Previous video-based human pose estimation methods have shown promising results by leveraging aggregated features of consecutive frames. However, most approaches compromise accuracy to mitigate jitter or do not sufficiently comprehend the temporal aspects of human motion. Furthermore, occlusion increases uncertainty between consecutive frames, which results in unsmooth results. To address these issues, we design an architecture that exploits the keypoint kinematic features with the following components. First, we effectively capture the temporal features by leveraging individual keypoint’s velocity and acceleration. Second, the proposed hierarchical transformer encoder aggregates spatio-temporal dependencies and refines the 2D or 3D input pose estimated from existing estimators. Finally, we provide an online cross-supervision between the refined input pose generated from the encoder and the final pose from our decoder to enable joint optimization. We demonstrate comprehensive results and validate the effectiveness of our model in various tasks: 2D pose estimation, 3D pose estimation, body mesh recovery, and sparsely annotated multi-human pose estimation. Our code is available at https://github.com/KyungMinJin/HANet.

Temporal Action Localization (TAL) is a significant and challenging task that searches for subtle human activities in an untrimmed video. To extract snippet-level video features, existing TAL methods commonly use video encoders pre-trained on short-video classification datasets. However, the snippet-level features can incur ambiguity between consecutive frames due to short and poor temporal information, disrupting the precise prediction of action instances. Several methods incorporating temporal relations have been proposed to mitigate this problem; however, they still suffer from poor video features. To address this issue, we propose a novel temporal action localization framework called an Action-aware Masking Network (AMNet). Our method simultaneously refines video features using action-aware attention and considers inherent temporal relations using self-attention and cross-attention mechanisms. First, we present an Action Masking Encoder (AME) that generates an action-aware mask to represent positive characteristics, which is then used to refine snippet-level features to be more salient around actions. Second, we design a Group Attention Module (GAM), which models relations of temporal information and exchanges mutual information by dividing the features into two groups, i.e., long and short-groups. Extensive experiments and ablation studies on two primary benchmark datasets demonstrate the effectiveness of AM-Net, and our method achieves state-of-the-art performances on THUMOS-14 and ActivityNet1.3.

This study focuses on improving the quality of pseudolabeling in the context of semi-supervised semantic segmentation. Previous studies have adopted confidence thresholding to reduce erroneous predictions in pseudo-labeled data and to enhance their qualities. However, numerous pseudolabels with high confidence scores exist in the early training stages even though their predictions are incorrect, and this ambiguity limits confidence thresholding substantially. In this paper, we present a novel method to resolve the ambiguity of confidence scores with the guidance of network pruning. A recent finding showed that network pruning severely impairs the network generalization ability on samples that are not yet well learned or represented. Inspired by this finding, we refine the confidence scores by reflecting the extent to which the predictions are affected by pruning. Furthermore, we adopted a curriculum learning strategy for the confidence score, which enables the network to learn gradually from easy to hard samples. This approach resolves the ambiguity by suppressing the learning of noisy pseudolabels, the confidence scores of which are difficult to trust owing to insufficient training in the early stages. Extensive experiments on various benchmarks demonstrate the superiority of our framework over state-of-the-art alternatives.

Recent studies for similarity-based self-supervised representation learning tend to consider only fixed temporal coverage from a given video. However, this approach limits that a model learns temporally persistent representations since it cannot reflect spatial and temporal information gaps from resolution variations. To overcome the limitation, this paper proposes a Temporal Adaptive Teacher-Student (TATS) framework that encourages the trained model to be robust on spatio-temporal variations. Our key approach is optimizing similarity-based learning that utilizes several views with dynamic temporal resolutions. From a given video, TATS captures spatio-temporal invariant clues for temporally persistent representation with cross-resolution correspondence between local and global views. Extensive experiments show that our TATS achieves competitive downstream (action recognition and video retrieval) performances on benchmarks (UCF101 and HMDB51).

Although many approaches for multi-human pose estimation in videos have shown profound results, they require densely annotated data which entails excessive man labor. Furthermore, there exists occlusion and motion blur that inevitably lead to poor estimation performance. To address these problems, we propose a method that leverages an attention mask for occluded joints and encodes temporal dependency between frames using transformers. First, our framework composes different combinations of sparsely annotated frames that denote the track of the overall joint movement. We propose an occlusion attention mask from these combinations that enable encoding occlusion-aware heatmaps as a semi-supervised task. Second, the proposed temporal encoder employs transformer architecture to effectively aggregate the temporal relationship and keypoint-wise attention from each time step and accurately refines the target frame’s final pose estimation. We achieve state-of-the-art pose estimation results for PoseTrack2017 and PoseTrack2018 datasets and demonstrate the robustness of our approach to occlusion and motion blur in sparsely annotated video data.

