Isomer: Isomerous Transformer for Zero-shot Video Object Segmentation

TL;DR

Isomer introduces a novel Transformer-based framework for zero-shot video object segmentation that improves efficiency and accuracy by employing stage-specific Transformer variants, achieving state-of-the-art results.

Contribution

The paper proposes a level-isomerous Transformer framework with CST and SGST variants tailored for different feature stages, significantly enhancing speed and performance in ZVOS.

Findings

Speed increased by 13 times over baseline.

Achieved new state-of-the-art ZVOS performance.

Efficient modeling of stage-specific dependencies.

Abstract

Recent leading zero-shot video object segmentation (ZVOS) works devote to integrating appearance and motion information by elaborately designing feature fusion modules and identically applying them in multiple feature stages. Our preliminary experiments show that with the strong long-range dependency modeling capacity of Transformer, simply concatenating the two modality features and feeding them to vanilla Transformers for feature fusion can distinctly benefit the performance but at a cost of heavy computation. Through further empirical analysis, we find that attention dependencies learned in Transformer in different stages exhibit completely different properties: global query-independent dependency in the low-level stages and semantic-specific dependency in the high-level stages. Motivated by the observations, we propose two Transformer variants: i) Context-Sharing Transformer (CST) that learns the global-shared contextual information within image frames with a lightweight computation. ii) Semantic Gathering-Scattering Transformer (SGST) that models the semantic correlation separately for the foreground and background and reduces the computation cost with a soft token merging mechanism. We apply CST and SGST for low-level and high-level feature fusions, respectively, formulating a level-isomerous Transformer framework for ZVOS task. Compared with the baseline that uses vanilla Transformers for multi-stage fusion, ours significantly increase the speed by 13 times and achieves new state-of-the-art ZVOS performance. Code is available at https://github.com/DLUT-yyc/Isomer.