arXiv Computer Vision
Oct 1

DiDA: Video Object Segmentation with Distillation Learning of Deformable Attention

DiDA introduces a lightweight video object segmentation framework that leverages Distillation Learning of Deformable Attention. The method uses deformable attention to adapt key and value positions across frames, enabling object representations that are responsive to spatial and temporal changes. Experiments on DAVIS and YouTube‑VOS benchmarks show state‑of‑the‑art performance and efficient memory usage.

By Quang-Trung Truong, Duc Thanh Nguyen, Binh-Son Hua, Sai-Kit Yeung
arXiv Computer Vision
Oct 22

BlazeVGGT: 13x Faster VGGT at 2,000 Frames via Camera Token without Training

BlazeVGGT is a training‑free, plug‑and‑play module that speeds up the VGGT visual geometry model by sparsifying cross‑view attention. It selects a compact set of complementary views using a greedy determinantal point process, preserving dense per‑frame predictions while eliminating redundant computations. On 2,000‑frame sequences, BlazeVGGT achieves roughly 13× faster inference and improves Chamfer Distance from 0.41 to 0.32, demonstrating a strong accuracy–efficiency trade‑off for point‑cloud reconstruction and camera‑pose estimation.

By Shujun Yao, Junhui Lin, Congying Han, Tiande Guo, Ziwen Liu, Bonan Li
arXiv Computer Vision
Oct 22

DetM-Tracker: Deterministic and Memory-Efficient VIS for Autonomous Industrial Inspection

DetM-Tracker is a lightweight Video Instance Segmentation framework designed for autonomous industrial inspection. It reduces memory and computation by condensing instance features into four-scalar descriptors, dynamically allocating query capacity, and using entropy-gated skip connections to focus processing on uncertain regions. The method achieves roughly 20× lower inference latency and 6× less VRAM usage than dense-feature baselines.

By You-Rui Lin, Jen-Chieh Wu, Tzu-Ti Wei, Jen-Jee Chen, Yu-Chee Tseng
arXiv Computer Vision
Oct 27

VGGT-$\Omega$

arXiv:2605.15195v2 Announce Type: replace Abstract: Recent feed-forward reconstruction models, such as VGGT, have proven competitive with traditional optimization-based reconstructors while also prov...

By Jianyuan Wang, Minghao Chen, Shangzhan Zhang, Nikita Karaev, Johannes Sch\"onberger, Patrick Labatut, Piotr Bojanowski, David Novotny, Andrea Vedaldi, Christian Rupprecht