Faizaan Sakib, Tilo Burghardt
We propose a first great ape-specific visual behaviour recognition system utilising deep learning that is capable of detecting nine core ape behaviours.
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Automating Visual Recognition of Leprosy in Wild Chimpanzees
研究针对野生黑猩猩麻风病视觉识别自动化问题，提出深度学习管道及数据集，通过基准测试不同分类方法，发现作物级预测简单聚合效果佳，且解决了部分可见个体帧抑制性能的问题。
The PanAf-SBR Dataset: Social Behaviour Recognition for Wild Great Apes
研究旨在解决野生大猩猩社会行为识别数据不足问题，引入PanAf-SBR数据集，结合AlphaChimp架构建立基准，通过双向迁移学习实验发现跨数据集预训练对特定类别有益，还研究了背景上下文在其中的作用。
PanAf20K: A Large Video Dataset for Wild Ape Detection and Behaviour Recognition
本研究发布了规模最大的开放获取野生大猿标注视频数据集PanAf20K，含2万段视频、超700万帧及配套标注基准，可支撑猿类检测等视觉任务，助力濒危大猿的保护研究。
A Study on the Impact of Face Image Quality on Face Recognition in the Wild
本文通过将野外人脸图像按质量分为三组并设计人类验证实验，研究图像质量对深度学习人脸识别的影响，发现质量问题仍是挑战且人类跨质量关联能力更强。
We propose a novel end-to-end curriculum learning approach for sparsely labelled animal datasets leveraging large volumes of unlabelled data to improve supervised species detectors. We exemplify the method in detail on the task of finding great apes in camera trap footage taken in challenging real-world jungle environments. In contrast to previous semi-supervised methods, our approach adjusts learning parameters dynamically over time and gradually improves detection quality by steering training towards virtuous self-reinforcement. To achieve this, we propose integrating pseudo-labelling with c…
While deep face recognition has benefited significantly from large-scale labeled data, current research is focused on leveraging unlabeled data to further boost performance, reducing the cost of human annotation. Prior work has mostly been in controlled settings, where the labeled and unlabeled data sets have no overlapping identities by construction. This is not realistic in large-scale face recognition, where one must contend with such overlaps, the frequency of which increases with the volume of data. Ignoring identity overlap leads to significant labeling noise, as data from the same ident…
An Open-World, Diverse, Cross-Spatial-Temporal Benchmark for Dynamic Wild Person Re-Identification
针对现有行人重识别基准数据集缺乏多样性、模型难以泛化到动态野外场景的问题，构建了开放世界、多样化、跨时空数据集OWD，并提出潜在域扩展方法LDE以解耦特征并增强隐式泛化能力。
In this article, the results of our team for the fifth Affective Behavior Analysis in-the-wild (ABAW) competition are presented. The usage of the pre-trained convolutional networks from the EmotiEffNet family for frame-level feature extraction is studied. In particular, we propose an ensemble of a multi-layered perceptron and the LightAutoML-based classifier. The post-processing by smoothing the results for sequential frames is implemented. Experimental results for the large-scale Aff-Wild2 database demonstrate that our model achieves a much greater macro-averaged F1-score for facial expressio…
Research in face recognition has seen tremendous growth over the past couple of decades. Beginning from algorithms capable of performing recognition in constrained environments, the current face recognition systems achieve very high accuracies on large-scale unconstrained face datasets. While upcoming algorithms continue to achieve improved performance, a majority of the face recognition systems are susceptible to failure under disguise variations, one of the most challenging covariate of face recognition. Most of the existing disguise datasets contain images with limited variations, often cap…
Lifting Scheme-Based Implicit Disentanglement of Emotion-Related Facial Dynamics in the Wild
本文提出隐式面部动态解耦框架IFDD，通过将小波提升方案扩展为可学习框架，利用两阶段模块从全局上下文中隐式解耦情感相关动态信息，在野外数据集上实现更高识别精度与可比效率。
We propose the first metric learning system for the recognition of great ape behavioural actions. Our proposed triple stream embedding architecture works on camera trap videos taken directly in the wild and demonstrates that the utilisation of an explicit DensePose-C chimpanzee body part segmentation stream effectively complements traditional RGB appearance and optical flow streams. We evaluate system variants with different feature fusion techniques and long-tail recognition approaches. Results and ablations show performance improvements of ~12% in top-1 accuracy over previous results achieve…
