7 papers
LDFE: Laplacian Decoupled Feature Enhancement Block for Dual-Stream CNN-based RGB-IR Object Detection
Wenhao Dong, Xiaoyan Luo, Linlin Yang +4
The complementary information between RGB and IR images can significantly enhance object detection performance under extreme conditions. Existing methods prefer dual-stream CNN bac…
Teaching Vision-Language-Action Models What to See and Where to Look
Yuguang Yang, Canyu Chen, Zhewen Tan +10
Vision-Language-Action (VLA) models have emerged as a promising paradigm for end-to-end autonomous driving. However, existing VLAs' training relies heavily on text-centric visual q…
CL-CLIP: CLIP-Based Continual Learning Framework with Cost-Volume Category Decoupling for Object Detection
Zihan Liu, Yuguang Yang, Shengjie Su +5
Continual Object Detection (COD) requires a detector to acquire new categories over time while preserving previously learned ones. This goal is closely related to open-vocabulary d…
FAIR-Calib: Frontier-Aware Instability-Reweighted Calibration for Post-Training Quantization of Diffusion Large Language Models
Haoyu Huang, Linlin Yang, Sheng Xu +5
Diffusion Large Language Models (dLLMs) refine tokens iteratively but commit them irreversibly, leading to a "stability lag" where early decisions remain fragile even after being w…
Devil is in Narrow Policy: Unleashing Exploration in Driving VLA Models
Canyu Chen, Yuguang Yang, Zhewen Tan +10
We identify a fundamental Narrow Policy limitation undermining the performance of autonomous VLA models, where driving Imitation Learning (IL) tends to collapse exploration and lim…
Noise-Robust Tiny Object Localization with Flows
Huixin Sun, Linlin Yang, Ronyu Chen +4
Despite significant advances in generic object detection, a persistent performance gap remains for tiny objects compared to normal-scale objects. We demonstrate that tiny objects a…