C3Po: Cross-View Cross-Modality Correspondence by Pointmap Prediction

Recent research by DeepSeek-OCR has led to the introduction of Context Cascade Compression (C3), a method designed to tackle the challenges of processing million-level token inputs in long-context tasks for Large Language Models (LLMs). C3 utilizes a two-stage approach where a smaller LLM compresses text into latent tokens, followed by a larger LLM that decodes this compressed context, achieving a notable 20x compression ratio with high decoding accuracy.

Recent advancements in neural video compression have led to the development of NVRC-Lite, an extension of Neural Video Representation Compression (NVRC). This new framework integrates multi-scale feature grids and higher resolution grids, significantly enhancing performance while maintaining low computational complexity.

A recent study titled 'AVGGT: Rethinking Global Attention for Accelerating VGGT' investigates the global attention mechanisms in models like VGGT and π3, revealing their roles in multi-view 3D performance. The authors propose a two-step acceleration scheme to enhance efficiency by modifying early global layers and subsampling global attention. This approach aims to reduce computational costs while maintaining performance.