Kristóf Karacs

Gergely Dinya, András Gelencsér, Krisztina Kupán et al.

Current research workflows for precise video segmentation are often forced into a compromise between labor-intensive manual curation, costly commercial platforms, and/or privacy-compromising cloud-based services. The demand for high-fidelity video instance segmentation in research is often hindered by the bottleneck of manual annotation and the privacy concerns of cloud-based tools. We present SAMannot, an open-source, local framework that integrates the Segment Anything Model 2 (SAM2) into a human-in-the-loop workflow. To address the high resource requirements of foundation models, we modified the SAM2 dependency and implemented a processing layer that minimizes computational overhead and maximizes throughput, ensuring a highly responsive user interface. Key features include persistent instance identity management, an automated ``lock-and-refine'' workflow with barrier frames, and a mask-skeletonization-based auto-prompting mechanism. SAMannot facilitates the generation of research-ready datasets in YOLO and PNG formats alongside structured interaction logs. Verified through animal behavior tracking use-cases and subsets of the LVOS and DAVIS benchmark datasets, the tool provides a scalable, private, and cost-effective alternative to commercial platforms for complex video annotation tasks.

András Attila Sulyok, Kristóf Karacs

Partially Observable Markov Decision Process (POMDP) is a framework applicable to many real world problems. In this work, we propose an approach to solve POMDPs with multimodal belief by relying on a policy that solves the fully observable version. By defininig a new, mixture value function based on the value function from the fully observable variant, we can use the corresponding greedy policy to solve the POMDP itself. We develop the mathematical framework necessary for discussion, and introduce a benchmark built on the task of Reconnaissance Blind TicTacToe. On this benchmark, we show that our policy outperforms policies ignoring the existence of multiple modes.

Gergely Dinya, Péter Halász, András Lőrincz et al.

We present a fast, spatio-temporal scene understanding framework based on Vision Gated Generative Transformers (VGGT). The proposed pipeline is designed to enable efficient, close to real-time performance, supporting applications including assistive navigation. To achieve continuous updates of the 3D scene representation, we process the image flow with a sliding window, aligning submaps, thereby overcoming VGGT's high memory demands. We exploit the VGGT tracking head to aggregate 2D semantic instance masks into 3D objects. To allow for temporal consistency and richer contextual reasoning the system stores timestamps and instance-level identities, thereby enabling the detection of changes in the environment. We evaluate the approach on well-known benchmarks and custom datasets specifically designed for assistive navigation scenarios. The results demonstrate the applicability of the framework to real-world scenarios.