Professor Tamer’s InfoLab has had a paper accepted for presentation at ACM KDD 2026, a premier international conference in data science, AI, knowledge discovery, and data mining, to be held from August 9–13, 2026, in Jeju, South Korea.

Figure 1 Example advantages of VisionDES over static ensemble models. Models with red highlights are attacked models.

The accepted paper, titled “VisionDES: Robust and Explainable Dynamic Vision Ensemble,” introduces the first dynamic ensemble selection framework for vision tasks. VisionDES uses deep vision embeddings and approximate nearest-neighbor search to identify a local region of competence for each test image, then dynamically selects and weights the most reliable models for the final predictions. The method is designed to improve robustness under adversarial attacks and distribution shifts while providing novel instance-level interpretability.

Figure 2 Framework of the proposed VisionDES, consisting of three main stages: training, selection, and aggregation.

The paper reports extensive evaluations on several image datasets under clean conditions, adversarial attacks, and distribution shifts. VisionDES outperforms static ensembles and uncertainty-based dynamic ensemble methods, achieving up to 20% higher robust accuracy under strong attacks and 2–3% higher accuracy under distribution shifts.

Figure 3 Interpretability for test images under benign (top) and adversarial (bottom) conditions. We show each model’s behavior in the Region of Competence (RoC), predictions, and RoC samples with their L2 distances (computed via FAISS).

VisionDES strengthens trustworthy computer vision by making ensemble models more adaptive, more robust to adversarial attacks and distribution shifts, and more explainable at the level of individual predictions.

For more details about InfoLab research activities, visit https://infolab.skku.edu