Hyunjun Kim

Jeong Hun Yeo, Chae Won Kim, Hyunjun Kim et al.

Lip reading aims to predict spoken language by analyzing lip movements. Despite advancements in lip reading technologies, performance degrades when models are applied to unseen speakers due to their sensitivity to variations in visual information such as lip appearances. To address this challenge, speaker adaptive lip reading technologies have advanced by focusing on effectively adapting a lip reading model to target speakers in the visual modality. However, the effectiveness of adapting language information, such as vocabulary choice, of the target speaker has not been explored in previous works. Additionally, existing datasets for speaker adaptation have limited vocabulary sizes and pose variations, which restrict the validation of previous speaker-adaptive methods in real-world scenarios. To address these issues, we propose a novel speaker-adaptive lip reading method that adapts a pre-trained model to target speakers at both vision and language levels. Specifically, we integrate prompt tuning and the LoRA approach, applying them to a pre-trained lip reading model to effectively adapt the model to target speakers. Furthermore, to validate its effectiveness in real-world scenarios, we introduce a new dataset, VoxLRS-SA, derived from VoxCeleb2 and LRS3. It contains a vocabulary of approximately 100K words, offers diverse pose variations, and enables the validation of adaptation methods in the wild, sentence-level lip reading for the first time in English. Through various experiments, we demonstrate that the existing speaker-adaptive method also improves performance in the wild at the sentence level. Moreover, we show that the proposed method achieves larger improvements compared to the previous works.

Jiwoo Chung, Sangeek Hyun, Hyunjun Kim et al.

Recent advances in text-to-image generative models have enabled numerous practical applications, including subject-driven generation, which fine-tunes pretrained models to capture subject semantics from only a few examples. While diffusion-based models produce high-quality images, their extensive denoising steps result in significant computational overhead, limiting real-world applicability. Visual autoregressive (VAR) models, which predict next-scale tokens rather than spatially adjacent ones, offer significantly faster inference suitable for practical deployment. In this paper, we propose the first VAR-based approach for subject-driven generation. However, naive fine-tuning VAR leads to computational overhead, language drift, and reduced diversity. To address these challenges, we introduce selective layer tuning to reduce complexity and prior distillation to mitigate language drift. Additionally, we found that the early stages have a greater influence on the generation of subject than the latter stages, which merely synthesize minor details. Based on this finding, we propose scale-wise weighted tuning, which prioritizes coarser resolutions for promoting the model to focus on the subject-relevant information instead of local details. Extensive experiments validate that our method significantly outperforms diffusion-based baselines across various metrics and demonstrates its practical usage.

MinKyu Lee, Sangeek Hyun, Woojin Jun et al.

This work investigates the internal training dynamics of image restoration~(IR) Transformers and uncovers a critical yet overlooked issue: conventional LayerNorm leads feature magnitude divergence, up to a million scale, and collapses channel-wise entropy. We analyze this phenomenon from the perspective of networks attempting to bypass constraints imposed by conventional LayerNorm due to conflicts against requirements in IR tasks. Accordingly, we address two misalignments between LayerNorm and IR tasks, and later show that addressing these mismatches leads to both stabilized training dynamics and improved IR performance. Specifically, conventional LayerNorm works in a per-token manner, disrupting spatial correlations between tokens, essential in IR tasks. Also, it employs an input-independent normalization that restricts the flexibility of feature scales, required to preserve input-specific statistics. Together, these mismatches significantly hinder IR Transformer's ability to accurately preserve low-level features throughout the network. To this end, we introduce Image Restoration Transformer Tailored Layer Normalization~(i-LN), a surprisingly simple drop-in replacement for conventional LayerNorm. We propose to normalize features in a holistic manner across the entire spatio-channel dimension, preserving spatial relationships among individual tokens. Additionally, we introduce an input-adaptive rescaling strategy that maintains the feature range flexibility required by individual inputs. Together, these modifications effectively contribute to preserving low-level feature statistics of inputs throughout IR Transformers. Experimental results verify that this combined strategy enhances both the stability and performance of IR Transformers across various IR tasks.