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In this paper, we propose a weakly-supervised approach for 3D object detection, which makes it possible to train a strong 3D detector with position-level annotations (i.e. annotations of object centers and categories). In order to remedy the information loss from box annotations to centers, our method makes use of synthetic 3D shapes to convert the position-level annotations into virtual scenes with box-level annotations, and in turn utilizes the fully-annotated virtual scenes to complement the real labels. Specifically, we first present a shape-guided label-enhancement method, which assembles 3D shapes into physically reasonable virtual scenes according to the coarse scene layout extracted from position-level annotations. Then we transfer the information contained in the virtual scenes back to real ones by applying a virtual-to-real domain adaptation method, which refines the annotated object centers and additionally supervises the training of detector with the virtual scenes. Since the shape-guided label enhancement method generates virtual scenes by human-heuristic physical constraints, the layout of the fixed virtual scenes may be unreasonable with varied object combinations. To address this, we further present differentiable label enhancement to optimize the virtual scenes including object scales, orientations and locations in a data-driven manner. Moreover, we further propose a label-assisted self-training strategy to fully exploit the capability of detector. By reusing the position-level annotations and virtual scenes, we fuse the information from both domains and generate box-level pseudo labels on the real scenes, which enables us to directly train a detector in fully-supervised manner. Extensive experiments on the widely used ScanNet and Matterport3D datasets show that our approach surpasses current weakly-supervised and semi-supervised methods by a large margin, and achieves comparable detection performance with some popular fully-supervised methods with less than 5% of the labeling labor.

In this article, we propose extremely low-precision vision transformers called Quantformer for efficient inference. Conventional network quantization methods directly quantize weights and activations of fully-connected layers without considering properties of transformer architectures. Quantization sizably deviates the self-attention compared with full-precision counterparts, and the shared quantization strategy for diversely distributed patch features causes severe quantization errors. To address these issues, we enforce the self-attention rank in quantized transformers to mimic that in full-precision counterparts with capacity-aware distribution for information retention, and quantize patch features with group-wise discretization strategy for quantization error minimization. Specifically, we efficiently preserve the self-attention rank consistency by minimizing the distance between the self-attention in quantized and real-valued transformers with adaptive concentration degree, where the optimal concentration degree is selected according to the self-attention entropy for model capacity adaptation. Moreover, we partition patch features in different dimensions with differentiable group assignment, so that features in different groups leverage various discretization strategies with minimal rounding and clipping errors. Experimental results show that our Quantformer outperforms the state-of-the-art network quantization methods by a sizable margin across various vision transformer architectures in image classification and object detection. We also integrate our Quantformer with mixed-precision quantization to further enhance the performance of the vanilla models.