Table question answering (TQA) requires accurate retrieval and reasoning over tabular data.Existing approaches attempt to retrieve queryrelevant content before leveraging large language models (LLMs) to reason over long tables.However, these methods often fail to accurately retrieve contextually relevant data which results in information loss, and suffer from excessive encoding overhead.In this paper, we propose TALON, a multi-agent framework designed for question answering over long tables.TALON features a planning agent that iteratively invokes a tool agent to access and manipulate tabular data based on intermediate feedback, which progressively collects necessary information for answer generation, while a critic agent ensures accuracy and efficiency in tool usage and planning.In order to comprehensively assess the effectiveness of TALON, we introduce two benchmarks derived from the WikiTableQuestion and BIRD-SQL datasets, which contain tables ranging from 50 to over 10,000 rows.Experiments demonstrate that TALON achieves average accuracy improvements of 7.5% and 12.0% across all language models, establishing a new state-of-the-art in long-table question answering.Our code is publicly available

Image compression methods are usually optimized isolatedly for human perception or machine analysis tasks. We reveal fundamental commonalities between these objectives: preserving accurate semantic information is paramount, as it directly dictates the integrity of critical information for intelligent tasks and aids human understanding. Concurrently, enhanced perceptual quality not only improves visual appeal but also, by ensuring realistic image distributions, benefits semantic feature extraction for machine tasks. Based on this insight, we propose Diff-ICMH, a generative image compression framework aiming for harmonizing machine and human vision in image compression. It ensures perceptual realism by leveraging generative priors and simultaneously guarantees semantic fidelity through the incorporation of Semantic Consistency loss (SC loss) during training. Additionally, we introduce the Tag Guidance Module (TGM) that leverages highly semantic image-level tags to stimulate the pre-trained diffusion model's generative capabilities, requiring minimal additional bit rates. Consequently, Diff-ICMH supports multiple intelligent tasks through a single codec and bitstream without any task-specific adaptation, while preserving high-quality visual experience for human perception. Extensive experimental results demonstrate Diff-ICMH's superiority and generalizability across diverse tasks, while maintaining visual appeal for human perception.

In the Internet era, the explosive growth of media data processing poses significant challenges for the research of Image Coding for Machines (ICM) in improving the efficiency of AI models while reducing the burdens of data storage and transmission. Existing ICM methods face challenges in achieving sufficient generalization ability when developing a single codec to handle diverse downstream tasks. To address these issues, we propose a unified ICM framework that facilitates diverse downstream tasks with a novel importance allocation mechanism. Equipped with a spatially variable-rate image compression codec, we introduce two options: online updating and offline predicting the non-uniform quality map, which governs the quality distribution of reconstructed images based on specific downstream tasks. Our proposed method is rigorously evaluated through extensive experiments on diverse and comprehensive fine-grained image classification datasets. The experiment results conclusively demonstrate the effectiveness of the proposed method in achieving a superior rate-distortion trade-off for ICM.