CONet: Crowd and occlusion-aware network for occluded human pose estimation.

Human pose estimation has numerous applications in motion recognition, virtual reality, human-computer interaction, and other related fields. However, multi-person pose estimation in crowded and occluded scenes is challenging. One major issue about the current top-down human pose estimation approaches is that they are limited to predicting the pose of a single person, even when the bounding box contains multiple individuals. To address this problem, we propose a novel Crowd and Occlusion-aware Network (CONet) using a divide-and-conquer strategy. Our approach includes a Crowd and Occlusion-aware Head (COHead) which estimates the pose of both the occluder and the occluded person using two separate branches. We also use the attention mechanism to guide the branches for differentiated learning, aiming to improve feature representation. Additionally, we propose a novel interference point loss to enhance the model's anti-interference ability. Our CONet is simple yet effective, and it outperforms the state-of-the-art model by +1.6 AP, achieving 71.6 AP on CrowdPose. Our proposed model has achieved state-of-the-art results on the CrowdPose dataset, demonstrating its effectiveness in improving the accuracy of human pose estimation in crowded and occluded scenes. This achievement highlights the potential of our model in many real-world applications where accurate human pose estimation is crucial, such as surveillance, sports analysis, and human-computer interaction.

[Phenolic constituents of Ampelopsis grossedentata from zhangjiajie].

OBJECTIVE: To study the phenolic constituents from Ampelopsis grossedentata. METHODS: Compounds were isolated using column chromatographic techniques (silica gel, polyamide gel, Sephadex LH-20) and semi-preparative HPLC. Structures were elucidated on the basis of spectral data (NMR and HR-MS). RESULTS: Eight compounds were isolated and identified as ampelopsin (I), 5, 7, 3',4',5'-pentahydroxyflavanone (II), galloyl-beta-D-glucopyranoside (III), gallic acid (IV), ethyl gallate (V), myricitrin (VI), (2R, 3S)-5,7,3',4',5'-pentahydroxyflavanonol (VII) and myricetin (VIII). CONCLUSION: Compounds II and VII are obtained from this genus for the first time.

ProMask: Probability mask representation for skeleton detection.

Detecting object skeletons in natural images presents challenges due to varied object scales and complex backgrounds. The skeleton is a highly compressing shape representation, which can bring some essential advantages but cause difficulties in detection. This skeleton line occupies a small part of the image and is overly sensitive to spatial position. Inspired by these issues, we propose the ProMask, which is a novel skeleton detection model. The ProMask includes the probability mask representation and vector router. This skeleton probability mask describes the gradual formation process of skeleton points, which can achieve high detection performance and robustness. Moreover, the vector router module possesses two sets of orthogonal basis vectors in a two-dimensional space, which can dynamically adjust the predicted skeleton position. Experiments show that our approach realizes better performance, efficiency, and robustness than state-of-the-art methods. We consider that our proposed skeleton probability representation will serve as a standard configuration for future skeleton detection, since it is reasonable, simple, and very effective.

On the robustness of skeleton detection against adversarial attacks.

Human perception of an object's skeletal structure is particularly robust to diverse perturbations of shape. This skeleton representation possesses substantial advantages for parts-based and invariant shape encoding, which is essential for object recognition. Multiple deep learning-based skeleton detection models have been proposed, while their robustness to adversarial attacks remains unclear. (1) This paper is the first work to study the robustness of deep learning-based skeleton detection against adversarial attacks, which are only slightly unlike the original data but still imperceptible to humans. We systematically analyze the robustness of skeleton detection models through exhaustive adversarial attacking experiments. (2) We propose a novel Frequency attack, which can directly exploit the regular and interpretable perturbations to sharply disrupt skeleton detection models. Frequency attack consists of an excitatory-inhibition waveform with high frequency attribution, which confuses edge-sensitive convolutional filters due to the sudden contrast between crests and troughs. Our comprehensive results verify that skeleton detection models are also vulnerable to adversarial attacks. The meaningful findings will inspire researchers to explore more potential robust models by involving explicit skeleton features.

Skeleton Filter: A Self-Symmetric Filter for Skeletonization in Noisy Text Images.

Robustly computing the skeletons of objects in natural images is difficult due to the large variations in shape boundaries and the large amount of noise in the images. Inspired by recent findings in neuroscience, we propose the Skeleton Filter, which is a novel model for skeleton extraction from natural images. The Skeleton Filter consists of a pair of oppositely oriented Gabor-like filters; by applying the Skeleton Filter in various orientations to an image at multiple resolutions and fusing the results, our system can robustly extract the skeleton even under highly noisy conditions. We evaluate the performance of our approach using challenging noisy text datasets and demonstrate that our pipeline realizes state-of-the-art performance for extracting the text skeleton. Moreover, the presence of Gabor filters in the human visual system and the simple architecture of the Skeleton Filter can help explain the strong capabilities of humans in perceiving skeletons of objects, even under dramatically noisy conditions.