Andrea Vedaldi
About
In The Last Decade
Andrea Vedaldi
149 papers receiving 24.2k citations
Hit Papers
Peers
Comparison fields: 5 of 203
- Computer Vision and Pattern Recognition 19.1k
- Artificial Intelligence 7.0k
- Media Technology 2.8k
- Signal Processing 2.4k
- Aerospace Engineering 1.9k
Countries citing papers authored by Andrea Vedaldi
This map shows the geographic impact of Andrea Vedaldi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Andrea Vedaldi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrea Vedaldi more than expected).
Fields of papers citing papers by Andrea Vedaldi
This network shows the impact of papers produced by Andrea Vedaldi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Andrea Vedaldi. The network helps show where Andrea Vedaldi may publish in the future.
Co-authorship network of co-authors of Andrea Vedaldi
This figure shows the co-authorship network connecting the top 25 collaborators of Andrea Vedaldi. A scholar is included among the top collaborators of Andrea Vedaldi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Andrea Vedaldi. Andrea Vedaldi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 75 | |
| 3 | Vggsound: A Large-Scale Audio-Visual Dataset breakdown → | 234 |
| 4 | RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces | 3 |
| 5 | Labelling unlabelled videos from scratch with multi-modal self-supervision | 6 |
| 6 | A critical analysis of self-supervision, or what we can learn from a single image | 11 |
| 7 | Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels | 14 |
| 8 | 149 | |
| 9 | Surprising Effectiveness of Few-Image Unsupervised Feature Learning. | 3 |
| 10 | Invariant Information Distillation for Unsupervised Image Segmentation and Clustering | 21 |
| 11 | Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks | 88 |
| 12 | Learning multiple visual domains with residual adapters | 270 |
| 13 | Unsupervised object learning from dense equivariant image labelling | 4 |
| 14 | Learning feed-forward one-shot learners | 88 |
| 15 | Semantic Part Segmentation with Deep Learning. | 13 |
| 16 | Deep Structured Output Learning for Unconstrained Text Recognition | 98 |
| 17 | Pylon Model for Semantic Segmentation | 71 |
| 18 | Oxford-IIIT TRECVID 2010 - Notebook paper. | 1 |
| 19 | Oxford-IIIT TRECVID 2009 - Notebook Paper | 1 |
| 20 | Structured output regression for detection with partial truncation | 45 |
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.