Qingyuan Wang

Binhua Huang, Wendong Yao, Shaowu Chen et al.

We introduce MoCrop, a motion-aware adaptive cropping module for efficient video action recognition in the compressed domain. MoCrop uses motion vectors that are available in H.264 video to locate motion-dense regions and produces a single clip-level crop that is applied to all I-frames at inference. The module is training free, adds no parameters, and can be plugged into diverse backbones. A lightweight pipeline that includes denoising & merge (DM), Monte Carlo sampling (MCS), and adaptive cropping (AC) via a motion-density submatrix search yields robust crops with negligible overhead. On UCF101, MoCrop improves accuracy or reduces compute. With ResNet-50, it delivers +3.5% Top-1 accuracy at equal FLOPs (attention setting), or +2.4% Top-1 accuracy with 26.5% fewer FLOPs (efficiency setting). Applied to CoViAR, it reaches 89.2% Top-1 accuracy at the original cost and 88.5% Top-1 accuracy while reducing compute from 11.6 to 8.5 GFLOPs. Consistent gains on MobileNet-V3, EfficientNet-B1, and Swin-B indicate strong generality and make MoCrop practical for real-time deployment in the compressed domain. Our code and models are available at https://github.com/microa/MoCrop.

Guoxin Wang, Qingyuan Wang, Binhua Huang et al.

Vision Transformers (ViTs) achieve strong performance in image classification but incur high computational costs from processing all image tokens. To reduce inference costs in large ViTs without compromising accuracy, we propose TinyDrop, a training-free token dropping framework guided by a lightweight vision model. The guidance model estimates the importance of tokens while performing inference, thereby selectively discarding low-importance tokens if large vit models need to perform attention calculations. The framework operates plug-and-play, requires no architectural modifications, and is compatible with diverse ViT architectures. Evaluations on standard image classification benchmarks demonstrate that our framework reduces FLOPs by up to 80% for ViTs with minimal accuracy degradation, highlighting its generalization capability and practical utility for efficient ViT-based classification.