Daniel Gläsner

2.9k total citations · 3 hit papers
17 papers, 1.8k citations indexed

About

Daniel Gläsner is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Daniel Gläsner has authored 17 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Computer Vision and Pattern Recognition, 2 papers in Artificial Intelligence and 2 papers in Media Technology. Recurrent topics in Daniel Gläsner's work include Advanced Vision and Imaging (6 papers), Advanced Neural Network Applications (4 papers) and Advanced Image Processing Techniques (3 papers). Daniel Gläsner is often cited by papers focused on Advanced Vision and Imaging (6 papers), Advanced Neural Network Applications (4 papers) and Advanced Image Processing Techniques (3 papers). Daniel Gläsner collaborates with scholars based in United States, Israel and Germany. Daniel Gläsner's co-authors include Michal Irani, Shai Bagon, Andreas Veit, Ayan Chakrabarti, Srinadh Bhojanapalli, Thomas Unterthiner, Daliang Li, Anat Levin, Boaz Nadler and Ronen Basri and has published in prestigious journals such as Sensors, ACM Transactions on Graphics and Image and Vision Computing.

In The Last Decade

Daniel Gläsner

17 papers receiving 1.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Super-resolution from a single image

2009 1.2k citations Daniel Gläsner, Shai Bagon et al. profile →
Understanding Robustness of Transformers for Image Classification

2021 227 citations Srinadh Bhojanapalli, Ayan Chakrabarti et al. 2021 IEEE/CVF International Conference on Computer Vision (ICCV) profile →
Rethinking FID: Towards a Better Evaluation Metric for Image Generation

2024 45 citations Sadeep Jayasumana, Srikumar Ramalingam et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Daniel Gläsner United States	10	1.6k	848	168	96	86	17	1.8k
Hongwei Yong Hong Kong	15	1.5k 1.0×	905 1.1×	134 0.8×	81 0.8×	107 1.2×	24	1.7k
Guodong Wang China	19	1.1k 0.7×	414 0.5×	120 0.7×	67 0.7×	61 0.7×	82	1.3k
Rui Song China	24	992 0.6×	906 1.1×	281 1.7×	155 1.6×	51 0.6×	95	1.7k
Kin Gwn Lore United States	12	1.2k 0.7×	523 0.6×	88 0.5×	81 0.8×	67 0.8×	19	1.5k
Tao Dai China	17	1.5k 1.0×	913 1.1×	304 1.8×	41 0.4×	91 1.1×	91	1.9k
Sunghyun Cho South Korea	20	2.4k 1.5×	1.3k 1.5×	247 1.5×	42 0.4×	143 1.7×	51	2.6k
Shai Bagon Israel	10	1.6k 1.0×	745 0.9×	100 0.6×	28 0.3×	120 1.4×	17	1.9k
Zhihao Wang China	4	980 0.6×	553 0.7×	75 0.4×	43 0.4×	67 0.8×	7	1.3k
Yiping Deng Sweden	3	1.2k 0.7×	545 0.6×	228 1.4×	60 0.6×	31 0.4×	3	1.4k
Vassilios Chatzis Greece	8	794 0.5×	493 0.6×	248 1.5×	57 0.6×	48 0.6×	29	1.2k

Countries citing papers authored by Daniel Gläsner

Since Specialization

Citations

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

Fields of papers citing papers by Daniel Gläsner

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Gläsner

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Gläsner. A scholar is included among the top collaborators of Daniel Gläsner 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 Daniel Gläsner. Daniel Gläsner is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

17 of 17 papers shown

1.

Jayasumana, Sadeep, Srikumar Ramalingam, Andreas Veit, et al.. (2024). Rethinking FID: Towards a Better Evaluation Metric for Image Generation. 9307–9315. 45 indexed citations breakdown →

2.

Jayasumana, Sadeep, Daniel Gläsner, Srikumar Ramalingam, et al.. (2024). MarkovGen: Structured Prediction for Efficient Text-to-Image Generation. 9316–9325. 1 indexed citations

3.

Kolberg, Jascha, Daniel Gläsner, Ralph Breithaupt, et al.. (2021). On the Effectiveness of Impedance-Based Fingerprint Presentation Attack Detection. Sensors. 21(17). 5686–5686. 11 indexed citations

4.

Bhojanapalli, Srinadh, Ayan Chakrabarti, Daniel Gläsner, et al.. (2021). Understanding Robustness of Transformers for Image Classification. arXiv (Cornell University). 10231–10241. 1 indexed citations

5.

Bhojanapalli, Srinadh, Ayan Chakrabarti, Daniel Gläsner, et al.. (2021). Understanding Robustness of Transformers for Image Classification. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 10211–10221. 227 indexed citations breakdown →

6.

Wang, Jialiang, Daniel Gläsner, & Todd Zickler. (2017). Toward Perceptually-Consistent Stereo: A Scanline Study. 17. 1557–1565. 2 indexed citations

7.

Wang, Jialiang, Daniel Gläsner, & Todd Zickler. (2017). A dynamic programming algorithm for perceptually consistent stereo. Digital Access to Scholarship at Harvard (DASH) (Harvard University). 1 indexed citations

8.

Gläsner, Daniel, Pascal Fua, Todd Zickler, & Lihi Zelnik‐Manor. (2015). Hot or Not: Exploring Correlations between Appearance and Temperature. 3997–4005. 12 indexed citations

9.

Kovalsky, Shahar Z., et al.. (2015). A Global Approach for Solving Edge-Matching Puzzles. SIAM Journal on Imaging Sciences. 8(2). 916–938. 7 indexed citations

10.

Gläsner, Daniel, Todd Zickler, & Anat Levin. (2014). A reflectance display. ACM Transactions on Graphics. 33(4). 1–12. 16 indexed citations

11.

Levin, Anat, Daniel Gläsner, Ying Xiong, et al.. (2013). High Spatial Resolution BRDFs with Metallic powders Using Wave Optics Analysis. DSpace@MIT (Massachusetts Institute of Technology). 1 indexed citations

12.

Gläsner, Daniel, et al.. (2013). Accurate Blur Models vs. Image Priors in Single Image Super-resolution. 2832–2839. 135 indexed citations

13.

Levin, Anat, Daniel Gläsner, Ying Xiong, et al.. (2013). Fabricating BRDFs at high spatial resolution using wave optics. ACM Transactions on Graphics. 32(4). 1–14. 1 indexed citations

14.

Gläsner, Daniel, Meirav Galun, Sharon Alpert, Ronen Basri, & Gregory Shakhnarovich. (2012). Viewpoint-aware object detection and continuous pose estimation. Image and Vision Computing. 30(12). 923–933. 25 indexed citations

15.

Gläsner, Daniel, Meirav Galun, Sharon Alpert, Ronen Basri, & Gregory Shakhnarovich. (2011). Viewpoint-aware object detection and pose estimation. 1275–1282. 60 indexed citations

16.

Gläsner, Daniel, Shiv Vitaladevuni, & Ronen Basri. (2011). Contour-based joint clustering of multiple segmentations. 2385–2392. 18 indexed citations

17.

Gläsner, Daniel, Shai Bagon, & Michal Irani. (2009). Super-resolution from a single image. 349–356. 1234 indexed citations breakdown →

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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