12 citations · 16 across the 7 of their papers we have counts for
9 papers
IMSE: Intrinsic Mixture of Spectral Experts Fine-tuning for Test-Time Adaptation
Sunghyun Baek, Jaemyung Yu, Seunghee Koh +3
Test-time adaptation (TTA) has been widely explored to prevent performance degradation when test data differ from the training distribution. However, fully leveraging the rich repr…
Beyond Semantics: Disentangling Information Scope in Sparse Autoencoders for CLIP
Yusung Ro, Jaehyun Choi, Junmo Kim
Sparse Autoencoders (SAEs) have emerged as a powerful tool for interpreting the internal representations of CLIP vision encoders, yet existing analyses largely focus on the semanti…
ARGOS: Who, Where, and When in Agentic Multi-Camera Person Search
Myungchul Kim, Kwanyong Park, Junmo Kim +1
Existing person search methods assume access to complete visual queries or exhaustive tracking, yet real-world witness accounts are vague, partial, and spread across cameras and ti…
IWP: Token Pruning as Implicit Weight Pruning in Large Vision Language Models
Dong-Jae Lee, Sunghyun Baek, Junmo Kim
Large Vision Language Models show impressive performance across image and video understanding tasks, yet their computational cost grows rapidly with the number of visual tokens. Ex…
Video Diffusion Models Excel at Tracking Similar-Looking Objects Without Supervision
Chenshuang Zhang, Kang Zhang, Joon Son Chung +3
Distinguishing visually similar objects by their motion remains a critical challenge in computer vision. Although supervised trackers show promise, contemporary self-supervised tra…
Towards Understanding Dual BN In Hybrid Adversarial Training
Chenshuang Zhang, Chaoning Zhang, Kang Zhang +3
There is a growing concern about applying batch normalization (BN) in adversarial training (AT), especially when the model is trained on both adversarial samples and clean samples…
ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object
Chenshuang Zhang, Fei Pan, Junmo Kim +2
We establish rigorous benchmarks for visual perception robustness. Synthetic images such as ImageNet-C, ImageNet-9, and Stylized ImageNet provide specific type of evaluation over s…
ContextMix: A context-aware data augmentation method for industrial visual inspection systems
Hyungmin Kim, Donghun Kim, Pyunghwan Ahn +3
While deep neural networks have achieved remarkable performance, data augmentation has emerged as a crucial strategy to mitigate overfitting and enhance network performance. These…
NICE: CVPR 2023 Challenge on Zero-shot Image Captioning
Taehoon Kim, Pyunghwan Ahn, Sangyun Kim +39
In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project and share the results and outcomes of 2023 challenge. This project is designed t…