Fine-tuning pre-trained point-cloud backbones typically updates all parameters, resulting in substantial computation and memory overhead. More importantly, modern point backbones rely on aggressive tokenization and downsampling, which yields compact global tokens but irreversibly discards fine-grained local geometry, an inherent bottleneck for parameter-efficient adaptation. Consequently, existing PEFT methods that operate only on these coarsened tokens can modulate global semantics but struggle to recover the missing multi-scale locality. We present Point Ladder Tuning (PLT), a locality-aware PEFT framework that performs hierarchical, instance-conditioned adaptation while keeping the backbone frozen. PLT forms a lightweight closed loop: (i) a Hierarchical Ladder Network (HLN) constructs a multi-resolution local feature pyramid directly from raw points; (ii) a Local-Global Fusion (LGF) aligns and fuses local pyramids with intermediate backbone semantics; and (iii) a Dynamic Prompt Generator produces instance-aware multi-scale prompts to modulate the frozen backbone effectively. For dense prediction, we further introduce a lightweight segmentation head that progressively upsamples fused features and leverages backbone priors to refine fine structures. Extensive experiments on classification and dense prediction show that PLT consistently surpasses prior PEFT baselines with minimal tunable parameters. PLT achieves state-of-the-art performance using only 2.71% trainable parameters for classification and 7.69% for dense prediction, and scales favorably to larger backbones, requiring merely 0.36% parameters on PointGPT-L. The code is released at https://github.com/JunLinChang/ECCV2026-PLT.
Aleksandr Konovalov, Anna Uporova, Alexander Drobyshev +2cs.SI cs.AI cs.LG
Dynamic community detection is commonly addressed either by full-snapshot recomputation or by solver-specific dynamic procedures. Full recomputation preserves the semantics of mature static solvers, but it repeatedly processes unchanged graph regions when updates are small. Solver-specific dynamic methods can reduce this cost, but their update rules often have limited transferability across objectives, feature representations, and implementations. In addition, localizing computation only by graph distance may omit community context needed by high-quality solvers. We introduce ComNetX, a solver-agnostic hierarchical adaptation framework for local dynamic updates. ComNetX maintains a multi-level community state, expands the updated region, closes it over affected communities, and contracts these communities into compact local instances. This affected-community closure and contraction preserve solver context while restricting computation to the changed part of the graph. The same interface can wrap modularity heuristics, graph-clustering models that use node features, and native dynamic solvers as local backends. We evaluate ComNetX through a multi-backend study on six real networks, longer real-data streams for topology-based backends, and controlled dynamic stochastic block model stress streams. The results show that ComNetX can preserve the quality of strong modularity-based solvers while reducing update time on large graphs: in paired runs on the largest real graph, Local Leiden keeps final modularity within 0.006 of full-snapshot recomputation while achieving a 41.9 +/- 0.2x speedup. The combined protocols also identify regimes where locality breaks down and a full refresh is preferable.