Hong Fu

Chongkai Lu, Man-Wai Mak, Ruimin Li et al.

We present a novel framework, Action Progression Network (APN), for temporal action detection (TAD) in videos. The framework locates actions in videos by detecting the action evolution process. To encode the action evolution, we quantify a complete action process into 101 ordered stages (0\%, 1\%, ..., 100\%), referred to as action progressions. We then train a neural network to recognize the action progressions. The framework detects action boundaries by detecting complete action processes in the videos, e.g., a video segment with detected action progressions closely follow the sequence 0\%, 1\%, ..., 100\%. The framework offers three major advantages: (1) Our neural networks are trained end-to-end, contrasting conventional methods that optimize modules separately; (2) The APN is trained using action frames exclusively, enabling models to be trained on action classification datasets and robust to videos with temporal background styles differing from those in training; (3) Our framework effectively avoids detecting incomplete actions and excels in detecting long-lasting actions due to the fine-grained and explicit encoding of the temporal structure of actions. Leveraging these advantages, the APN achieves competitive performance and significantly surpasses its counterparts in detecting long-lasting actions. With an IoU threshold of 0.5, the APN achieves a mean Average Precision (mAP) of 58.3\% on the THUMOS14 dataset and 98.9\% mAP on the DFMAD70 dataset.

Rushuang Zhou, Zhiguo Zhang, Hong Fu et al.

Affective brain-computer interfaces based on electroencephalography (EEG) is an important branch in the field of affective computing. However, individual differences and noisy labels seriously limit the effectiveness and generalizability of EEG-based emotion recognition models. In this paper, we propose a novel transfer learning framework with Prototypical Representation based Pairwise Learning (PR-PL) to learn discriminative and generalized prototypical representations for emotion revealing across individuals and formulate emotion recognition as pairwise learning for alleviating the reliance on precise label information. Extensive experiments are conducted on two benchmark databases under four cross-validation evaluation protocols (cross-subject cross-session, cross-subject within-session, within-subject cross-session, and within-subject within-session). The experimental results demonstrate the superiority of the proposed PR-PL against the state-of-the-arts under all four evaluation protocols, which shows the effectiveness and generalizability of PR-PL in dealing with the ambiguity of EEG responses in affective studies. The source code is available at https://github.com/KAZABANA/PR-PL.

Guiming Cao, Kaize Shi, Hong Fu et al.

Pre-trained Vision-Language (V-L) models set the benchmark for generalization to downstream tasks among the noteworthy contenders. Many characteristics of the V-L model have been explored in existing research including the challenge of the sensitivity to text input and the tuning process across multi-modal prompts. With the advanced utilization of the V-L model like CLIP, recent approaches deploy learnable prompts instead of hand-craft prompts to boost the generalization performance and address the aforementioned challenges. Inspired by layer-wise training, which is wildly used in image fusion, we note that using a sequential training process to adapt different modalities branches of CLIP efficiently facilitates the improvement of generalization. In the context of addressing the multi-modal prompting challenge, we propose Token-wise Adaptive for Multi-modal Prompt Learning (APLe) for tuning both modalities prompts, vision and language, as tokens in a sequential manner. APLe addresses the challenges in V-L models to promote prompt learning across both modalities, which indicates a competitive generalization performance in line with the state-of-the-art. Preeminently, APLe shows robustness and favourable performance in prompt-length experiments with an absolute advantage in adopting the V-L models.

Bin Li, Panpan Zhang, Jinye Peng et al.

Heartbeat rhythm and heart rate (HR) are important physiological parameters of the human body. This study presents an efficient multi-hierarchical spatio-temporal convolutional network that can quickly estimate remote physiological (rPPG) signal and HR from face video clips. First, the facial color distribution characteristics are extracted using a low-level face feature generation (LFFG) module. Then, the three-dimensional (3D) spatio-temporal stack convolution module (STSC) and multi-hierarchical feature fusion module (MHFF) are used to strengthen the spatio-temporal correlation of multi-channel features. In the MHFF, sparse optical flow is used to capture the tiny motion information of faces between frames and generate a self-adaptive region of interest (ROI) skin mask. Finally, the signal prediction module (SP) is used to extract the estimated rPPG signal. The heart rate estimation results show that the proposed network overperforms the state-of-the-art methods on three datasets, 1) UBFC-RPPG, 2) COHFACE, 3) our dataset, with the mean absolute error (MAE) of 2.15, 5.57, 1.75 beats per minute (bpm) respectively.

Bin Li, Hong Fu, Ruimin Li et al.

Accurate hand joints detection from images is a fundamental topic which is essential for many applications in computer vision and human computer interaction. This paper presents a two stage network for hand joints detection from single unmarked image by using serial-parallel multi-scale feature fusion. In stage I, the hand regions are located by a pre-trained network, and the features of each detected hand region are extracted by a shallow spatial hand features representation module. The extracted hand features are then fed into stage II, which consists of serially connected feature extraction modules with similar structures, called "multi-scale feature fusion" (MSFF). A MSFF contains parallel multi-scale feature extraction branches, which generate initial hand joint heatmaps. The initial heatmaps are then mutually reinforced by the anatomic relationship between hand joints. The experimental results on five hand joints datasets show that the proposed network overperforms the state-of-the-art methods.

Dong Yang, Monica Mengqi Li, Hong Fu et al.

Combining skeleton structure with graph convolutional networks has achieved remarkable performance in human action recognition. Since current research focuses on designing basic graph for representing skeleton data, these embedding features contain basic topological information, which cannot learn more systematic perspectives from skeleton data. In this paper, we overcome this limitation by proposing a novel framework, which unifies 15 graph embedding features into the graph convolutional network for human action recognition, aiming to best take advantage of graph information to distinguish key joints, bones, and body parts in human action, instead of being exclusive to a single feature or domain. Additionally, we fully investigate how to find the best graph features of skeleton structure for improving human action recognition. Besides, the topological information of the skeleton sequence is explored to further enhance the performance in a multi-stream framework. Moreover, the unified graph features are extracted by the adaptive methods on the training process, which further yields improvements. Our model is validated by three large-scale datasets, namely NTU-RGB+D, Kinetics and SYSU-3D, and outperforms the state-of-the-art methods. Overall, our work unified graph embedding features to promotes systematic research on human action recognition.