CSPN++: Learning Context and Resource Aware Convolutional Spatial Propagation Networks for Depth Completion

Depth Completion deals with the problem of converting a sparse depth map to a dense one, given the corresponding color image. Convolutional spatial propagation network (CSPN) is one of the state-of-the-art (SoTA) methods of depth completion, which recovers structural details of the scene. In this paper, we propose CSPN++, which further improves its effectiveness and efficiency by learning adaptive convolutional kernel sizes and the number of iterations for the propagation, thus the context and computational resources needed at each pixel could be dynamically assigned upon requests. Specifically, we formulate the learning of the two hyper-parameters as an architecture selection problem where various configurations of kernel sizes and numbers of iterations are first defined, and then a set of soft weighting parameters are trained to either properly assemble or select from the pre-defined configurations at each pixel. In our experiments, we find weighted assembling can lead to significant accuracy improvements, which we referred to as "context-aware CSPN", while weighted selection, "resource-aware CSPN" can reduce the computational resource significantly with similar or better accuracy. Besides, the resource needed for CSPN++ can be adjusted w.r.t. the computational budget automatically. Finally, to avoid the side effects of noise or inaccurate sparse depths, we embed a gated network inside CSPN++, which further improves the performance. We demonstrate the effectiveness of CSPN++on the KITTI depth completion benchmark, where it significantly improves over CSPN and other SoTA methods.Comment: Camera Ready Version. Accepted by AAAI 202

VBLC: Visibility Boosting and Logit-Constraint Learning for Domain Adaptive Semantic Segmentation under Adverse Conditions

Generalizing models trained on normal visual conditions to target domains under adverse conditions is demanding in the practical systems. One prevalent solution is to bridge the domain gap between clear- and adverse-condition images to make satisfactory prediction on the target. However, previous methods often reckon on additional reference images of the same scenes taken from normal conditions, which are quite tough to collect in reality. Furthermore, most of them mainly focus on individual adverse condition such as nighttime or foggy, weakening the model versatility when encountering other adverse weathers. To overcome the above limitations, we propose a novel framework, Visibility Boosting and Logit-Constraint learning (VBLC), tailored for superior normal-to-adverse adaptation. VBLC explores the potential of getting rid of reference images and resolving the mixture of adverse conditions simultaneously. In detail, we first propose the visibility boost module to dynamically improve target images via certain priors in the image level. Then, we figure out the overconfident drawback in the conventional cross-entropy loss for self-training method and devise the logit-constraint learning, which enforces a constraint on logit outputs during training to mitigate this pain point. To the best of our knowledge, this is a new perspective for tackling such a challenging task. Extensive experiments on two normal-to-adverse domain adaptation benchmarks, i.e., Cityscapes -> ACDC and Cityscapes -> FoggyCityscapes + RainCityscapes, verify the effectiveness of VBLC, where it establishes the new state of the art. Code is available at https://github.com/BIT-DA/VBLC.Comment: Camera ready for AAAI 2023. Code is available at https://github.com/BIT-DA/VBL

Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation

Active learning, a label-efficient paradigm, empowers models to interactively query an oracle for labeling new data. In the realm of LiDAR semantic segmentation, the challenges stem from the sheer volume of point clouds, rendering annotation labor-intensive and cost-prohibitive. This paper presents Annotator, a general and efficient active learning baseline, in which a voxel-centric online selection strategy is tailored to efficiently probe and annotate the salient and exemplar voxel girds within each LiDAR scan, even under distribution shift. Concretely, we first execute an in-depth analysis of several common selection strategies such as Random, Entropy, Margin, and then develop voxel confusion degree (VCD) to exploit the local topology relations and structures of point clouds. Annotator excels in diverse settings, with a particular focus on active learning (AL), active source-free domain adaptation (ASFDA), and active domain adaptation (ADA). It consistently delivers exceptional performance across LiDAR semantic segmentation benchmarks, spanning both simulation-to-real and real-to-real scenarios. Surprisingly, Annotator exhibits remarkable efficiency, requiring significantly fewer annotations, e.g., just labeling five voxels per scan in the SynLiDAR-to-SemanticKITTI task. This results in impressive performance, achieving 87.8% fully-supervised performance under AL, 88.5% under ASFDA, and 94.4% under ADA. We envision that Annotator will offer a simple, general, and efficient solution for label-efficient 3D applications. Project page: https://binhuixie.github.io/annotator-webComment: NeurIPS 2023. Project page at https://binhuixie.github.io/annotator-web

Involvement of CD244 in regulating CD4+ T cell immunity in patients with active tuberculosis.

CD244 (2B4) is a member of the signaling lymphocyte activation molecule (SLAM) family of immune cell receptors and it plays an important role in modulating NK cell and CD8(+) T cell immunity. In this study, we investigated the expression and function of CD244/2B4 on CD4(+) T cells from active TB patients and latent infection individuals. Active TB patients had significantly elevated CD244/2B4 expression on M. tuberculosis antigen-specific CD4(+) T cells compared with latent infection individuals. The frequencies of CD244/2B4-expressing antigen-specific CD4(+) T cells were significantly higher in retreatment active TB patients than in new active TB patients. Compared with CD244/2B4-dull and -middle CD4(+) T cells, CD244/2B4-bright CD4(+) T cell subset had significantly reduced expression of IFN-γ, suggesting that CD244/2B4 expression may modulate IFN-γ production in M. tuberculosis antigen-responsive CD4(+) T cells. Activation of CD244/2B4 signaling by cross-linking led to significantly decreased production of IFN-γ. Blockage of CD244/2B4 signaling pathway of T cells from patients with active TB resulted in significantly increased production of IFN-γ, compared with isotype antibody control. In conclusion, CD244/2B4 signaling pathway has an inhibitory role on M. tuberculosis antigen-specific CD4(+) T cell function