{"ID":2837841,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.19765","arxiv_id":"2511.19765","title":"Lightweight Transformer Framework for Weakly Supervised Semantic Segmentation","abstract":"Weakly supervised semantic segmentation (WSSS) must learn dense masks from noisy, under-specified cues. We revisit the SegFormer decoder and show that three small, synergistic changes make weak supervision markedly more effective-without altering the MiT backbone or relying on heavy post-processing. Our method, CrispFormer, augments the decoder with: (1) a boundary branch that supervises thin object contours using a lightweight edge head and a boundary-aware loss; (2) an uncertainty-guided refiner that predicts per-pixel aleatoric uncertainty and uses it to weight losses and gate a residual correction of the segmentation logits; and (3) a dynamic multi-scale fusion layer that replaces static concatenation with spatial softmax gating over multi-resolution features, optionally modulated by uncertainty. The result is a single-pass model that preserves crisp boundaries, selects appropriate scales per location, and resists label noise from weak cues. Integrated into a standard WSSS pipeline (seed, student, and EMA relabeling), CrispFormer consistently improves boundary F-score, small-object recall, and mIoU over SegFormer baselines trained on the same seeds, while adding minimal compute. Our decoder-centric formulation is simple to implement, broadly compatible with existing SegFormer variants, and offers a reproducible path to higher-fidelity masks from image-level supervision.","short_abstract":"Weakly supervised semantic segmentation (WSSS) must learn dense masks from noisy, under-specified cues. We revisit the SegFormer decoder and show that three small, synergistic changes make weak supervision markedly more effective-without altering the MiT backbone or relying on heavy post-processing. Our method, CrispFo...","url_abs":"https://arxiv.org/abs/2511.19765","url_pdf":"https://arxiv.org/pdf/2511.19765v1","authors":"[\"Ali Torabi\",\"Sanjog Gaihre\",\"Yaqoob Majeed\"]","published":"2025-11-24T22:40:57Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Transformer\"]","has_code":false}