In the realm of deep neural network optimization, network slimming has emerged as a key technique for reducing model size without significantly compromising performance. Traditional pruning algorithms use neural architecture search (NAS) to identify networks with adjustable widths, focusing on extracting representative subnets across varied pruning ratios. However, a significant challenge lies in ensuring that the pruned network maintains high performance (accuracy) while substantially reducing the model size and computational cost. To tackle this challenge, we introduce GCPNet, a gradient-aware channel pruning network designed for efficient neural network slimming. Specifically, GCPNet incorporates a bilateral coupled sampling strategy (BCSS) to sample the smallest, largest, and several middle-sized models and perform forward and backward passes in each training iteration. The gradients of these models are then fused to update the overarching supernet. In addition, we develop a gradient-aware homogenization technique (GHT) to mitigate gradient conflicts between the supernet and the sampled models due to differing gradient directions , this accelerates the convergence of the supernet and ensures comprehensive training for GCPNet. The trained supernet serves as a reliable performance indicator, with the performance of architectures ranked by our supernet exhibiting a high correlation with true performance. Extensive experiments validate that GCPNet outperforms current advanced channel pruning approaches on the ImageNet dataset while employing comparable FLOPs and parameters. For example, under the constraints of 100 Flops and 50 Flops, our pruned MobilenetV2 achieved 68.7% and 63.5% Top-1 accuracy on the ImageNet dataset, outperforming the most advanced BCNet by 0.7% and 0.8% respectively.

Point cloud registration, which estimates a rigid transformation matrix between two point clouds, is a fundamental process in numerous applications. While existing detector-free techniques present exceptional performance, they overlook the extraction of hybrid local features that capture correlations between points and their neighbours, thereby limiting the quality of point cloud recognition. Moreover, these approaches typically treat point clouds as sequential data and employ the transformer to integrate global context from all points, which inevitably introduces interference from irrelevant regions, hence affecting the registration accuracy. In this work, we propose a novel detector-free approach AGHL to address these challenges. For the first issue, AGHL introduces a hybrid local feature perception module that designs two parallel branches to concurrently extract low-level and high-level local features, which effectively encode the correlations between each point and its neighborhood points in both Euclidean space and high-dimensional feature space. For the second issue, AGHL develops an anchor-guided cross attention that adheres to the local geometric consistency to constrain the network's attention on reliable anchors, thereby effectively suppressing interference from irrelevant regions. Benefiting from these techniques, AGHL achieves impressive point cloud registration accuracy across all synthetic, indoor, and outdoor datasets. Furthermore, we build an experimental platform and conduct a real-world robot localization experiment, with results showing the strong generalization ability of AGHL.

We present CO-Net++, a cohesive framework that optimizes multiple point cloud tasks collectively across heterogeneous dataset domains with a two-stage feature rectification strategy. The core of CO-Net++ lies in optimizing task-shared parameters to capture universal features across various tasks while discerning task-specific parameters tailored to encapsulate the unique characteristics of each task. Specifically, CO-Net++ develops a two-stage feature rectification strategy (TFRS) that distinctly separates the optimization processes for task-shared and task-specific parameters. At the first stage, TFRS configures all parameters in backbone as task-shared, which encourages CO-Net++ to thoroughly assimilate universal attributes pertinent to all tasks. In addition, TFRS introduces a sign-based gradient surgery to facilitate the optimization of task-shared parameters, thus alleviating conflicting gradients induced by various dataset domains. In the second stage, TFRS freezes task-shared parameters and flexibly integrates task-specific parameters into the network for encoding specific characteristics of each dataset domain. CO-Net++ prominently mitigates conflicting optimization caused by parameter entanglement, ensuring the sufficient identification of universal and specific features. Extensive experiments reveal that CO-Net++ realizes exceptional performances on both 3D object detection and 3D semantic segmentation tasks. Moreover, CO-Net++ delivers an impressive incremental learning capability and prevents catastrophic amnesia when generalizing to new point cloud tasks.