2025.6. 정민우 학생의 Mamba 모델의 효율성을 높인 논문이 CVPR에 accept 되어 6월 미국 내슈빌에서 발표 예정입니다.
LC-Mamba: Local and Continuous Mamba with Shifted Windows for Frame Interpolation
LC-Mamba: Local and Continuous Mamba with Shifted Windows for Frame Interpolation
Hybrid Embedding Framework for Memory-Efficient Recommendation Systems
Viewpoint-Adaptive Collage-based Streaming for 4K Light Field Video
Micro-Cells Enabling Spatio-Angular Joint Attention for High-Resolution Light Field Editing Overview of the proposed system. The input includes the pre-editing LF, and one edited SAI. Geometry information is inferred using mono depth estimation to generate a disparity map, for warping-based propagation. The edited LF is refined using a ViT-based composition network to address artifacts like …
2024.11 김권중 학생의 라이트필드 처리를 위한 트랜스포머 구조에 대한 논문 IEEE Access 에 게재되었습니다. 더 보기 »
Accelerating CNN Training with Concurrent Execution of GPU and Processing-In-Memory The baseline system architecture in this paper, consisting of a host and a PIM. The host is a GPU similar in design to NVIDIA’s V100, and the main memory is three-dimensional (3D) stacked DRAM (HBM). The PIM consists of a single-instruction-multiple- data (SIMD) operator and …
2024.10 CNN 학습을 위한 가속 방법에 관한 논문이 IEEE Access 에 게재되었습니다. 더 보기 »
The proposed 3D reconstruction system has two main stages: iterative depth predictions for occluded regions and 3D reconstruction using these predictions. Initially, conventional monocular depth estimation is applied, followed by iterative amodal mask generation (AMG) and amodal depth estimation (ADE) to generate amodal depths, enriching the 3D reconstruction with occlusion information.
EGformer: Equirectangular Geometry-biased Transformer for 360 Depth Estimation 우리 연구실과 공동 연구를 진행하고 있는 서울대 윤일위 박사과정 학생이 제 1저자, 신찬용 학생이 제 2저자인 연구 결과물을 아래 링크에서 확인하실 수 있습니다. https://openaccess.thecvf.com/content/ICCV2023/papers/Yun_EGformer_Equirectangular_Geometry-biased_Transformer_for_360_Depth_Estimation_ICCV_2023_paper.pdf
논문 제목: FIACCEL: Memory Efficient Frame Interpolation Accelerator for Full-HD Video 아래 링크에서 논문을 early access 할 수 있습니다! https://ieeexplore.ieee.org/document/10306257
Interactive video 를 가능하게 하는 360도 영상의 depth estimation 논문이 IEEE Transactions on Multimedia에 accept 되었습니다. “Adversarial Mixture Density Network and Uncertainty-based Joint Learning for 360 Monocular Depth Estimation” 출판되면 소식 업데이트 하겠습니다!