9 accepted papers
Traditional on-policy Reinforcement Learning with Verifiable Rewards (RLVR) frameworks suffer from experience waste and reward homogeneity, which directly hinders learning efficiency on difficult samples during large language models post-training. In this paper, we introduce Batch Adaptation Policy…
Growing renewable penetration introduces substantial uncertainty into power system operations, necessitating frequent adaptation of dispatch objectives and constraints and challenging expertise-intensive, near-real-time modeling workflows. Large Language Models (LLMs) provide a promising avenue for …
Large real-world robot datasets hold great potential for developing generalist robot policies, but scaling real-world data collection is time-consuming, costly, and resource-intensive. Simulation offers a promising solution, with recent advances in generative AI and synthetic data generation tools e…
Recent years, generative adversarial networks (GANs) have gained significant prominence in single image super-resolution (SISR) tasks. This can mainly be attributed to their exceptional ability to generate intricate details. However, the instability and lack of realism in the details generated by GA…
In recent years, although Transformer has been introduced into stereo image super-resolution and accomplished great advances, the long-range complementary information in stereo images hasn’t been fully utilized. In view of beneficial non-local prior knowledge in both intra-view and cross-view, we pr…
Part-prototype networks (e.g., ProtoPNet, ProtoTree, and ProtoPool) have attracted broad research interest for their intrinsic interpretability and comparable accuracy to non-interpretable counterparts. However, recent works find that the interpretability from prototypes is fragile, due to the seman…
Recently, iterative optimization-based approaches have gained tremendous progress in the field of stereo matching. However, it still remains a challenge to accurately estimate disparity for occlusion and textureless regions. To address this challenge, we present the Global Matching-Optimization Ster…
Transformer-based methods have shown impressive potential in many low-level vision tasks but are rarely used for low-light image enhancement (LLIE). Direct use of Transformer in LLIE will bring unnatural visual effects. This phenomenon encourages us to attempt to learn from the theory of Retinex. Af…
Few-shot semantic segmentation aims to learn new knowledge rapidly with very few annotated data to segment novel classes. Recent methods follow a metric learning framework with prototypes for foreground representation [1]. However, representing support images by one or more prototypes may face probl…