Vedanuj Goswami
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 5%
- Signal Processing
- Radiology, Nuclear Medicine and Imaging
- Information Systems
- Co-authors
- Marcus RohrbachJiasen LuStefan LeeDevi ParikhAmanpreet SinghRonghang HuGuillaume CouaironDouwe Kiela
- Topics
- Topic Modeling (9 papers)Multimodal Machine Learning Applications (7 papers)Natural Language Processing Techniques (7 papers)
- Journals
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)DIGITAL.CSIC (Spanish National Research Council (CSIC))International Conference on Learning Representations
- Partner nations
- United StatesIsraelSpain
In The Last Decade
Vedanuj Goswami
13 papers receiving 550 citations
Hit Papers
Peers
Comparison fields: 5 of 72
- Computer Vision and Pattern Recognition 421
- Artificial Intelligence 410
- Signal Processing 27
- Radiology, Nuclear Medicine and Imaging 22
- Information Systems 15
Countries citing papers authored by Vedanuj Goswami
This map shows the geographic impact of Vedanuj Goswami'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 Vedanuj Goswami with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vedanuj Goswami more than expected).
Fields of papers citing papers by Vedanuj Goswami
This network shows the impact of papers produced by Vedanuj Goswami. 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 Vedanuj Goswami. The network helps show where Vedanuj Goswami may publish in the future.
Co-authorship network of co-authors of Vedanuj Goswami
This figure shows the co-authorship network connecting the top 25 collaborators of Vedanuj Goswami. A scholar is included among the top collaborators of Vedanuj Goswami 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 Vedanuj Goswami. Vedanuj Goswami is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 1 | |
| 3 | 2 | |
| 4 | 7 | |
| 5 | 11 | |
| 6 | 3 | |
| 7 | 15 | |
| 8 | 9 | |
| 9 | FLAVA: A Foundational Language And Vision Alignment Modelbreakdown → | 272 |
| 10 | MoVie: Revisiting Modulated Convolutions for Visual Counting and Beyond | 1 |
| 11 | 12-in-1: Multi-Task Vision and Language Representation Learningbreakdown → | 244 |
| 12 | 6 | |
| 13 | 7 |
About Vedanuj Goswami
Vedanuj Goswami is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Graphics and Computer-Aided Design, having authored 13 papers that have together received 581 indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Multimodal Machine Learning Applications (7 papers) and Natural Language Processing Techniques (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (421 citations), Artificial Intelligence (410 citations) and Signal Processing (27 citations). Vedanuj Goswami has collaborated with scholars based in United States, Israel and Spain. Frequent co-authors include Marcus Rohrbach, Jiasen Lu, Stefan Lee, Devi Parikh, Amanpreet Singh, Ronghang Hu, Guillaume Couairon, Douwe Kiela, Wojciech Galuba and Shruti Bhosale. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), DIGITAL.CSIC (Spanish National Research Council (CSIC)) and International Conference on Learning Representations.
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.