiplicative noise. We propose a transformer-based model to explicitly learn this decomposition. The sequential model uses 3D Swin-Transformers for spatio-temporal encoding and 3D U-Nets as prediction heads for individual parts of the decomposition. We demonstrate that by separately pre-training our model on weakly supervised pseudo labels, we can steer our model to optimize for our ambiguous problem definition and learn to differentiate between the different image distortions.

DIAR: Deep Image Alignment and Reconstruction using Swin Transformers

When taking images of some occluded content, one is often faced with the problem that every individual image frame contains unwanted artifacts, but a collection of images contains all relevant information if properly aligned and aggregated. In this paper, we attempt to build a deep learning pipeline that simultaneously aligns a sequence of distorted images and reconstructs them. We create a dataset that contains images with image distortions, such as lighting, specularities, shadows, and occlusion. We create perspective distortions with corresponding ground-truth homographies as labels. We use our dataset to train Swin transformer models to analyze sequential image data. The attention maps enable the model to detect relevant image content and differentiate it from outliers and artifacts. We further explore using neural feature maps as alternatives to classical key point detectors. The feature maps of trained convolutional layers provide dense image descriptors that can be used to find point correspondences between images. We utilize this to compute coarse image alignments and explore its limitations.

SIDAR: Synthetic Image Dataset for Alignment & Restoration

Image alignment and image restoration are classical computer vision tasks. However, there is still a lack of datasets that provide enough data to train and evaluate end-to-end deep learning models. Obtaining ground-truth data for image alignment requires sophisticated structure-from-motion methods or optical flow systems that often do not provide enough data variance, i.e., typically providing a high number of image correspondences, while only introducing few changes of scenery within the underlying image sequences. Alternative approaches utilize random perspective distortions on existing image data. However, this only provides trivial distortions, lacking the complexity and variance of real-world scenarios. Instead, our proposed data augmentation helps to overcome the issue of data scarcity by using 3D rendering: images are added as textures onto a plane, then varying lighting conditions, shadows, and occlusions are added to the scene. The scene is rendered from multiple viewpoints, generating perspective distortions more consistent with real-world scenarios, with homographies closely resembling those of camera projections rather than randomized homographies. For each scene, we provide a sequence of distorted images with corresponding occlusion masks, homographies, and ground-truth labels. The resulting dataset can serve as a training and evaluation set for a multitude of tasks involving image alignment and artifact removal, such as deep homography estimation, dense image matching, 2D bundle adjustment, inpainting, shadow removal, denoising, content retrieval, and background subtraction. Our data generation pipeline is customizable and can be applied to any existing dataset, serving as a data augmentation to further improve the feature learning of any existing method.

Image-based Detection of Surface Defects in Concrete during Construction

Defects increase the cost and duration of construction projects. Automating defect detection would reduce documentation efforts that are necessary to decrease the risk of defects delaying construction projects. Since concrete is a widely used construction material, this work focuses on detecting honeycombs, a substantial defect in concrete structures that may even affect structural integrity. First, images were compared that were either scraped from the web or obtained from actual practice. The results demonstrate that web images represent just a selection of honeycombs and do not capture the complete variance. Second, Mask R-CNN and EfficientNet-B0 were trained for honeycomb detection to evaluate instance segmentation and patch-based classification, respectively achieving 47.7% precision and 34.2% recall as well as 68.5% precision and 55.7% recall. Although the performance of those models is not sufficient for completely automated defect detection, the models could be used for active learning integrated into defect documentation systems. In conclusion, CNNs can assist detecting honeycombs in concrete.

Content-Based Landmark Retrieval Combining Global and Local Features using Siamese Neural Networks

In this work, we present a method for landmark retrieval that utilizes global and local features. A Siamese network is used for global feature extraction and metric learning, which gives an initial ranking of the landmark search. We utilize the extracted feature maps from the Siamese architecture as local descriptors, the search results are then further refined using a cosine similarity between local descriptors. We conduct a deeper analysis of the Google Landmark Dataset, which is used for evaluation, and augment the dataset to handle various intra-class variances. Furthermore, we conduct several experiments to compare the effects of transfer learning and metric learning, as well as experiments using other local descriptors. We show that a re-ranking using local features can improve the search results. We believe that the proposed local feature extraction using cosine similarity is a simple approach that can be extended to many other retrieval tasks.

Action-based Contrastive Learning for Trajectory Prediction

Trajectory prediction is an essential task for successful human robot interaction, such as in autonomous driving. In this work, we address the problem of predicting future pedestrian trajectories in a first person view setting with a moving camera. To that end, we propose a novel action-based contrastive learning loss, that utilizes pedestrian action information to improve the learned trajectory embeddings. The fundamental idea behind this new loss is that trajectories of pedestrians performing the same action should be closer to each other in the feature space than the trajectories of pedestrians with significantly different actions. In other words, we argue that behavioral information about pedestrian action influences their future trajectory. Furthermore, we introduce a novel sampling strategy for trajectories that is able to effectively increase negative and positive contrastive samples. Additional synthetic trajectory samples are generated using a trained Conditional Variational Autoencoder (CVAE), which is at the core of several models developed for trajectory prediction. Results show that our proposed contrastive framework employs contextual information about pedestrian behavior, i.e. action, effectively, and it learns a better trajectory representation. Thus, integrating the proposed contrastive framework within a trajectory prediction model improves its results and outperforms state-of-the-art methods on three trajectory prediction benchmarks [31, 32, 26].

A modular framework for object-based saccadic decisions in dynamic scenes

Visually exploring the world around us is not a passive process. Instead, we actively explore the world and acquire visual information over time. Here, we present a new model for simulating human eye-movement behavior in dynamic real-world scenes. We model this active scene exploration as a sequential decision making process. We adapt the popular drift-diffusion model (DDM) for perceptual decision making and extend it towards multiple options, defined by objects present in the scene. For each possible choice, the model integrates evidence over time and a decision (saccadic eye movement) is triggered as soon as evidence crosses a decision threshold. Drawing this explicit connection between decision making and object-based scene perception is highly relevant in the context of active viewing, where decisions are made continuously while interacting with an external environment. We validate our model with a carefully designed ablation study and explore influences of our model parameters. A comparison on the VidCom dataset supports the plausibility of the proposed approach.

Learning Disentangled Expression Representations from Facial Images

Face images are subject to many different factors of variation, especially in unconstrained in-the-wild scenarios. For most tasks involving such images, e.g. expression recognition from video streams, having enough labeled data is prohibitively expensive. One common strategy to tackle such a problem is to learn disentangled representations for the different factors of variation of the observed data using adversarial learning. In this paper, we use a formulation of the adversarial loss to learn disentangled representations for face images. The used model facilitates learning on single-task datasets and improves the state-of-the-art in expression recognition with an accuracy of60.53%on the AffectNetdataset, without using any additional data.