9 accepted papers
Tackling multi-object tracking and segmentation (MOTS) can be attributed to a multi-task learning task, i.e., performing Segmentation and Identity Embedding jointly (SIEJ). Unfortunately, achieving optimal SIEJ is non-trivial, as it relies on different spatiotemporal features of objects. Besides, th…
Due to the complementary properties between sensors, multi-sensor fusion can effectively promote accuracy and tackle the challenging scenes in simultaneous localization and mapping (SLAM) tasks. To this end, we propose a novel LiDAR-camera fused method for odometry and mapping using dense colored po
Loop Closure Detection (LCD) is an essential component of Simultaneous Localization and Mapping (SLAM), helping to correct drift errors, facilitate map merging, or both by identifying previously observed scenes. Despite its importance, traditional LCD algorithms based on single sensor such as camera…
Standing out as one of the most widely used tools in Cross-Domain Few-Shot Learning (CDFSL), data augmentation forms the bedrock of numerous recent advancements. However, the current augmentations in CDFSL are limited in their ability to modify high-level semantic attributes, resulting in a lack of…
Visual Question Answering (VQA) models often prioritize language cues over visual knowledge, leading to the "language prior" phenomenon. To address this, researchers have proposed methods to balance language and image information during training and inference. However, these approaches often struggl…
Extrinsic calibration is an essential prerequisite for the applications of camera-LiDAR fusion. Existing methods either suffer from the complex offline setting of man-made targets or tend to produce suboptimal and unrobust results. In this paper, we propose an online two-stage calibration method tha…
Few-shot learning (FSL) aims to enable learning models with the ability to automatically adapt to novel (unseen) domains in open-world scenarios. Nonetheless, there exists a significant disparity between the vast number of new concepts encountered in the open world and the restricted available scale…
This paper explores Deployment Authorization (DPA) as a means of restricting the generalization capabilities of vision models on certain domains to protect intellectual property. Nevertheless, the current advancements in DPA are predominantly confined to fully supervised settings. Such settings requ…
Domain adversarial training is a popular approach for Unsupervised Domain Adaptation (DA). However, the transferability of adversarial training framework may drop greatly on the adaptation tasks with a large distribution divergence between source and target domains. In this paper, we propose a new a…