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Robotics and Computer Vision Lab

AI in Sensing, AI in Perception, AI in Action

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허 재연

About Posts
[CVPR 2019] Learning Loss for Active Learning
  • Posted on: 11/12/2023 –
  • Comments: 6 Comments
[ICML 2021] Effective Evaluation of Deep Active Learning on Image Classification Tasks
  • Posted on: 11/05/2023 –
  • Comments: 2 Comments
[IEEE transactions 2017] CEAL : Cost-Effective Active Learning for Deep Image Classification
  • Posted on: 10/08/2023 –
  • Comments: 12 Comments
[ICLR 2018] Active Learning For Convolutional Neural Networks: A Core-Set Approach
  • Posted on: 09/24/2023 –
  • Comments: 12 Comments
[CVPR 2021] Understanding the Behaviour of Contrastive Loss
  • Posted on: 09/17/2023 –
  • Comments: 6 Comments
[CVPR 2020] Momentum Contrast for Unsupervised Visual Representation Learning
  • Posted on: 09/03/2023 –
  • Comments: 8 Comments
[ICAI 2022] Contrastive Self-Supervised Learning: A Survey on Different Architectures
  • Posted on: 08/27/2023 –
  • Comments: 8 Comments
[NeurIPS 2021] Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning
  • Posted on: 08/20/2023 –
  • Comments: 2 Comments
KCCV2023 참관기
  • Posted on: 08/13/2023 –
  • Comments: No Comments
[ICLR 2020] A Simple Framework for Contrastive Learning of Visual Representations
  • Posted on: 07/30/2023 –
  • Comments: 16 Comments
Newer Posts 1 2 … 6 7 8 Older Posts

Conference Deadline

NEW POST

  • [arXiv 2025] VideoRAG: Retrieval-Augmented Generation over Video Corpus
  • [WACV 2026] UNO: Unifying One-stage Video Scene Graph Generation via Object-Centric Visual Representation Learning
  • DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning
  • [NeurIPS 2020]Object-Centric Learning with Slot Attention
  • [ICRA 2024]NoMaD : Goal Masked Diffusion Policies for Navigation and Exploration

New Comment

  1. 김 영규 on [NeurIPS 2025] PhysX-3D: Physical-Grounded 3D Asset Generation11/26/2025

    안녕하세요 정민님 댓글 감사합니다. 정리를 하자면 Absolute Scale의 경우는 사람이 직접 기입합니다. Kinematics에 관련된 부분도 수학적인 기하 알고리즘을 통해 접촉면…

  2. 김 영규 on [NeurIPS 2025] PhysX-3D: Physical-Grounded 3D Asset Generation11/26/2025

    안녕하세요 우현님, 다른 리뷰에 대한 댓글인것 같긴 한데, 일단 답변 드리겠습니다 우선 Robo-SAM 자체가 segment 해야하는게 쉽게 로봇, task 관련…

  3. 김 영규 on [IROS 2025] RoboEngine: Plug-and-Play Robot Data Augmentation with Semantic Robot Segmentation and Background Generation11/26/2025

    안녕하세요 정우님 댓글 감사합니다. 답변이 늦어 죄송합니다 음.. 일단 최근 Imitation Learning 모델들은 rollout 단위로 학습하지 않고 video 기준으로 특정…

  4. 김 영규 on [IROS 2025] RoboEngine: Plug-and-Play Robot Data Augmentation with Semantic Robot Segmentation and Background Generation11/26/2025

    안녕하세요 정민님 댓글 감사합니다. 답변이 늦은점 죄송합니다,, A 1,2 (제가 설명을 깔끔하게 못 한것 같습니다,,) Robo-SAM이 저자들이 제안한 3800장의 데이터셋으로…

  5. 신 인택 on [NeurIPS 2020]Object-Centric Learning with Slot Attention11/25/2025

    안녕하세요 재연님 답글 감사합니다. 각 질문에 대해서 답글을 달아드리자면 1. 논문에서 learnable slot 에 대해서 언급하지는 않았으나 random sampled slot이…

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