6 accepted papers
Motion estimation in degraded scenes has long been a significant challenge, primarily attributed to substantial scene variations and insufficient training data. Existing approaches typically address this limitation by incorporating additional training strategies or modifying network architectures wi
Although the overall performance of existing optical flow estimation methods has improved rapidly, motion discontinuities caused by large displacements and occlusions remain significant challenges for accurate optical flow estimation. To address this issue, we propose a novel Inject Context Informat…
Large Vision-Language Models (LVLMs) suffer from serious hallucination problems, where the model-generated responses are inconsistent with the visual inputs. Existing hallucination mitigation methods are mainly based on preference alignment and require external human annotations or auxiliary models
Although optical flow estimation has improved significantly in recent years, large displacements and occlusions remain challenging for current methods due to motion discontinuities that may hinder accurate feature correspondences in these regions, leading to degraded performance. To address this cha…
"Between the imaging sensor and the image applications, the hardware image signal processing (ISP) pipelines reconstruct an RGB image from the sensor signal and feed it into downstream tasks. The processing blocks in ISPs depend on a set of tunable hyperparameters that have a complex interaction wit…
Open-vocabulary object detection aims to detect novel object categories beyond the training set. The advanced open-vocabulary two-stage detectors employ instance-level visual-to-visual knowledge distillation to align the visual space of the detector with the semantic space of the Pre-trained Visual-…