You Huang

You Huang, Wenbin Lai, Jiayi Ji et al.

The Segment Anything Model (SAM) has advanced interactive segmentation but is limited by the high computational cost on high-resolution images. This requires downsampling to meet GPU constraints, sacrificing the fine-grained details needed for high-precision interactive segmentation. To address SAM's limitations, we focus on visual length extrapolation and propose a lightweight model named HRSAM. The extrapolation enables HRSAM trained on low resolutions to generalize to high resolutions. We begin by finding the link between the extrapolation and attention scores, which leads us to base HRSAM on Swin attention. We then introduce the Flexible Local Attention (FLA) framework, using CUDA-optimized Efficient Memory Attention to accelerate HRSAM. Within FLA, we implement Flash Swin attention, achieving over a 35% speedup compared to traditional Swin attention, and propose a KV-only padding mechanism to enhance extrapolation. We also develop the Cycle-scan module that uses State Space models to efficiently expand HRSAM's receptive field. We further develop the HRSAM++ within FLA by adding an anchor map, providing multi-scale data augmentation for the extrapolation and a larger receptive field at slight computational cost. Experiments show that, under standard training, HRSAMs surpass the previous SOTA with only 38% of the latency. With SAM-distillation, the extrapolation enables HRSAMs to outperform the teacher model at lower latency. Further finetuning achieves performance significantly exceeding the previous SOTA.

You Huang, Lichao Chen, Jiayi Ji et al.

Interactive segmentation (IS) improves annotation efficiency by segmenting target regions from user prompts, with widespread applications in real-world scenarios. Current approaches face a critical trade-off: dense-token methods achieve superior accuracy and detail preservation but suffer from prohibitively slow processing on CPU devices, while the Segment Anything Model (SAM) advances the field with sparse prompt tokens for fast inference but compromises segmentation quality. In this paper, we propose Inter2Former to address this challenge by optimizing computation allocation in dense-token processing, which introduces four key enhancements. First, we propose Dynamic Prompt Embedding (DPE) that adaptively processes only regions of interest while avoiding additional overhead from background tokens. Second, we introduce Dynamic Hybrid Attention (DHA), which leverages previous segmentation masks to route tokens through either full attention (O(N2)) for boundary regions or our proposed efficient BSQ attention (O(N)) for non-boundary regions. Third, we develop Hybrid Mixture of Experts (HMoE), which applies similar adaptive computation strategies in FFN modules with CPU-optimized parallel processing. Finally, we present Dynamic Local Upsampling (DLU), a reverse operation of DPE, which localizes objects with a lightweight MLP and performs fine-grained upsampling only in detected regions. Experimental results on high-precision IS benchmarks demonstrate that Inter2Former achieves SOTA performance with high efficiency on CPU devices.