CVPR14 图像检索papers

本文汇总了CVPR14年关于图像检索与视频检索的前沿研究,包括Triangulation embedding、民主聚合、协作哈希、多词汇集贝叶斯融合等关键技术,以及视频事件检测、视觉语义搜索等领域的最新成果。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

CVPR14年关于图像检索方面的papers,汇总成一个list,方便阅读。


图像检索

  1. Triangulation embedding and democratic aggregation for image search (Orals)
  2. Collaborative Hashing (post)
  3. Packing and Padding: Coupled Multi-index for Accurate Image Retrieval (post) technical report
  4. Bayes Merging of Multiple Vocabularies for Scalable Image Retrieval (post) technical report
  5. Fast Supervised Hashing with Decision Trees for High-Dimensional Data (post)
  6. Learning Fine-grained Image Similarity with Deep Ranking (post)
  7. Congruency-Based Reranking (post)可能
  8. Fisher and VLAD with FLAIR (post)可能
  9. Locality in Generic Instance Search from One Example (post)
  10. Asymmetric sparse kernel approximations for large-scale visual search (post)
  11. Locally Linear Hashing for Extracting Non-Linear Manifolds (post)
  12. Adaptive Object Retrieval with Kernel Reconstructive Hashing (post)
  13. Hierarchical Feature Hashing for Fast Dimensionality Reduction (post)

视频检索与事件检测

  1. Temporal Sequence Modeling For Video Event Detection (Orals)
  2. Visual Semantic Search: Retrieving Videos via Complex Textual Queries (post)

感兴趣文章

  • Robust Orthonormal Subspace Learning: Efficient Recovery of Corrupted Low-rank Matrices (Orals)

  • Fast and Accurate Image Matching with Cascade Hashing for 3D Reconstruction (Orals)

  • Large-scale Video Classification using Convolutional Neural Networks (Orals)

  • Locally Optimized Product Quantization (post)

  • Product Sparse Coding (post)

  • Distance Encoded Product Quantization (post)

  • Covariance descriptors for 3D shape matching and retrieval (post)

  • Turning Mobile Phones into 3D Scanners (post)

  • Linear Ranking Analysis (post)

 

from: http://yongyuan.name/blog/cvpr14-reading-list.html

评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值