Large language models (LLM) have achieved remarkable performance across a wide range of tasks. However, their substantial parameter sizes pose significant challenges for deployment on edge devices with limited computational and memory resources. Low-rank compression is a promising approach to address this issue, as it reduces both computational and memory costs, making LLM more suitable for resource-constrained environments. Nonetheless, naïve low-rank compression methods require a significant reduction in the retained rank to achieve meaningful memory and computation savings. For a low-rank model, the ranks need to be reduced by more than half to yield efficiency gains. Such aggressive truncation, however, typically results in substantial performance degradation. To address this trade-off, we propose SkipCat, a novel low-rank compression framework that enables the use of higher ranks while achieving the same compression rates. First, we introduce an intra-layer shared low-rank projection method, where multiple matrices that share the same input use a common projection. This reduces redundancy and improves compression efficiency. Second, we propose a block skipping technique that omits computations and memory transfers for selected sub-blocks within the low-rank decomposition. These two techniques jointly enable our compressed model to retain more effective ranks under the same compression budget. Experimental results show that, without any additional fine-tuning, our method outperforms previous low-rank compression approaches by 7% accuracy improvement on zero-shot tasks under the same compression rate. These results highlight the effectiveness of our rank-maximized compression strategy in preserving model performance under tight resource constraints.

Although large language models (LLM) have achieved remarkable performance, their enormous parameter counts hinder deployment on resource-constrained hardware.Low-rank compression can reduce both memory usage and computational demand, but applying a uniform compression ratio across all layers often leads to significant performance degradation, and previous methods perform poorly during decoding.To address these issues, we propose the Finegrained Low-Rank Compressor (FLRC), which efficiently determines an optimal rank allocation for each layer, and incorporates progressive low-rank decoding to maintain text generation quality.Comprehensive experiments on diverse benchmarks demonstrate the superiority of FLRC, achieving up to a 17% improvement in ROUGE-L on summarization tasks compared to state-of-the-art low-rank compression methods, establishing a more robust and efficient framework to improve LLM inference.

Feature correspondence is an important topic in many computer vision or robot vision tasks. Different from traditional optimization based matching method, in the last two years, researchers are finally able to solve the matching process in a learning manner. As a representative method, SuperGlue achieves superior performance in many real-world tasks, but it still has problems in dealing with outlier features. Targeting at the outlier problem, this paper improves SuperGlue by introducing a deep learning based feature correspondence method, which consists of the pruned attentional graph neural network and the improved matching layer for the outlier problem. Experiments on real world images validate the effectiveness of the proposed method.

Video sensors are widely used in many applications such as security monitoring and home care. However, the growth of the number of sensors makes it impractical to stream all videos back to a central server for further processing, due to communication bandwidth and server storage constraints. Multi-view video summarization allows us to discard redundant data in the video streams taken by a group of sensors. All prior multi-view summarization methods, however, process video data in an off-line and centralized manner, which means that all videos are still required to be streamed back to the server before conducting the summarization. This paper proposes an on-line, distributed multi-view summarization system, which integrates the ideas of Maximal Marginal Relevance (MMR) and MS-Wave, a bandwidth-efficient distributed algorithm for finding k-nearest-neighbors and k-farthest-neighbors. Empirical studies show that our proposed system can discard redundant videos and keep important keyframes as effectively as centralized approaches, while transmitting only 1/6 to 1/3 as much data.

In M2M applications, it is very common to encounter the ad hoc snapshot query that requires fast responses from many local machines in which all the data are distributed. In the scenario when the query is more complex, the communication cost for sending it to all the local machines for processing can be very high. This paper aims to address this issue. Given a reference set of multiple and large-size patterns, we propose an approach to identifying its k nearest and farthest neighbors globally across all the local machines. By decomposing the reference patterns into a multi-resolution representation and using novel distance bound designs, our method guarantees the exact results in a communication-efficient manner. Analytical and empirical studies show that our method outperforms the state-of-the-art methods in saving significant bandwidth usage, especially for large numbers of machines and large-sized reference patterns.