When Predicting Nothing Beats SAM 3: Revisiting Evaluation in Video Object Segmentation

Long videos challenge open-world segmentation systems to continually discover objects and preserve their identities through disappearance and reappearance. Savvy, a zero-shot, semi-online, class-agnostic system for persistent object discovery and identity maintenance, and OGA, an evaluation suite that credits coherent part-level predictions when their granularity differs from the reference annotations. Savvy combines modular mask discovery, deferred admission based on accumulated evidence, and track consolidation to maintain an evolving object set. OGA allows multiple predictions to support one reference while preserving prediction IDs, pairing granularity-aware fidelity with diagnostics of identity bleeding and fragmented support. Across 142 ScanNet videos and 106 HM3D trajectories, evaluated using adaptively sampled prefixes capped at 1,500 source frames, Savvy outperforms DEVA+SAM and EntitySAM in VPQ_inf, STQ, and AQ under both conventional and OGA evaluation. Conventional VPQ_inf reaches 18.44 versus DEVA+SAM's 11.52 on ScanNet and 17.31 versus 6.58 on HM3D. VIPSeg comparisons reveal conventional one-to-one VPQ's sensitivity to annotation granularity. Controlled identity-severing and flicker tests show that OGA detects temporal failures even when frame-level masks remain unchanged. Together, these results demonstrate complementary advances: Savvy improves long-horizon segmentation and association, and OGA distinguishes coherent part-level support from temporal identity failures. Project website: https://github.com/QingSuML/savvy

We present SAM3Dual, our third-place solution to the MOSEv2 track of the 8th Large-scale Video Object Segmentation (LSVOS) Challenge at ECCV 2026. SAM3Dual is a training-free inference extension of pretrained SAM 3 that explicitly separates temporal memory into a short-term branch for recent observations and a long-term branch for interval-sampled historical representations. The two memory responses are combined using a deterministic sequence-relative fusion schedule and conservatively modulated by the previous-frame object confidence. All pretrained SAM 3 parameters remain frozen, requiring no task-specific training, fine-tuning, test-time training, or online parameter optimization. The complete system achieved an official J&F score of 64.37 and ranked third in the MOSEv2 track. This result highlights the potential of reorganizing temporal memory entirely at inference time to obtain competitive long-term VOS performance while preserving the pretrained model.

Complex video object segmentation requires robust target propagation under severe occlusion, disappearance and reappearance. Although SAM3 provides strong promptable mask propagation, a uniform inference path remains unreliable for tiny targets with insufficient visual evidence and semantic-dominated targets whose identities depend on explicit attributes. To this end, we present VOS-Agent, a collaborative framework that retains SAM3 as the shared dense segmentation module and conditionally activates specialized agents according to target characteristics. A Target Perception and Routing Agent assigns each sequence to a regular, tiny, or semantic-dominated route. Tiny targets are supported by a Visual Tracking Agent through confidence-aware box prompts, while semantic-dominated targets are handled by an MLLM-based Semantic Agent through description-guided localization and candidate verification. On the MOSEv2 test set, VOS-Agent achieves 69.82% on the official $\mathcal{J}\&\dot{\mathcal{F}}$ metric and ranks first in the MOSEv2 Track of the 8th LSVOS Challenge at ECCV 2026.