The placement of analog Integrated Circuits (ICs) plays a critical role in their physical design. The objective is to minimize the Half-Perimeter Wire Length (HPWL) while satisfying complex analog IC constraints, such as symmetry. Unlike digital ICs, analog ICs are highly sensitive to parasitic effects, making device symmetry crucial for optimal circuit performance. However, existing methods, including both machine learning-based and analytical approaches, struggle to meet strict symmetry constraints. In machine learning-based methods, training a general model is challenging due to the limited diversity of the training data. In analytical methods, the difficulty lies in formulating symmetry constraints as a convex function, which is necessary for gradient-based optimization of the placement. To address the issue, we formulate the placement process as a Markov decision process and propose SACPlace, a multi-agent deep reinforcement learning method for Symmetry-Aware analog Circuit Placement. SACPlace initially extracts layout information and various constraints as the input information for placement refinement and evaluation. Subsequently, SACPlace constructs multi-agent policy networks for symmetry-aware placement by refining placement guided by the evaluation of optimal symmetry quality. Following this, SACPlace constructs multilayer perceptron-based critic networks to embed placement information for evaluating symmetry quality. This evaluation reward will be used for guiding placement refinement. Experimental results from four public analog ICs instances demonstrate that our method achieves the lowest actual wirelength and area while fully satisfying symmetry and common constraints, outperforming state-of-the-art methods. Additionally, simulation results on real-world analog ICs show better performance than these methods and even manual designs.

Blind face restoration is a challenging task due to the unknown and complex degradation. Although face prior-based methods and reference-based methods have recently demonstrated high-quality results, the restored images tend to contain over-smoothed results and lose identity-preserved details when the degradation is severe. It is observed that this is attributed to short-range dependencies, the intrinsic limitation of convolutional neural networks. To model long-range dependencies, we propose a Transformer-based blind face restoration method, named BFRFormer, to reconstruct images with more identity-preserved details in an end-to-end manner. In BFRFormer, to remove blocking artifacts, the wavelet discriminator and aggregated attention module are developed, and spectral normalization and balanced consistency regulation are adaptively applied to address the training instability and over-fitting problem, respectively. Extensive experiments show that our method outperforms state-of-the-art methods on a synthetic dataset and four real-world datasets. The source code, Casia-Test dataset, and pre-trained models is released at https://github.com/s8Znk/BFRFormer.

Face restoration models are usually trained on synthetic degraded data to output an image that matches the clean version of itself. Most previous methods use a single model to deal with all the degradation levels, resulting in a domain generalization problem. We explore the value of degradation information and propose a Degradation Conditioned GAN (DeCGAN). The architecture consists of modulated convolution, bias, and fusion modules, inspired by deblurring, denoising and super-resolution. The whole network can be modulated by the degradation levels to achieve delicate and precise restoration effects. Experiments are conducted on conventional and modulated face restoration tasks. DeCGAN can achieve more faithful restoration and better metrics (FID, LPIPS, etc.) than previous methods do. Moreover, our model performs well on real-world low-quality face images.

Transformers have achieved great success in various tasks, especially that introducing pure Transformers into video understanding shows powerful performance. However, video Transformer suffers from the problem of memory explosion: it is difficult to be deployed on hardware due to the intensive computation. To address this issue, we propose ST-shift (spatial-temporal) operation with zero computation and zero parameter. We are only shifting a small portion of the channels along the temporal and spatial dimensions. Based on this operation, we build an attention-free ShiftFormer, where ST-shift blocks substitute the attention layers in video Transformer. ShiftFormer is accurate and efficient: it can reduce 56.34% of memory usage and achieve 3.41× faster training. When both using random initialization, our model performs even better than Video Swin Transformer for video recognition on Something-Something v2.

Vision-Language Pre-training (VLP) has been shown to be an efficient method to improve the performance of models on different vision-and-language downstream tasks. Substantial studies have shown that neural networks may be able to learn some general rules about language and visual concepts from a large-scale weakly labeled image-text dataset. However, most of the public cross-modal datasets that contain more than 100M image-text pairs are in English; there is a lack of available large-scale and high-quality Chinese VLP datasets. In this work, we propose a new framework for automatic dataset acquisition and cleaning with which we construct a new large-scale and high-quality cross-modal dataset named as TaiSu, containing 166 million images and 219 million Chinese captions. Compared with the recently released Wukong dataset, our dataset is achieved with much stricter restrictions on the semantic correlation of image-text pairs. We also propose to combine texts collected from the web with texts generated by a pre-trained image-captioning model. To the best of our knowledge, TaiSu is currently the largest publicly accessible Chinese cross-modal dataset. Furthermore, we test our dataset on several vision-language downstream tasks. TaiSu outperforms BriVL by a large margin on the zero-shot image-text retrieval task and zero-shot image classification task. TaiSu also shows better performance than Wukong on the image-retrieval task without using image augmentation for training. Results demonstrate that TaiSu can serve as a promising VLP dataset, both for understanding and generative tasks. More information can be referred to https://github.com/ksOAn6g5/TaiSu.