Shedding the Facades, Connecting the Domains: Detecting Shifting Multimodal Hate Video with Test-Time Adaptation

Hate Video Detection (HVD) is crucial for online ecosystems. Existing methods assume identical distributions between training (source) and inference (target) data. However, hateful content often evolves into irregular and ambiguous forms to evade censorship, resulting in substantial semantic drift and rendering previously trained models ineffective. Test-Time Adaptation (TTA) offers a solution by adapting models during inference to narrow the cross-domain gap, while conventional TTA methods target mild distribution shifts and struggle with the severe semantic drift in HVD. To tackle these challenges, we propose SCANNER, the first TTA framework tailored for HVD. Motivated by the insight that, despite the evolving nature of hateful manifestations, their underlying cores remain largely invariant (i.e., targeting is still based on characteristics like gender, race, etc), we leverage these stable cores as a bridge to connect the source and target domains. Specifically, SCANNER initially reveals the stable cores from the ambiguous layout in evolving hateful content via a principled centroid-guided alignment mechanism. To alleviate the impact of outlier-like samples that are weakly correlated with centroids during the alignment process, SCANNER enhances the prior by incorporating a sample-level adaptive centroid alignment strategy, promoting more stable adaptation. Furthermore, to mitigate semantic collapse from overly uniform outputs within clusters, SCANNER introduces an intra-cluster diversity regularization that encourages the cluster-wise semantic richness. Experiments show that SCANNER outperforms all baselines, with an average gain of 4.69% in Macro-F1 over the best.

Expert-Guided Diffusion Planner for Auto-bidding

Auto-bidding is extensively applied in advertising systems, serving a multitude of advertisers. Generative bidding is gradually gaining traction due to its robust planning capabilities and generalizability. In contrast to traditional reinforcement learning-based bidding, generative bidding does not rely on the Markov Decision Process (MDP) exhibiting superior planning capabilities in long-horizon scenarios. Conditional diffusion modeling approaches have demonstrated significant potential in the realm of auto-bidding. However, relying solely on return as the optimality condition is weak to guarantee the generation of genuinely optimal decision sequences, lacking personalized structural information. Moreover, diffusion models' t-step autoregressive generation mechanism inherently carries timeliness risks. To address these issues, we propose a novel conditional diffusion modeling method based on expert trajectory guidance combined with a skip-step sampling strategy to enhance generation efficiency. We have validated the effectiveness of this approach through extensive offline experiments and achieved statistically significant results in online A/B testing, achieving an increase of 11.29% in conversion and a 12.35% in revenue compared with the baseline.