6 accepted papers
High Dynamic Range Novel View Synthesis (HDR NVS) seeks to learn an HDR 3D model from Low Dynamic Range (LDR) training images captured under conventional imaging conditions. Current methods primarily focus on static scenes, implicitly assuming all scene elements remain stationary and non-living. How…
As the size of large language models grows exponentially, GPU memory has become a bottleneck for adapting these models to downstream tasks. In this paper, we aim to push the limits of memory-efficient training by minimizing memory usage on model weights, gradients, and optimizer states, within a uni…
In cross-domain visual understanding tasks, models often achieve strong performance on the source domain but suffer severe degradation when applied to target domains with substantial distribution shifts. This challenge is particularly prominent under the zero-shot domain adaptation setting, where ad…
Existing point cloud semantic segmentation models are usually trained and evaluated using data collected under clear weather conditions. Under adverse weather conditions such as rain, snow and fog, point clouds are usually distorted and significant degradation of existing model performance occurs. M…
High Dynamic Range Novel View Synthesis (HDR-NVS) aims to establish a 3D scene HDR model from Low Dynamic Range (LDR) imagery. Typically, multiple-exposure LDR images are employed to capture a wider range of brightness levels in a scene, as a single LDR image cannot represent both the brightest and…
Most existing person re-identification (re-id) methods rely on supervised model learning on per-camera-pair manually labelled pairwise training data. This leads to poor scalability in practical re-id deployment due to the lack of exhaustive identity (ID) labelling of image pairs (both positive and n…