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Ho-Joong Kim

7 accepted papers

2026

TF-CADE: Foreground-Concentrated Text-Video Alignment for Zero-Shot Temporal Action Detection

CVPR 2026

Zero-Shot Temporal Action Detection (ZSTAD) aims to localize and recognize action instances from unseen action categories in untrimmed videos. Although existing methods have shown effectiveness by advancing architectural text-video alignment, they still struggle with capturing semantic distinctions

Cited by 0SourceScholar
2025

Comprehensive Information Bottleneck for Unveiling Universal Attribution to Interpret Vision Transformers

CVPR 2025highlight

The feature attribution method reveals the contribution of input variables to the decision-making process to provide an attribution map for explanation. Existing methods grounded on the information bottleneck principle compute information in a specific layer to obtain attributions, compressing the f…

Cited by 0SourcePDFScholar
2025

DiGIT: Multi-Dilated Gated Encoder and Central-Adjacent Region Integrated Decoder for Temporal Action Detection Transformer

CVPR 2025poster

In this paper, we examine a key limitation in query-based detectors for temporal action detection (TAD), which arises from their direct adaptation of originally designed architectures for object detection. Despite the effectiveness of the existing models, they struggle to fully address the unique ch…

2025

MIRe: Enhancing Multimodal Queries Representation via Fusion-Free Modality Interaction for Multimodal Retrieval

ACL 2025finding

Recent multimodal retrieval methods have endowed text-based retrievers with multimodal capabilities by utilizing pre-training strategies for visual-text alignment. They often directly fuse the two modalities for cross-reference during the alignment to understand multimodal queries. However, existing…

2024

TE-TAD: Towards Full End-to-End Temporal Action Detection via Time-Aligned Coordinate Expression

CVPR 2024poster

In this paper we investigate that the normalized coordinate expression is a key factor as reliance on hand-crafted components in query-based detectors for temporal action detection (TAD). Despite significant advancements towards an end-to-end framework in object detection query-based detectors have…

2024

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

NeurIPS 2024poster

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 pro…

2024

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

AAAI 2024technical

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 uncur…