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Ho-Joong Kim

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2 papers

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NeurIPS Conference 2024 Conference Paper

Text-Infused Attention and Foreground-Aware Modeling for Zero-Shot Temporal Action Detection

Yearang Lee
Ho-Joong Kim
Seong-Whan Lee

Zero-Shot Temporal Action Detection (ZSTAD) aims to classify and localize action segments in untrimmed videos for unseen action categories. Most existing ZSTAD methods utilize a foreground-based approach, limiting the integration of text and visual features due to their reliance on pre-extracted proposals. In this paper, we introduce a cross-modal ZSTAD baseline with mutual cross-attention, integrating both text and visual information throughout the detection process. Our simple approach results in superior performance compared to previous methods. Despite this improvement, we further identify a common-action bias issue that the cross-modal baseline over-focus on common sub-actions due to a lack of ability to discriminate text-related visual parts. To address this issue, we propose Text-infused attention and Foreground-aware Action Detection (Ti-FAD), which enhances the ability to focus on text-related sub-actions and distinguish relevant action segments from the background. Our extensive experiments demonstrate that Ti-FAD outperforms the state-of-the-art methods on ZSTAD benchmarks by a large margin: 41. 2\% (+ 11. 0\%) on THUMOS14 and 32. 0\% (+ 5. 4\%) on ActivityNet v1. 3. Code is available at: https: //github. com/YearangLee/Ti-FAD.

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AAAI Conference 2024 Conference Paper

Unknown-Aware Graph Regularization for Robust Semi-supervised Learning from Uncurated Data

Heejo Kong
Suneung Kim
Ho-Joong Kim
Seong-Whan Lee

Recent advances in semi-supervised learning (SSL) have relied on the optimistic assumption that labeled and unlabeled data share the same class distribution. However, this assumption is often violated in real-world scenarios, where unlabeled data may contain out-of-class samples. SSL with such uncurated unlabeled data leads training models to be corrupted. In this paper, we propose a robust SSL method for learning from uncurated real-world data within the context of open-set semi-supervised learning (OSSL). Unlike previous works that rely on feature similarity distance, our method exploits uncertainty in logits. By leveraging task-dependent predictions of logits, our method is capable of robust learning even in the presence of highly correlated outliers. Our key contribution is to present an unknown-aware graph regularization (UAG), a novel technique that enhances the performance of uncertainty-based OSSL frameworks. The technique addresses not only the conflict between training objectives for inliers and outliers but also the limitation of applying the same training rule for all outlier classes, which are existed on previous uncertainty-based approaches. Extensive experiments demonstrate that UAG surpasses state-of-the-art OSSL methods by a large margin across various protocols. Codes are available at https://github.com/heejokong/UAGreg.

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