Composed Image Retrieval (CIR) aims to retrieve target images from candidate set using a hybrid-modality query consisting of a reference image and a relative caption that describes the user intent. Recent studies attempt to utilize Vision-Language Pre-training Models (VLPMs) with various fusion strategies for addressing the task. However, these methods typically fail to simultaneously meet two key requirements of CIR: comprehensively extracting visual information and faithfully following the user intent. In this work, we propose CIR-LVLM, a novel framework that leverages the large vision-language model (LVLM) as the powerful user intent-aware encoder to better meet these requirements. Our motivation is to explore the advanced reasoning and instruction-following capabilities of LVLM for accurately understanding and responding the user intent. Furthermore, we design a novel hybrid intent instruction module to provide explicit intent guidance at two levels: (1) The task prompt clarifies the task requirement and assists the model in discerning user intent at the task level. (2) The instance-specific soft prompt, which is adaptively selected from the learnable prompt pool, enables the model to better comprehend the user intent at the instance level compared to a universal prompt for all instances. CIR-LVLM achieves state-of-the-art performance across three prominent benchmarks with acceptable inference efficiency. We believe this study provides fundamental insights into CIR-related fields.

Image retrieval with composed query (IR-CQ) is a challenging task since it aims to retrieve the target image according to a hybrid-modality query which consists of a reference image and a text modifier. Previous approaches mainly focus on designing various multi-modal fusion modules to fuse the hybrid-modality query, but these fusion modules are often suboptimal without considering sufficient fusion between the two modalities. In this paper, we propose a general fusion block by taking three fusion strategies: weighted summing, concatenating, and bilinear pooling. Importantly, this general fusion block can be deployed to fuse not only the hybrid-modality query but also the multi-scale features of the reference image. Specifically, we first fuse the multi-scale features of the reference image with the Multi-Scale Fusion (MSF) block and then fuse the features of the reference image and text modifier with the Multi-Modal Fusion (MMF) block, where both MSF and MMF are instantiations of our general fusion block. Extensive experiments on three benchmark datasets show that our proposed model significantly outperforms existing approaches.

With the development of video sharing websites, numerous users desire to create their own attractive video montages. However, it is difficult for inexperienced users to create well-edited video montages due to the lack of professional expertise. In the meantime, it is time-consuming even for experts to create video montages of high quality, which requires effectively selecting shots from abundant candidates and assembling them together. Instead of manual creation, various automatic methods have been proposed for video montage generation, which typically take a single sentence as input for text-to-shot retrieval, and ignore the semantic cross-sentence coherence given complicated text script of multiple sentences. To overcome this drawback, we propose a novel model for video montage generation by retrieving and assembling shots with arbitrary text scripts. To this end, a sequence consistency transformer is devised for cross-sentence coherence modeling. More importantly, with this transformer, two novel sequence-level tasks are defined for sentence-shot alignment in sequence-level: Cross-Modal Sequence Matching (CMSM) task, and Chaotic Sequence Recovering (CSR) task. To facilitate the research on video montage generation, we construct a new, highly-varied dataset which collects thousands of video-script pairs in documentary. Extensive experiments on the constructed dataset demonstrate the superior performance of the proposed model. The dataset and generated video demos are available at https://github.com/RATVDemo/RATV.

Machine learning algorithms are advocated for automated diagnosis of board-level functional failures due to the extreme complexity of the problem. Such reasoning-based solutions, however, remain ineffective at the early stage of the product cycle, simply because there are insufficient historical data for training the diagnostic system that has a large number of test syndromes. In this paper, we present a novel test syndrome merging methodology to tackle this problem. That is, by leveraging the domain knowledge of the diagnostic tests and the board structural information, we adaptively reduce the feature size of the diagnostic system by selectively merging test syndromes such that it can effectively utilize the available training cases. Experimental results demonstrate the effectiveness of the proposed solution.

Region-based multi-supply voltage (MSV) design, by which circuits are partitioned into multiple "voltage islands" and each island operates at a supply voltage that meets its own performance requirement, is an effective technique to tradeoff power and performance. Different from conventional voltage island generation techniques that work in a conservative manner to guarantee "always correct" computation, in this work, we investigate the MSV design problem for timing-speculative circuits, which achieves high energy-efficiency by allowing the occurrence of infrequent timing errors and correcting them online. A novel algorithm based on dynamic programming is developed to tackle this problem. Experimental results on various benchmark circuits demonstrate the effectiveness of the proposed methodology.

Hardware Trojans (HTs) implemented by adversaries serve as backdoors to subvert or augment the normal operation of infected devices, which may lead to functionality changes, sensitive information leakages, or Denial of Service attacks. To tackle such threats, this paper proposes a novel verification technique for hardware trust, namely VeriTrust, which facilitates to detect HTs inserted at design stage. Based on the observation that HTs are usually activated by dedicated trigger inputs that are not sensitized with verification test cases, VeriTrust automatically identifies such potential HT trigger inputs by examining verification corners. The key difference between VeriTrust and existing HT detection techniques is that VeriTrust is insensitive to the implementation style of HTs. Experimental results show that VeriTrust is able to detect all HTs evaluated in this paper (constructed based on various HT design methodologies shown in the literature) at the cost of moderate extra verification time, which is not possible with existing solutions.

Diagnosing functional failures in complicated electronic boards is a challenging task, wherein debug technicians try to identify defective components by analyzing some syndromes obtained from the application of diagnostic tests. The diagnosis effectiveness and efficiency rely heavily on the quality of the in-house developed diagnostic tests and the debug technicians' knowledge and experience, which, however, have no guarantees nowadays. To tackle this problem, we propose a novel agent-assisted diagnostic framework for board-level functional failures, namely AgentDiag, which facilitates to evaluate the quality of the diagnostic tests and bridge the knowledge gap between the diagnostic programmers who write diagnostic tests and the debug technicians who conduct in-field diagnosis with a lightweight model of the boards and tests. Experimental results on a real industrial board and an OpenRISC design demonstrate the effectiveness of the proposed solution.