Vectorized High-Definition (HD) map construction requires predictions of the category and point coordinates of map elements (e.g. road boundary, lane divider, pedestrian crossing, etc.). State-of-the-art methods are mainly based on point-level representation learning for regressing accurate point coordinates. However, this pipeline has limitations in obtaining element-level information and handling element-level failures, e.g. erroneous element shape or entanglement between elements. To tackle the above issues, we propose a simple yet effective HybrId framework named HIMap to sufficiently learn and interact both point-level and element-level information. Concretely, we introduce a hybrid representation called HIQuery to represent all map elements, and propose a point-element interactor to interactively extract and encode the hybrid information of elements, e.g. point position and element shape, into the HIQuery. Additionally, we present a point-element con-sistency constraint to enhance the consistency between the point-level and element-level information. Finally, the output point-element integrated HIQuery can be directly converted into map elements' class, point coordinates, and mask. We conduct extensive experiments and consistently outperform previous methods on both nuScenes and Argo-verse2 datasets. Notably, our method achieves 77.8 mAP on the nuScenes dataset, remarkably superior to previous SOTAs by 8.3 mAP at least.

Image generation has been attracting widespread attention in recent years along with the development of generative models. Existing works mostly focus on pursuing high-quality generated samples as a priority. In this work, we introduce a lightweight transformer-based module, called GradTrans, that provides a novel balance on the speed-performance trade-off with generative adversarial networks for image generation. GradTrans effectively leverages the instructive information in the discriminator network to guide the generator network for a higher generation quality at the inference stage without overburdening the cost. Extensive experiments are conducted for unconditional image generation task and style transfer task on diverse datasets, including CIFAR10, STL10 and Horse2Zebra, demonstrating that our proposed GradTrans can surpass different related methods with significantly superior performance, as well as being generalizable with large compatibility to different base models.

HD map construction is a fundamental and challenging task in autonomous driving to understand the surrounding environment. Recently, Camera-LiDAR BEV feature fusion methods have attracted increasing attention in HD map construction task, which can significantly boost the benchmark. However, existing fusion methods ignore modal interaction and utilize very simple fusion strategy, which suffers from the problems of misalignment and information loss. To tackle this, we propose a novel Multi-modal BEV feature fusion method named MBFusion. Specifically, to solve the semantic misalignment problem between Camera and LiDAR features, we design Cross-modal Interaction Transform (CIT) module to make these two feature spaces interact knowledge with each other to enhance the feature representation by the cross-attention mechanism. Then, we propose a Dual Dynamic Fusion (DDF) module to automatically select valuable information from different modalities for better feature fusion. Moreover, MBFusion is simple, and can be plug-and-played into existing pipelines. We evaluate MBFusion on three architectures, including HDMapNet, VectorMapNet, and MapTR, to show its versatility and effectiveness. Compared with the state-of-the-art methods, MBFusion achieves 3.6% and 4.1% absolute improvements on mAP on the nuScenes and the Argoverse2 datasets, respectively, demonstrating the superiority of our method.

Deploying deep convolutional neural networks on ultra-low power systems is challenging due to the extremely limited resources. Especially, the memory becomes a bottleneck as the systems put a hard limit on the size of on-chip memory. Because peak memory explosion in the lower layers is critical even in tiny models, the size of an input image should be reduced with sacrifice in accuracy. To overcome this drawback, we propose a novel Raster-Scanning Network, named RaScaNet, inspired by raster-scanning in image sensors. RaScaNet reads only a few rows of pixels at a time using a convolutional neural network and then sequentially learns the representation of the whole image using a recurrent neural network. The proposed method operates on an ultra-low power system without input size reduction; it requires 15.9–24.3× smaller peak memory and 5.3–12.9× smaller weight memory than the state-of-the-art tiny models. Moreover, RaScaNet fully exploits on-chip SRAM and cache memory of the system as the sum of the peak memory and the weight memory does not exceed 60 KB, improving the power efficiency of the system. In our experiments, we demonstrate the binary classification performance of RaScaNet on Visual Wake Words and Pascal VOC datasets.

Reducing bit-widths of activations and weights of deep networks makes it efficient to compute and store them in memory, which is crucial in their deployments to resource-limited devices, such as mobile phones. However, decreasing bit-widths with quantization generally yields drastically degraded accuracy. To tackle this problem, we propose to learn to quantize activations and weights via a trainable quantizer that transforms and discretizes them. Specifically, we parameterize the quantization intervals and obtain their optimal values by directly minimizing the task loss of the network. This quantization-interval-learning (QIL) allows the quantized networks to maintain the accuracy of the full-precision (32-bit) networks with bit-width as low as 4-bit and minimize the accuracy degeneration with further bit-width reduction (i.e., 3 and 2-bit). Moreover, our quantizer can be trained on a heterogeneous dataset, and thus can be used to quantize pretrained networks without access to their training data. We demonstrate the effectiveness of our trainable quantizer on ImageNet dataset with various network architectures such as ResNet-18, -34 and AlexNet, on which it outperforms existing methods to achieve the state-of-the-art accuracy.