We propose the first multi-frame video object detection framework trained to detect great apes. It is applicable to challenging camera trap footage in complex jungle environments and extends a traditional feature pyramid architecture by adding self-attention driven feature blending in both the spatial as well as the temporal domain. We demonstrate that this extension can detect distinctive species appearance and motion signatures despite significant partial occlusion. We evaluate the framework using 500 camera trap videos of great apes from the Pan African Programme containing 180K frames, whi…
Light field (LF) cameras provide rich spatio-angular visual representations by sensing the visual scene from multiple perspectives and have recently emerged as a promising technology to boost the performance of human-machine systems such as biometrics and affective computing. Despite the significant success of LF representation for constrained facial image analysis, this technology has never been used for face and expression recognition in the wild. In this context, this paper proposes a new deep face and expression recognition solution, called CapsField, based on a convolutional neural networ…
This paper presents a novel CNN-RNN based approach, which exploits multiple CNN features for dimensional emotion recognition in-the-wild, utilizing the One-Minute Gradual-Emotion (OMG-Emotion) dataset. Our approach includes first pre-training with the relevant and large in size, Aff-Wild and Aff-Wild2 emotion databases. Low-, mid- and high-level features are extracted from the trained CNN component and are exploited by RNN subnets in a multi-task framework. Their outputs constitute an intermediate level prediction; final estimates are obtained as the mean or median values of these predictions.…
From Forest to Zoo: Great Ape Behavior Recognition with ChimpBehave
本文针对黑猩猩行为识别的挑战，提出了ChimpBehave数据集，包含超过2小时精心标注的动物园黑猩猩视频，通过行为类别对齐支持跨环境域适应研究，并使用CNN模型提供了数据集内和跨数据集的首个基线结果。
Despite the great advances in visual recognition, it has been witnessed that recognition models trained on clean images of common datasets are not robust against distorted images in the real world. To tackle this issue, we present a Universal and Recognition-friendly Image Enhancement network, dubbed URIE, which is attached in front of existing recognition models and enhances distorted input to improve their performance without retraining them. URIE is universal in that it aims to handle various factors of image degradation and to be incorporated with any arbitrary recognition models. Also, it…
Camera traps, unmanned observation devices, and deep learning-based image recognition systems have greatly reduced human effort in collecting and analyzing wildlife images. However, data collected via above apparatus exhibits 1) long-tailed and 2) open-ended distribution problems. To tackle the open-set long-tailed recognition problem, we propose the Temporal Flow Mask Attention Network that comprises three key building blocks: 1) an optical flow module, 2) an attention residual module, and 3) a meta-embedding classifier. We extract temporal features of sequential frames using the optical flow…
Improving Visual Perception of a Social Robot for Controlled and In-the-wild Human-robot Interaction
针对深度学习视觉模型高计算量对社交机器人交互表现与用户体验影响不明的问题，研究用前沿人体感知跟踪模型优化Pepper机器人视觉，通过两类实验评估其多人员场景下的用户跟随效果。
Automatic understanding of human affect using visual signals is of great importance in everyday human-machine interactions. Appraising human emotional states, behaviors and reactions displayed in real-world settings, can be accomplished using latent continuous dimensions (e.g., the circumplex model of affect). Valence (i.e., how positive or negative is an emotion) & arousal (i.e., power of the activation of the emotion) constitute popular and effective affect representations. Nevertheless, the majority of collected datasets this far, although containing naturalistic emotional states, have been…
Talking face generation with great practical significance has attracted more attention in recent audio-visual studies. How to achieve accurate lip synchronization is a long-standing challenge to be further investigated. Motivated by xxx, in this paper, an AttnWav2Lip model is proposed by incorporating spatial attention module and channel attention module into lip-syncing strategy. Rather than focusing on the unimportant regions of the face image, the proposed AttnWav2Lip model is able to pay more attention on the lip region reconstruction. To our limited knowledge, this is the first attempt to…
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