We printed a checkerboard with a 9x10 grid of blocks, each measuring 87 mm x 87 mm. Parameter V alue Model Architecture Panoptic-PolarNet Test Batch Size 2 V al Batch Size 2 Test Batch size 1 post proc threshold 0.1 post proc nms kernel 5 post proc top k 100 center loss MSE offset loss L1 center loss weight 100 offset loss weight 10 enable SAP True SAP start epoch 30 SAP rate 0.01 Table 3: Parameters for Panoptic Segmentation model Parameter V alue(s) Model Architecture 4D-StOP Learning Rate 0.0005 Momentum 0.98 Stride 1 Max in points 5000 Sampling importance Decay Sampling None Input Threads 16 Checkpoint Gap 100 Table 4: Parameters for the 4D Panoptic Segmentation model The results reveal a significant variance in performance across different categories. Notably, 'Structure' and'Ground' both achieved high mIoU at Result The results are shown in Table 8. presents the mean intersection-over-union (mIoU) percent-56 Notably, 'Structure' achieved the highest mIoU at'General Objects' category have the lowest mIoU, The dataset is divided into 17 and 6 categories, respectively. Ground' and'Roads', as opposed to grouping anything related to ground as a single category. Overall, the performance across these tasks underscores the challenges posed by our dataset's With our dataset, future work can focus on improving the model's capacity to handle such diverse The raw data, processed data, and framework code can be found on our website.

Recent years have witnessed significant advancements made in the field of unsupervised domain adaptation for semantic segmentation. Depth information has been proved to be effective in building a bridge between synthetic datasets and real-world datasets. However, the existing methods may not pay enough attention to depth distribution in different categories, which makes it possible to use them for further improvement. Besides the existing methods that only use depth regression as an auxiliary task, we propose to use depth distribution density to support semantic segmentation. Therefore, considering the relationship among depth distribution density, depth and semantic segmentation, we also put forward a branch balance loss for these three subtasks in multi-task learning schemes. In addition, we also propose a spatial aggregation priors of pixels in different categories, which is used to refine the pseudo-labels for self-training, thus further improving the performance of the prediction model. Experiments on SYNTHIA-to-Cityscapes and SYNTHIA-to-Mapillary benchmarks show the effectiveness of our proposed method.

The paper presents C3T (Cross-modal Capabilities Conservation Test), a new benchmark for assessing the performance of speech-aware large language models. The benchmark utilizes textual tasks and a voice cloning text-to-speech model to quantify the extent to which language understanding capabilities are preserved when the model is accessed via speech input. C3T quantifies the fairness of the model for different categories of speakers and its robustness across text and speech modalities.

We printed a checkerboard with a 9x10 grid of blocks, each measuring 87 mm x 87 mm. Parameter V alue Model Architecture Panoptic-PolarNet Test Batch Size 2 V al Batch Size 2 Test Batch size 1 post proc threshold 0.1 post proc nms kernel 5 post proc top k 100 center loss MSE offset loss L1 center loss weight 100 offset loss weight 10 enable SAP True SAP start epoch 30 SAP rate 0.01 Table 3: Parameters for Panoptic Segmentation model Parameter V alue(s) Model Architecture 4D-StOP Learning Rate 0.0005 Momentum 0.98 Stride 1 Max in points 5000 Sampling importance Decay Sampling None Input Threads 16 Checkpoint Gap 100 Table 4: Parameters for the 4D Panoptic Segmentation model The results reveal a significant variance in performance across different categories. Notably, 'Structure' and'Ground' both achieved high mIoU at Result The results are shown in Table 8. presents the mean intersection-over-union (mIoU) percent-56 Notably, 'Structure' achieved the highest mIoU at'General Objects' category have the lowest mIoU, The dataset is divided into 17 and 6 categories, respectively. Ground' and'Roads', as opposed to grouping anything related to ground as a single category. Overall, the performance across these tasks underscores the challenges posed by our dataset's With our dataset, future work can focus on improving the model's capacity to handle such diverse The raw data, processed data, and framework code can be found on our website.

We utilize the same experimental setup as the "High-quality Fifty-participant Human

Data-free knowledge distillation (DFKD) is an effective manner to solve model compression and transmission restrictions while retaining privacy protection, which has attracted extensive attention in recent years. Currently, the majority of existing methods utilize a generator to synthesize images to support the distillation. Although the current methods have achieved great success, there are still many issues to be explored. Firstly, the outstanding performance of supervised learning in deep learning drives us to explore a pseudo-supervised paradigm on DFKD. Secondly, current synthesized methods cannot distinguish the distributions of different categories of samples, thus producing ambiguous samples that may lead to an incorrect evaluation by the teacher. Besides, current methods cannot optimize the category-wise diversity samples, which will hinder the student model learning from diverse samples and further achieving better performance. In this paper, to address the above limitations, we propose a novel learning paradigm, i.e., conditional pseudo-supervised contrast for data-free knowledge distillation (CPSC-DFKD). The primary innovations of CPSC-DFKD are: (1) introducing a conditional generative adversarial network to synthesize category-specific diverse images for pseudo-supervised learning, (2) improving the modules of the generator to distinguish the distributions of different categories, and (3) proposing pseudo-supervised contrastive learning based on teacher and student views to enhance diversity. Comprehensive experiments on three commonly-used datasets validate the performance lift of both the student and generator brought by CPSC-DFKD. The code is available at https://github.com/RoryShao/CPSC-DFKD.git Keywords: model compression, knowledge distillation, representation learning, contrastive learning, privacy protection1. Introduction With the development of artificial intelligence, the deep con-volutional neural networks (DCNNs) have been widely applied in various computer vision tasks and achieved remarkable success, such as image classification [1], object detection [2], and semantic segmentation [3]. Nevertheless, in practical applications, DCNNs suffer from some heavy issues. Firstly, DCNNs always require heavy computation and storage. For example, only to handle one image, a VGG network commonly requires more than 500MB of memory, which makes them hard to be deployed on resource-constrained embedded or edge devices such as mobile phones and autonomous cars.