Concept Bottleneck Models (CBMs), which route predictions through a layer of human-interpretable concepts, have substantially improved the interpretability of visual classification by exposing an explicit, language-based rationale for each prediction. Most existing work focuses on static images, and temporal dynamics in videos have been underexplored in conceptual representation. We propose a framework that extends concept-based image recognition to procedural video understanding. It represents a multi-step video as a temporal sequence of segment-level concept score vectors and feeds this sequence into a Transformer encoder to predict the class label. We also explore a way to estimate the contribution of each concept for further interpretability. We evaluated three concept sets of different granularities on cooking and household-activity videos and found that coarser-grained concepts are sufficient for classifying these videos, which implies that fine-grained textual descriptions may be unnecessary.
Katsuya Ogata, Ryoya Yoshiyama, Kawachi Hodaka, Yuta Nakashima
Photorealistic avatar videos decouple the appearance of a rendered face from the person who operates it, which makes conventional face recognition unsuitable for identifying the operator. Verifying the operator therefore requires representations of how a face moves that are insensitive to what the avatar looks like. We propose a verification framework built without any external pretrained model. It uses two complementary modalities provided for each sample: facial landmark sequences and video frames. From the landmark modality, it learns motion representations by diffusion masked pretraining over grouped landmarks, guided by lightweight auxiliary tasks that predict behavioral cues such as motion intensity and eye openness, and turns them into operator embeddings with a contrastive linear probe. To generalize to unseen avatar generators, the pretraining input is randomly transformed to simulate how a generator may alter the operator's motion. From the video modality, it captures fine facial motion through differences between consecutive frames. The embeddings of the two modalities are combined into one. Our approach reaches an overall AUC of 0.769 on the WBA2026 benchmark. Linear probing shows that the video embedding retains little information about the avatar's identity, whereas the landmark embedding retains some, and fusing the two modalities improves verification in particular across speaking and listening modes.