Unsupervised video object segmentation aims to automatically segment moving objects over an unconstrained video without any user annotation. So far, only few unsupervised online methods have been reported in the literature, and their performance is still far from satisfactory because the complementary information from future frames cannot be processed under online setting. To solve this challenging problem, in this paper, we propose a novel unsupervised online video object segmentation (UOVOS) framework by construing the motion property to mean moving in concurrence with a generic object for segmented regions. By incorporating the salient motion detection and the object proposal, a pixel-wise fusion strategy is developed to effectively remove detection noises, such as dynamic background and stationary objects. Furthermore, by leveraging the obtained segmentation from immediately preceding frames, a forward propagation algorithm is employed to deal with unreliable motion detection and object proposals. Experimental results on several benchmark datasets demonstrate the efficacy of the proposed method. Compared to state-of-the-art unsupervised online segmentation algorithms, the proposed method achieves an absolute gain of 6.2%. Moreover, our method achieves better performance than the best unsupervised offline algorithm on the DAVIS-2016 benchmark dataset. Our code is available on the project website: https://www.github.com/visiontao/uovos.

The objective of the occluded person re-identification (ReID) task is to capture the same person from different camera angles when the pedestrian’s body is partially occluded. In this task, there are two main challenges: 1) pedestrians are often occluded by other persons or objects, and 2) pedestrians change poses. Moreover, these two issues often simultaneously occur. Although many occluded person ReID algorithms have been proposed, many existing methods can often only solve one of these issues well, and the other issue is often ignored. In this work, a novel semantic perception and CNN-transformer hybrid network (abbreviated as SPH) is proposed for occluded person ReID, which consists of a CNN-based human semantic perception stream and a transformer-based pose perception stream. In the former, a human semantic auxiliary module and a human semantic perception module are designed to obtain human semantic information where multi-granularity region features of the human body are extracted to solve the issues of occlusion. In the latter, we propose a token-based pose integration module to obtain the corresponding patch for each pose key-point and the relative position information to solve the change in pedestrian pose. Moreover, these two streams are jointly optimized in a unified framework. In addition, to further solve the issue of occlusion, the human completion strategy is proposed for the query sample where the gallery samples are used to complete the missing parts of the query. Extensive experimental results on three public occluded person ReID datasets, Occluded-DukeMTMC, P-DukeMTMC-reID, and Occluded-REID, demonstrate that the proposed method can outperform all SOTA occluded person ReID methods in terms of the mAP and Rank-1. Compared with PAT (CVPR21) on the Occluded-DukeMTMC and Occluded-REID datasets, the improvements in mAP/Rank-1 reached 10.1%/7.4%, and 10%/1%, respectively. Moreover, when TransReID (ICCV21) was used, SPH achieved improvements of 4.5% (mAP) and 5.5% (Rank-1) on the Occluded-DukeMTMC dataset.

Hand-object interaction plays a very important role when humans manipulate objects. While existing methods focus on improving hand-object recognition with fully automatic methods, human intention has been largely neglected in the recognition process, thus leading to undesirable interaction descriptions. To better interpret human-object interaction that is aligned to human intention, we argue that a reference specifying human intention should be taken into account. Thus, we propose a new approach to represent interactions while reflecting human purpose with three key factors, i.e. , hand, object and reference. Specifically, we design a pattern of < hand-object, object-reference, hand, object, reference > (HOR) to recognize intention based atomic hand-object interactions. This pattern aims to model interactions with the states of hand, object, reference and their relationships. Furthermore, we design a simple yet effective Spatially Part-based (3+1)D convolutional neural network, namely SP(3+1)D, which leverages 3D and 1D convolutions to model visual dynamics and object position changes based on our HOR, respectively. With the help of our SP(3+1)D network, the recognition results are able to indicate human purposes accurately. To evaluate the proposed method, we annotate a Something-1.3k dataset, which contains 10 atomic hand-object interactions and about 130 videos for each interaction. Experimental results on Something-1.3k demonstrate the effectiveness of our SP(3+1)D network.

Raven’s Progressive Matrices (RPM) is highly correlated with human intelligence, and it has been widely used to measure the abstract reasoning ability of humans. In this paper, to study the abstract reasoning capability of deep neural networks, we propose the first unsupervised learning method for solving RPM problems. Since the ground truth labels are not allowed, we design a pseudo target based on the prior constraints of the RPM formulation to approximate the ground-truth label, which effectively converts the unsupervised learning strategy into a supervised one. However, the correct answer is wrongly labelled by the pseudo target, and thus the noisy contrast will lead to inaccurate model training. To alleviate this issue, we propose to improve the model performance with negative answers. Moreover, we develop a decentralization method to adapt the feature representation to different RPM problems. Extensive experiments on three datasets demonstrate that our method even outperforms some of the supervised approaches. Our code is available at https://github.com/visiontao/ncd .

RipX of Ralstonia solanacearum is translocated into host cells by a type III secretion system and acts as a harpin-like protein to induce a hypersensitive response in tobacco plants. The molecular events in association with RipX-induced signaling transduction have not been fully elucidated. This work reports that transient expression of RipX induced a yellowing phenotype in Nicotiana benthamiana, coupled with activation of the defense reaction. Using yeast two-hybrid and split-luciferase complementation assays, mitochondrial ATP synthase F1 subunit α (ATPA) was identified as an interaction partner of RipX from N. benthamiana. Although a certain proportion was found in mitochondria, the YFP-ATPA fusion was able to localize to the cell membrane, cytoplasm, and nucleus. RFP-RipX fusion was found from the cell membrane and cytoplasm. Moreover, ATPA interacted with RipX at both the cell membrane and cytoplasm in vivo. Silencing of the atpA gene had no effect on the appearance of yellowing phenotype induced by RipX. However, the silenced plants improved the resistance to R. solanacearum. Moreover, qRT-PCR and promoter GUS fusion experiments revealed that the transcript levels of atpA were evidently reduced in response to expression of RipX. These data demonstrated that RipX exerts a suppressive effect on the transcription of atpA gene, to induce defense reaction in N. benthamiana.