AAAI 2026 Oral：明略科技开创稀疏数据「信息瓶颈动态压缩」，精度+速度双SOTA

Core Insights - The article discusses the challenges of "Efficient AI," particularly in the context of transformer models becoming larger and more general, while also becoming computationally heavy for edge devices like robots [1][2] - A paper titled "CompTrack," accepted for oral presentation at AAAI 2026, addresses the issue of whether models need to process all input data, showcasing how compression techniques can significantly reduce computational costs while maintaining or even improving model performance [2][14] Redundancy Challenges - Current AI models face "Dual-Redundancy" challenges, which include: 1. Spatial Redundancy: Unrelated background points and blank areas are processed, wasting computational resources and degrading accuracy [3][5] 2. Informational Redundancy: Even in relevant foreground targets, there is a prevalence of redundant and low-value information, which can lead to inefficiencies [5][7] CompTrack Framework - CompTrack proposes an end-to-end framework that addresses both types of redundancy simultaneously [7] - The framework includes: 1. A Spatial Foreground Predictor (SFP) that filters out low-information background noise using information entropy theory [8] 2. An Information Bottleneck-guided Dynamic Token Compression (IB-DTC) module designed to dynamically compress information redundancy in the foreground [10][11] Efficiency and Performance - The IB-DTC module is significant for Efficient AI as it: 1. Is based on the Information Bottleneck principle, retaining only valuable information for predictions [11] 2. Utilizes online Singular Value Decomposition (SVD) for dynamic compression rates based on the intrinsic rank of input data [12] 3. Allows for end-to-end training by using SVD as a guide for optimal compression rates [12] Application and Results - CompTrack has been applied to challenging 3D point cloud tracking tasks, demonstrating that systematic compression of information redundancy is highly effective [14] - The framework not only enhances efficiency but also sets a precedent for addressing information redundancy in various fields, including sensor fusion in robotics and multimodal processing in visual-language models [14][15] - Performance metrics show that CompTrack achieves real-time performance at 80 FPS on RTX 3090, surpassing state-of-the-art methods, with a significant reduction in computational load to 0.94G FLOPs [15]