Accurately recovering the dense 3D mesh of both hands from monocular images poses considerable challenges due to occlusions and projection ambiguity. Most of the existing methods extract features from color images to estimate the root-aligned hand meshes, ...

In contrast to fully supervised methods using pixel-wise mask labels, box-supervised instance segmentation takes advantage of simple box annotations, which has recently attracted increasing research attention. This paper presents a novel single-shot ...

Weakly-supervised segmentation with label-efficient sparse annotations has attracted increasing research attention to reduce the cost of laborious pixel-wise labeling process, while the pairwise affinity modeling techniques play an essential role in this ...

In recommender systems, click behaviors play a fundamental role in mining users’ interests and training models (clicked items as positive samples). Such signals are implicit feedback and are arguably less representative of users’ inherent interests. Most ...

A backdoor attack is executed by injecting a few poisoned samples into the training dataset of Deep Neural Networks (DNNs), enabling attackers to implant a hidden manipulation. This manipulation can be triggered during inference to exhibit controlled ...

Due to the high diversity and complexity of body shapes, it is challenging to directly estimate the human geometry from a single image with the various clothing styles. Most of the model-based approaches are limited to predict the shape and pose ...

In this paper, we consider the challenging task of simultaneously locating and recovering multiple hands from a single 2D image. Previous studies either focus on single hand reconstruction or solve this problem in a multi-stage way. Moreover, the ...

• A single-stage framework for multi-hand 3D reconstruction.
• End-to-end training and requires no additional third-party detectors.
• Multi-hand data generation under weak supervision.
• Advantages in prediction accuracy and inference ...

Although having achieved the promising results on shape and color recovery through self-supervision, the multi-layer perceptrons-based methods usually suffer from heavy computational cost on learning the deep implicit surface representation. Since ...

Semantic segmentation on driving-scene images is vital for autonomous driving. Although encouraging performance has been achieved on daytime images, the performance on nighttime images are less satisfactory due to the insufficient exposure and the lack of ...

With the development of advanced driver assistance systems (ADAS) and autonomous vehicles, conducting experiments in various scenarios becomes an urgent need. Although having been capable of synthesizing photo-realistic street scenes, conventional image-...

In contrast to the fully supervised methods using pixel-wise mask labels, box-supervised instance segmentation takes advantage of the simple box annotations, which has recently attracted a lot of research attentions. In this paper, we propose a ...

At this blooming age of social media and mobile platform, mass consumers are migrating from horizontal video to vertical contents delivered on hand-held devices. Accordingly, revitalizing the exposure of horizontal video becomes vital and urgent, which is ...

Parsing building facades into procedural grammars plays an important role for 3D building model generation tasks, which have been long desired in computer vision. Deep learning is a promising approach to facade parsing, however, a straightforward solution ...

In this paper, we propose to investigate the problem of out-of-domain visio-linguistic pretraining, where the pretraining data distribution differs from that of downstream data on which the pretrained model will be fine-tuned. Existing methods for this ...

Recent years have witnessed promising results of exploring deep convolutional neural network for face detection. Despite making remarkable progress, face detection in the wild remains challenging especially when detecting faces at ...

Recent years have witnessed an unprecedented growing of sport videos, as different types of sports activities can be widely-observed (i.e., from professional athletics to personal fitness). Existing approaches by computer vision have predominantly ...

Accurate pose analysis in sport videos is beneficial to users to improve skills. In this paper, we propose an AI coach system to provide personalized athletic training experiences for posture-wise sports activities, in which the training quality largely ...

Most of existing correlation filter-based tracking approaches only estimate simple axis-aligned bounding boxes, and very few of them is capable of recovering the underlying similarity transformation. To tackle this challenging problem, in this paper, we ...

Image segmentation is a long-standing challenge in image and video processing. The method of co-segmentation aims at discovering common foreground object shared in image set. The traditional co-segmentation methods usually assume that all images should ...

Recently, discriminative correlation filter (DCF) has been wildly studied and adopted in visual object tracking task. Since the convolution operation can be efficiently computed through fast Fourier transform (FFT), DCF trackers achieve the outstanding ...