5 papers
ClearAIR: A Human-Visual-Perception-Inspired All-in-One Image Restoration
Xu Zhang, Huan Zhang, Guoli Wang +2
All-in-One Image Restoration (AiOIR) has advanced significantly, offering promising solutions for complex real-world degradations. However, most existing approaches rely heavily on…
RS2-SAM2: Customized SAM2 for Referring Remote Sensing Image Segmentation
Fu Rong, Meng Lan, Qian Zhang +1
Referring Remote Sensing Image Segmentation (RRSIS) aims to segment target objects in remote sensing (RS) images based on textual descriptions. Although Segment Anything Model 2 (S…
UniUIR: Considering Underwater Image Restoration as An All-in-One Learner
Xu Zhang, Huan Zhang, Guoli Wang +3
Existing underwater image restoration (UIR) methods generally only handle color distortion or jointly address color and haze issues, but they often overlook the more complex degrad…
MPG-SAM 2: Adapting SAM 2 with Mask Priors and Global Context for Referring Video Object Segmentation
Fu Rong, Meng Lan, Qian Zhang +1
Referring video object segmentation (RVOS) aims to segment objects in a video according to textual descriptions, which requires the integration of multimodal information and tempor…
Perceive-IR: Learning to Perceive Degradation Better for All-in-One Image Restoration
Xu Zhang, Jiaqi Ma, Guoli Wang +3
Existing All-in-One image restoration methods often fail to perceive degradation types and severity levels simultaneously, overlooking the importance of fine-grained quality percep…