Temporal action localization (TAL) is a task of identifying a set of actions in a video, which involves localizing the start and end frames and classifying each action instance. Existing methods have addressed this task by using predefined anchor windows or heuristic bottom-up boundary-matching strategies, which are major bottlenecks in inference time. Additionally, the main challenge is the inability to capture long-range actions due to a lack of global contextual information. In this paper, we present a novel anchor-free framework, referred to as HTNet, which predicts a set of $\langle$start time, end time, class$\rangle$ triplets from a video based on a Transformer architecture. After the prediction of coarse boundaries, we refine it through a background feature sampling (BFS) module and hierarchical Transformers, which enables our model to aggregate global contextual information and effectively exploit the inherent semantic relationships in a video. We demonstrate how our method localizes accurate action instances and achieves state-of-the-art performance on two TAL benchmark datasets: THUMOS14 and ActivityNet 1.3.

Although various face-related tasks have significantly advanced in recent years, occlusion and extreme pose still impede the achievement of higher performance. Existing face rotation or de-occlusion methods only have emphasized the aspect of each problem. In addition, the lack of high-quality paired data remains an obstacle for both methods. In this work, we present a self-supervision strategy called Swap-R&R to overcome the lack of ground-truth in a fully unsupervised manner for joint face rotation and de-occlusion. To generate an input pair for self-supervision, we transfer the occlusion from a face in an image to an estimated 3D face and create a damaged face image, as if rotated from a different pose by rotating twice with the roughly de-occluded face. Furthermore, we propose Complete Face Recovery GAN (CFR-GAN) to restore the collapsed textures and disappeared occlusion areas by leveraging the structural and textural differences between two rendered images. Unlike previous works, which have selected occlusion-free images to obtain ground-truths, our approach does not require human intervention and paired data. We show that our proposed method can generate a de-occluded frontal face image from an occluded profile face image. Moreover, extensive experiments demonstrate that our approach can boost the performance of facial recognition and facial expression recognition. The code is publicly available1

Despite the recent success of 3D human reconstruction methods, recovering the accurate and smooth 3D human motion from video is still challenging. Designing a temporal model in the encoding stage is not sufficient enough to settle the trade-off problem between the per-frame accuracy and the motion smoothness. To address this problem, we approach some of the fundamental problems of 3D reconstruction tasks, simultaneously predicting 3D pose and 3D motion dynamics. First, we utilize the power of uncertainty to address the problem of multiple 3D configurations resulting in the same 2D projections. Second, we confirmed that dividing the body into local regions shows outstanding results for estimating 3D motion dynamics. In this paper, we propose (i) an encoder that makes two different estimations: a static feature that presents 2D pose feature as distribution and a dynamic feature that includes optical flow information and (ii) a decoder that divides the body into five different local regions to estimate the 3D motion dynamics of each region. We demonstrate how our method recovers the accurate and smooth motion and achieves the state-of-the-art results for both constrained and in-the-wild videos.

Computer-aided diagnosis has recently received attention for its advantage of low cost and time efficiency. Although deep learning played a major role in the recent success of acne detection, there are still several challenges such as color shift by inconsistent illumination, variation in scales, and high density distribution. To address these problems, we propose an acne detection network which consists of three components, specifically: Composite Feature Refinement, Dynamic Context Enhancement, and Mask-Aware Multi-Attention. First, Composite Feature Refinement integrates semantic information and fine details to enrich feature representation, which mitigates the adverse impact of imbalanced illumination. Then, Dynamic Context Enhancement controls different receptive fields of multi-scale features for context enhancement to handle scale variation. Finally, Mask-Aware Multi-Attention detects densely arranged and small acne by suppressing uninformative regions and highlighting probable acne regions. Experiments are performed on acne image dataset ACNE04 and natural image dataset PASCAL VOC 2007. We demonstrate how our method achieves the state-of-the-art result on ACNE04 and competitive performance with previous state-of-the-art methods on the PASCAL VOC 2007.

3D Morphable Model (3DMM) is a statistical model of facial shape and texture using a set of linear basis functions. Most of the recent 3D face reconstruction methods aim to embed the 3D morphable basis functions into Deep Convolutional Neural Network (DCNN). However, balancing the requirements of strong regularization for global shape and weak regularization for high level details is still ill-posed. To address this problem, we properly control generality and specificity in terms of regularization by harnessing the power of uncertainty. Additionally, we focus on the concept of nonlinearity and find out that Graph Convolutional Neural Network (Graph CNN) and Generative Adversarial Network (GAN) are effective in reconstructing high quality 3D shapes and textures respectively. In this paper, we propose to employ (i) an uncertainty-aware encoder that presents face features as distributions and (ii) a fully nonlinear decoder model combining Graph CNN with GAN. We demonstrate how our method builds excellent high quality results and outperforms previous state-of-the-art methods on 3D face reconstruction tasks for both constrained and in-the-wild images.