Demo Data from EDBT 2026 / reviewers in the wild / expert
Alexander Swerdlow
dblp:323/8044
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2025
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 since 2021Systems, architecture and hardware · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Vision and language · 70% 3D vision · 23% Segmentation and scene understanding · 7% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › Vision and language › 3d vision and language
3d language grounding |
0.9 | 1 | 2025 | Unifying 2D and 3D Vision-Language Understanding · ICML 2025 |
Computer vision › 3D vision
3d scene understanding |
0.9 | 1 | 2025 | Unifying 2D and 3D Vision-Language Understanding · ICML 2025 |
Computer vision › Vision and language › 3d vision and language
3d vision-language grounding |
0.9 | 1 | 2025 | Unifying 2D and 3D Vision-Language Understanding · ICML 2025 |
Computer vision › Vision and language › 3d vision and language
3d vision-language understanding |
0.9 | 1 | 2025 | Unifying 2D and 3D Vision-Language Understanding · ICML 2025 |
Methods — techniques the papers use, named apart from their topics
transfer learning · 0.9language-conditioned mask decoder · 0.92d-to-3d lifting · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Unifying 2D and 3D Vision-Language UnderstandingabstractProgress in 3D vision-language learning has been hindered by the scarcity of large-scale 3D datasets. We introduce UniVLG, a unified architecture for 2D and 3D vision-language understanding that bridges the gap between existing 2D-centric models and the rich 3D sensory data available in embodied systems. Our approach initializes most model weights from pre-trained 2D models and trains on both 2D and 3D vision-language data. We propose a novel language-conditioned mask decoder shared across 2D and 3D modalities to ground objects effectively in both RGB and RGB-D images, outperforming box-based approaches. To further reduce the domain gap between 2D and 3D, we incorporate 2D-to-3D lifting strategies, enabling UniVLG to utilize 2D data to enhance 3D performance. With these innovations, our model achieves state-of-the-art performance across multiple 3D vision-language grounding tasks, demonstrating the potential of transferring advances from 2D vision-language learning to the data-constrained 3D domain. Furthermore, co-training on both 2D and 3D data enhances performance across modalities without sacrificing 2D capabilities. By removing the reliance on 3D mesh reconstruction and ground-truth object proposals, UniVLG sets a new standard for realistic, embodied-aligned evaluation. Code and additional visualizations are available at https://univlg.github.io. Alexander Swerdlow, Sergio Arnaud, Ada Martin, Alexander Sax, Franziska Meier, Katerina Fragkiadaki |
ICML | 2 |
| 2022 | SCALER: A Tough Versatile Quadruped Free-Climber RobotabstractThis paper introduces SCALER, a quadrupedal robot that demonstrates climbing on bouldering walls, over-hangs, ceilings and trotting on the ground. SCALER is one of the first high-degrees of freedom four-limbed robots that can free-climb under the Earth's gravity and one of the most mechanically efficient quadrupeds on the ground. Where other state-of-the-art climbers specialize in climbing, SCALER promises practical free-climbing with payload and ground locomotion, which realizes true versatile mobility. A new climbing gait, SKATE gait, increases the payload by utilizing the SCALER body linkage mechanism. SCALER achieves a maximum normalized locomotion speed of 1.87 /s, or 0.56 m/s on the ground and 1.0 /min, or 0.35 m/min in bouldering wall climbing. Payload capacity reaches 233 % of the SCALER weight on the ground and 35 % on the vertical wall. Our GOAT gripper, a mechanically adaptable underactuated two-finger gripper, successfully grasps convex and non-convex objects and supports SCALER. Yuki Shirai, Xuan Lin, Alexander Schperberg, Hayato Kato, Alexander Swerdlow, Naoya Kumagai, Dennis W. Hong |
IROS | 6 |