UniConvSeg: Query-Free Convolutional Unified Segmentation in Images and Videos

Unified segmentation models commonly rely on Transformer object queries andrepeated global query-pixel interactions, raising the question of whether one efficient model can cover image, prompt, and video segmentation using convolution alone. We present UniConvSeg, a purely convolutional, query-free architecture that shares a ConvNeXt V2 feature pyramid and combines dense object-conditioned kernels, a convolutional panoptic readout, a prompt-conditioned maskdecoder, bounded convolutional memory, and dataset-conditioned vocabulary selection. Training proceeds through task-specific prompt/VOS and panoptic stagesfollowed by joint optimization over panoptic, prompted, VOS, and video-panopticdata with sampling probabilities (0.40, 0.20, 0.20, 0.20). With 122M parameters,UniConvSeg reaches 130 FPS for image-panoptic inference on an A800, 5.9x the 22 FPS of OMG-Seg under the same batch-1 end-to-end protocol. It achieves 52.7 COCO PQ, 43.2 instance AP, 48.3 VIPSeg VPQ, 53.2 YT-VIS AP, 88.7DAVIS J&F, and 68.1 promptable-segmentation mIoU. These results, togetherwith controlled readout, memory, training, and association ablations, show that purely convolutional task conditioning can provide a strong accuracy-efficiency tradeoff across four segmentation interfaces without a Transformer query decoder.