DB-Video: Detail-Preserving Dual-Branch Compression for Long Video Understanding

Video Large Language Models (VideoLLMs) face significant challenges in long video understanding due to the expensive computational cost of processing massive visual tokens. Token compression is hence desired to preserve the fundamental video semantics while simultaneously retaining the fine-grained details most relevant to the query. In this paper, we propose DB-Video, which introduces the novel detail-preserving dual-branch compression to achieve this objective. Our approach employs a dual-branch mechanism, i.e., a base semantic branch to maintain global context and a query-guided residual branch to capture query-specific high-resolution details. To preserve such fine-grained details, we further propose the reconstruction loss to enforce visual fidelity in the compressed latent space. Benefited by the proposed dual-branch compressor and reconstruction loss, DB-Video achieves a token compression ratio 17x while preserving 98% of the original performance. Experimental results demonstrate that DB-Video outperforms other training-based compression methods and generalizes well across three model architectures, offering a more efficient strategy for long video understanding.