Band selection has a great impact on the spectral recovery quality. To solve this ill-posed inverse problem, most band selection methods adopt hand-crafted priors or exploit clustering or sparse regularization constraints to find most prominent bands. These methods are either very slow due to the computational cost of repeatedly training with respect to different selection frequencies or different band combinations. Many traditional methods rely on the scene prior and thus are not applicable to other scenarios. In this paper, we present a novel one-shot Neural Band Selection (NBS) framework for spectral recovery. Unlike conventional searching approaches with a discrete search space and a non-differentiable search strategy, our NBS is based on the continuous relaxation of the band selection process, thus allowing efficient band search using gradient descent. To enable the compatibility for selecting any number of bands in one-shot, we further exploit the band-wise correlation matrices to progressively suppress similar adjacent bands. Extensive evaluations on the NTIRE 2022 Spectral Reconstruction Challenge demonstrate that our NBS achieves consistent performance gains over competitive baselines when examined with four different spectral recovery methods. Our code will be publicly available.

Current one-step multi-object tracking and segmentation (MOTS) methods lag behind recent two-step methods. By separating the instance segmentation stage from the tracking stage, two-step methods can exploit non-video datasets as extra data for training instance segmentation. Moreover, instances belonging to different IDs on different frames, rather than limited numbers of instances in raw consecutive frames, can be gathered to allow more effective hard example mining in the training of trackers. In this paper, we bridge this gap by presenting a novel data augmentation strategy named continuous copy-paste (CCP). Our intuition behind CCP is to fully exploit the pixel-wise annotations provided by MOTS to actively increase the number of instances as well as unique instance IDs in training. Without any modifications to frameworks, current MOTS methods achieve significant performance gains when trained with CCP. Based on CCP, we propose the first effective one-stage online MOTS method named CCPNet, which generates instance masks as well as the tracking results in one shot. Our CCPNet surpasses all state-of-the-art methods by large margins (3.8% higher sMOTSA and 4.1% higher MOTSA for pedestrians on the KITTI MOTS Validation) and ranks 1st on the KITTI MOTS leaderboard. Evaluations across three datasets also demonstrate the effectiveness of both CCP and CCPNet. Our codes are publicly available at: https://github.com/detectRecog/CCP.

3D object detection is an essential task in autonomous driving and robotics. Though great progress has been made, challenges remain in estimating 3D pose for distant and occluded objects. In this paper, we present a novel framework named ZoomNet for stereo imagery-based 3D detection. The pipeline of ZoomNet begins with an ordinary 2D object detection model which is used to obtain pairs of left-right bounding boxes. To further exploit the abundant texture cues in rgb images for more accurate disparity estimation, we introduce a conceptually straight-forward module – adaptive zooming, which simultaneously resizes 2D instance bounding boxes to a unified resolution and adjusts the camera intrinsic parameters accordingly. In this way, we are able to estimate higher-quality disparity maps from the resized box images then construct dense point clouds for both nearby and distant objects. Moreover, we introduce to learn part locations as complementary features to improve the resistance against occlusion and put forward the 3D fitting score to better estimate the 3D detection quality. Extensive experiments on the popular KITTI 3D detection dataset indicate ZoomNet surpasses all previous state-of-the-art methods by large margins (improved by 9.4% on APbv (IoU=0.7) over pseudo-LiDAR). Ablation study also demonstrates that our adaptive zooming strategy brings an improvement of over 10% on AP3d (IoU=0.7). In addition, since the official KITTI benchmark lacks fine-grained annotations like pixel-wise part locations, we also present our KFG dataset by augmenting KITTI with detailed instance-wise annotations including pixel-wise part location, pixel-wise disparity, etc.. Both the KFG dataset and our codes will be publicly available at https://github.com/detectRecog/ZoomNet.

License plate recognition is an essential step in automatic license plate recognition since it is a key technology to recognize detected license plates. Though there are extensive researches on license plate recognition, it is still challenging to recognize license plates under conditions like great tilt angles, uneven illuminations, and distortions. Based on the observation that an accurate shape correction can significantly improve the recognition accuracy on these images, this paper proposes a robust methodology named LCR for license plate recognition free of conventional image analysis operations. This approach is based on three neural networks for three different purposes: (i) predicting the locations of four vertices; (ii) predicting cutting locations; (iii) character classification. To the best of our knowledge, LCR is the first to address shape correction by designing neural networks to accurately predict the coordinates of license plates vertices. Experiments on over 250,000 unique images show that LCR significantly outperforms several state-of-the-art license plate recognition approaches. Moreover, in evaluations, the application of shape correction significantly improve the recognition accuracy.

For online conversations with top privacy, we often need to erase the existing contact behavior. Thus we want communications in which adversaries can not link you to the person you contact, namely communications with unlinkability. However, most current communication systems including variations of Mix networks fail to maintain unlinkability against global active adversaries (GAA) who can monitor global traffic and easily compromise clients and infrastructures. Therefore, designing an unlinkable communication system against GAA is challenging. By analyzing limitations of current communication systems, we propose two other features to assure unlinkability: covertness and deniability. In this paper, we design HTor, a novel and practical communication system with unlinkability, via a single web server. HTor interpolates the server to cut off the direct connection between two people in one communication and exploits covert channels (CCs) to hide communications between clients and the server. Considering servers might be corrupted, HTor utilizes a group mechanism to protect the receiver for each message. By extensive large-scale evaluations, we show that communications over HTor are robust and difficult to detect. Besides, HTor is easily implemented and, with multiple servers, it can provide enough bandwidth and relatively low latency for chatting.