arXiv subjects

Hyeonseo Yang

Publications and source records attributed to Hyeonseo Yang.

3 recordsLinked to original sources

arXiv2025

OBSER: Object-Based Sub-Environment Recognition for Zero-Shot Environmental Inference

We present the Object-Based Sub-Environment Recognition (OBSER) framework, a novel Bayesian framework that infers three fundamental relationships between sub-environments and their constituent objects. In the OBSER framework, metric and self-supervised learning models estimate the object distributions of sub-environments on the latent space to compute these measures. Both theoretically and empirically, we validate the proposed framework by introducing the ($\epsilon,\delta$) statistically separable (EDS) function which indicates the alignment of the representation. Our framework reliably performs inference in open-world and photorealistic environments and outperforms scene-based methods in chained retrieval tasks. The OBSER framework enables zero-shot recognition of environments to achieve autonomous environment understanding.

cs.CV↗

arXiv2025

From Sparse to Dense: Toddler-inspired Reward Transition in Goal-Oriented Reinforcement Learning

Reinforcement learning (RL) agents often face challenges in balancing exploration and exploitation, particularly in environments where sparse or dense rewards bias learning. Biological systems, such as human toddlers, naturally navigate this balance by transitioning from free exploration with sparse rewards to goal-directed behavior guided by increasingly dense rewards. Inspired by this natural progression, we investigate the Toddler-Inspired Reward Transition in goal-oriented RL tasks. Our study focuses on transitioning from sparse to potential-based dense (S2D) rewards while preserving optimal strategies. Through experiments on dynamic robotic arm manipulation and egocentric 3D navigation tasks, we demonstrate that effective S2D reward transitions significantly enhance learning performance and sample efficiency. Additionally, using a Cross-Density Visualizer, we show that S2D transitions smooth the policy loss landscape, resulting in wider minima that improve generalization in RL models. In addition, we reinterpret Tolman's maze experiments, underscoring the critical role of early free exploratory learning in the context of S2D rewards.

cs.LG↗

arXiv2024

Surface-Based Visibility-Guided Uncertainty for Continuous Active 3D Neural Reconstruction

View selection is critical in active 3D neural reconstruction as it impacts the contents of training set and resulting final output quality. Recent view selection strategies emphasize the visibility when evaluating model uncertainty in active 3D reconstruction. However, existing approaches estimate visibility only after the model fully converges, which has confined their application primarily to non-continuous active learning settings. This paper proposes Surface-Based Visibility field (SBV) that successfully estimates the visibility-guided uncertainty in continuous active 3D neural reconstruction. During learning neural implicit surfaces, our model learns rendering uncertainties and infers surface confidence values derived from signed distance functions. It then updates surface confidences using a voxel grid, robustly deducing the surface-based visibility for uncertainties. This approach captures uncertainties across all regions, whether well-defined surfaces or ambiguous areas, ensuring accurate visibility measurement in continuous active learning. Experiments on benchmark datasets-Tanks and Temples, BlendedMVS, Blender, DTU-and the newly proposed imbalanced viewpoint dataset (ImBView) show that view selection based on SBV-guided uncertainty improves performance by up to 11.6% over existing methods, highlighting its effectiveness in challenging reconstruction scenarios.

cs.CV↗