Electromigration-related reliability concerns in very large-scale integration (VLSI) circuits have garnered increasing attention as technology continues to scale. As integrated circuits shrink and their density rises, solving Korhonen's equation for the multi-segment interconnect line model becomes increasingly challenging. Recent advances in neural network-based approaches have demonstrated notable efficacy in addressing differential equations arising in physical modeling frameworks. Inspired by Physics-Informed Graph Neural Network (PIGNN) methodologies, we propose a novel Physics-Informed Message Passing (PIMNEM) architecture designed to solve coupled multi-domain Korhonen equations. At the same time, we introduce AdaptEM, which incorporates a graph partitioning mechanism with a hierarchical training strategy and employs the PIM-NEM architecture as a subgraph computation unit. AdaptEM enables multi-scale decomposition of interconnected circuits and facilitates hierarchical unsupervised learning via its hierarchical architecture. Unsupervised training is first applied to partitioned subgraphs using the PIMP mechanism, followed by global graph fine-tuning, where inter-subgraph boundary constraints are explicitly enforced through differentiable penalty terms. AdaptEM achieves a 20× speedup over FEM-based methods at the cost of about 0.5% accuracy loss. While AdaptEM may not match the absolute computational speed of state-of-the-art EM tools, its end-to-end unsupervised training framework, enhanced by a hierarchical subgraph training strategy, offers superior generalization capabilities and greater tuning flexibility.

Video moment retrieval locates a specified moment by a sentence query. Recent approaches have made remarkable advancements with large-scale video-sentence annotations. These annotations require extensive human labor and expertise, leading to the need for unsupervised fashion. Generating pseudo-supervision from videos is an effective strategy. With the power of the large-scale pre-trained model, we introduce knowledge into constructing pseudo-supervision. The main technical challenge is improving pseudo-supervision diversity and alleviating noise brought by external knowledge. To address these problems, we propose two Knowledge-Based Pseudo-Supervision Construction (KPSC) strategies: KPSC-P and KPSC-F. They all follow two steps: generating diverse samples and alleviating knowledge chaos. The main difference is that the former first learns a representation space with prompt tuning, while the latter directly utilizes data information. KPSC-P has two modules: (1) Proposal Prompt (PP): Generate temporal proposals; (2) Verb Prompt (VP): Generate pseudo-queries with noun-verb patterns. KPSC-F also has two modules: (1) Captioner: Generating candidate queries; (2) Filter: Alleviating knowledge chaos. Thus, our KPSC involves two attempts to extract knowledge from pre-trained models. Extensive experiments show that our attempts outperform the existing unsupervised methods on two public datasets (Charades-STA and ActivityNet-Captions) and perform on par with several methods using stronger supervision.

In recent years, learnable prompts have emerged as a major prompt learning paradigm, enhancing the performance of large-scale vision-language pre-trained models in few-shot image classification. However, enhancing methods are often time-consuming and inflexible because 1) class-specific prompts are inefficient in certain situations; 2) instance-specific prompts are put in a fixed position. To address these issues, inspired by the coarse-to-fine decision-making paradigm of human, we propose an Instance-Aware Hierarchical-Structured Policy (IAHSP) that integrates instance-specific prompt selection and appropriate position selection using a reinforcement learning fashion. Specifically, IAHSP consists of two sub-policies: 1) the root policy selects the most suitable prompt from the prompts pool, and 2) the leaf policy identifies the optimal position for inserting the selected prompt. We train these two policies iteratively with rewards constraining the prompts while maintaining their diversity. Extensive experiments on 11 public benchmarks demonstrate that our IAHSP significantly boosts the few-shot image classification performance of vision-language pre-trained models, while also exhibiting superior generalization performance.

Video moment retrieval aims at localizing a specific moment from an untrimmed video by a sentence query. Most methods rely on heavy annotations of video moment-query pairs. Recent zero-shot methods reduced annotation cost, yet they neglected the global visual feature due to the separation of video and text learning process. To avoid the lack of visual features, we propose a Prompt-based Zero-shot Video Moment Retrieval (PZVMR) method. Motivated by the frame of prompt learning, we design two modules: 1) Proposal Prompt (PP): We randomly masks sequential frames to build a prompt to generate proposals; 2) Verb Prompt (VP): We provide patterns of nouns and the masked verb to build a prompt to generate pseudo queries with verbs. Our PZVMR utilizes task-relevant knowledge distilled from pre-trained CLIP and adapts the knowledge to VMR. Unlike the pioneering work, we introduce visual features into each module. Extensive experiments show that our PZVMR not only outperforms the existing zero-shot method (PSVL) on two public datasets (Charades-STA and ActivityNet-Captions) by 4.4% and 2.5% respectively in mIoU, but also outperforms several methods using stronger supervision.