Many deep learning based radar target detectors rely on range-cell level labels for training, which are expensive to obtain. To reduce the labeling burden, this paper presents a training strategy that uses only range-window level labels. Specifically, two sub-echoes are randomly cropped from the same window echo, resul …
Deep-learning-based radar target detection has substantially improved detection performance, but its effectiveness depends critically on the correctness of the training labels assigned to radar echo samples. In practical applications, training data may contain partially mislabeled samples, which can cause detection mod …
Recent advances in penalty-based methods for stochastic bilevel optimization (SBO) have eliminated the need for second-order derivative oracles. However, for stochastic nonconvex-strongly convex bilevel problems, existing first-order methods typically rely on nested loops and/or large batch sizes for attaining $O(ε^{-6 …
Prior-free 6D object pose tracking seeks to recover the trajectory of an unseen object from a single RGB video without object-specific CAD models, posed reference images, or pose annotations. Geometric foundation models provide complementary object-centric and scene-centric cues, yet SAM3D CAD is indexed by an arbitrar …
Self-rewarding reinforcement learning (RL) enables large language models (LLMs) to self-evolve without human labels. Existing ensemble-based methods construct reward references from rollout groups and assign rewards accordingly. However, a response's reward representation also depends on its randomly sampled group cont …
Precision manipulation with contact-critical interactions is often history-dependent: visually similar observations can correspond to different latent interaction states and therefore require different actions, while small execution errors can alter task outcomes. Policies relying on the current visual observation alon …