6 accepted papers
Feature matching is a critical task in geometric perception, yet existing methods often struggle under domain shifts and illumination changes due to reliance on visual-only learning and expensive 3D supervision. In this paper, we present TextFM, the first language-guided feature matching framework t
Routine visual inspections of concrete structures are imperative for upholding the safety and integrity of critical infrastructure. Such visual inspections sometimes happen under low-light conditions, e.g., checking for bridge health. Crack segmentation under such conditions is challenging due to th…
Many learning-based low light image enhancement (LLIE) algorithms are based on the Retinex theory. However, the Retinex-based decomposition models introduce corruptions which limit their enhancement performance. In this paper, we propose a Latent Disentangle-based Enhancement Network (LDE-Net) for l…
"In this paper, we tackle the problem of RGB-D Semantic Segmentation. The key challenges in solving this problem lie in 1) how to extract features from depth sensor data and 2) how to effectively fuse the features extracted from the two modalities. For the first challenge, we found that the depth in…
Activity recognition has become an important function in many emerging computer vision applications e.g. automatic video surveillance system, human-computer interaction application, and video recommendation system, etc. In this paper, we propose a novel semantics based group activity recognition sch…
Existing human action recognition systems for 3D sequences obtained from the depth camera are designed to cope with only one action category, either single-person action or two-person interaction, and are difficult to be extended to scenarios where both action categories co-exist. In this paper, we…