Open-world face anti-spoofing must address both covariate and semantic shifts: source and target domains differ in imaging conditions, while target domains contain diverse attack types absent from training. Existing prompt-based approaches often express spoofing through category semantics or language guidance, which is effective for modeling high-level concepts but is less suited to explicitly capturing the evolving fine-grained and spatially heterogeneous forensic evidence of unseen attacks. Motivated by the hypothesis that many unseen attacks can be characterized by new combinations of recurring visual cues, we propose a compositional forensic visual prompt learning framework that operates entirely in the visual feature space. Built on a frozen ViT-based vision foundation model, the framework employs patch-aware attention to refine a shared set of learnable micro-forensic primitives into localized forensic evidence units derived from image patches. Class-specific global contextual prompts then provide input-dependent routing weights that adaptively select and compose these primitives into compositional forensic visual prompts for real/spoof discrimination. The primitives are not assigned predefined semantic meanings; instead, their specialization and reuse emerge from shared parameterization and joint optimization across categories. Extensive experiments on nine open-world protocols demonstrate state-of-the-art performance, strong cross-domain generalization, and robust adaptation to unseen attacks.
Fangling Jiang, Qi Li, Bing Liu et al.· 0 citations
The increasing prevalence of wearable cameras has driven the development of egocentric (first-person) systems that assist human activities proactively by anticipating imminent interactions. A central challenge in this domain is active object interaction anticipation from first-person video-predicting what interaction will occur, when it will happen, and where it will take place. This involves forecasting (1) what interaction category (verb-noun pair), (2) when (time-to-interaction), and (3) where (the active object's location, bounding box in the last observed frame). However, existing approaches rely on limited prior knowledge about active objects and their state changes, and they struggle to (1) predict diverse state-change interactions, (2) handle temporal uncertainty of changes, and (3) localize the active object accurately in the presence of spatial distractors. To address these problems, we propose ST-KAD. It consists of a Spatial-Temporal Knowledge Aggregator that integrates rich commonsense priors to enhance what-when-where interaction anticipation and guides the model's attention toward informative cues, and a Teacher-Student Distillation framework that enables efficient inference without access to oracle inputs by transferring knowledge from an oracle-informed teacher model to a query-based student decoder. On two egocentric anticipation benchmarks (Ego4D-STA, EPIC-Kitchens-STA), ST-KAD sets a new state of the art, demonstrating accurate what-when-where prediction of future interactions. Moreover, results on four active object detection benchmarks (Ego4D-AOD, EPIC-Kitchens-AOD, MECCANO, 100DOH) further confirm that our prior-informed aggregation and teacher-student distillation generalize beyond anticipation to spatial localization, validating the generality of the design.
Yang Liu, Dejie Yang, Minghang Zheng et al.· IEEE Transactions on Pattern...· 0 citations
