Chirality in Action: Time-Aware Video Representation Learning by Latent Straightening

• URL: http://arxiv.org/abs/2509.08502v2
• Date: Tue, 23 Sep 2025 19:04:53 GMT
• Title: Chirality in Action: Time-Aware Video Representation Learning by Latent Straightening
• Authors: Piyush Bagad, Andrew Zisserman,
• Abstract summary: We introduce a new task: chiral action recognition, where one needs to distinguish between a pair of temporally opposite actions.<n>Our goal is to build time aware video representations which offer linear separability between these chiral pairs.<n>We show that this results in a compact but time-sensitive video representation for the proposed task across three datasets.
• Score: 54.66784646111214
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Our objective is to develop compact video representations that are sensitive to visual change over time. To measure such time-sensitivity, we introduce a new task: chiral action recognition, where one needs to distinguish between a pair of temporally opposite actions, such as "opening vs. closing a door", "approaching vs. moving away from something", "folding vs. unfolding paper", etc. Such actions (i) occur frequently in everyday life, (ii) require understanding of simple visual change over time (in object state, size, spatial position, count . . . ), and (iii) are known to be poorly represented by many video embeddings. Our goal is to build time aware video representations which offer linear separability between these chiral pairs. To that end, we propose a self-supervised adaptation recipe to inject time-sensitivity into a sequence of frozen image features. Our model is based on an auto-encoder with a latent space with inductive bias inspired by perceptual straightening. We show that this results in a compact but time-sensitive video representation for the proposed task across three datasets: Something-Something, EPIC-Kitchens, and Charade. Our method (i) outperforms much larger video models pre-trained on large-scale video datasets, and (ii) leads to an improvement in classification performance on standard benchmarks when combined with these existing models.

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