Roland Memisevic

6.3k citations
43 papers · 3.0k indexed · 2 hit papers · h-index 22

Roland Memisevic

42 papers receiving 2.8k citations

Hit Papers

The “Something Something” Video Database for Learning and...7722015202620182022250500750

Peers

Roland Memisevic
Comparison fields: 5 of 136
  • Computer Vision and Pattern Recognition 2.0k
  • Artificial Intelligence 1.5k
  • Human-Computer Interaction 192
  • Experimental and Cognitive Psychology 362
  • Computational Mathematics 15
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Citations per year

Countries citing papers authored by Roland Memisevic

Since Specialization
Citations

This map shows the geographic impact of Roland Memisevic'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 Roland Memisevic with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Roland Memisevic more than expected).

Fields of papers citing papers by Roland Memisevic

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Roland Memisevic. 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 Roland Memisevic. The network helps show where Roland Memisevic may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Roland Memisevic, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Roland Memisevic Line = papers co-authored together Roland Memisevic links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1
Evaluating visual "common sense" using fine-grained classification and captioning tasks.
20180
2
Fine-grained Video Classification and Captioning
201815
3
The “Something Something” Video Database for Learning and Evaluating Visual Common Sensebreakdown →
2017772
4
Architectural Complexity Measures of Recurrent Neural Networks
201616
5
Deep Learning Vector Quantization.
201612
6
On Using Very Large Target Vocabulary for Neural Machine Translationbreakdown →
2015465
7
Real-time activity recognition via deep learning of motion features
20151
8 2015103
9 201582
10
Modeling Deep Temporal Dependencies with Recurrent Grammar Cells
201439
11
Learning to encode motion using spatio-temporal synchrony
20146
12
On autoencoder scoring
201310
13
The role of spatio-temporal synchrony in the encoding of motion
20131
14
Feature grouping from spatially constrained multiplicative interaction
20131
15 201128
16
Gated Softmax Classification
201059
17
Learning to solve QBF
200738
18 20054
19 200547
20
Multiple Relational Embedding
200425

About Roland Memisevic

Roland Memisevic is a scholar working on Computer Vision and Pattern Recognition, Biophysics and Artificial Intelligence, having authored 43 papers that have together received 3.0k indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (12 papers), Human Pose and Action Recognition (9 papers), Neural Networks and Applications (9 papers), Face and Expression Recognition (8 papers), Advanced Vision and Imaging (7 papers), Domain Adaptation and Few-Shot Learning (7 papers), Multimodal Machine Learning Applications (6 papers) and Model Reduction and Neural Networks (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.0k citations), Artificial Intelligence (1.5k citations) and Human-Computer Interaction (192 citations). Roland Memisevic has collaborated with scholars based in Canada, Germany and United States. Frequent co-authors include Yoshua Bengio, Geoffrey E. Hinton, Sébastien Jean, Vincent Michalski, Kyunghyun Cho, Kishore Konda, Samira Ebrahimi Kahou, Ingo Bax, Joanna Materzyńska and Christopher Pal. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Neural Computation and Neural Networks.

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.

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