6 accepted papers
Open vocabulary semantic segmentation is a hot topic in research, focusing on segmenting and recognizing a diverse array of categories in varied environments, including those previously unknown, thereby holding significant practical value. Mainstream studies utilize the CLIP model for direct semanti…
Open-World Few-Shot Learning (OFSL) is a crucial research field dedicated to accurately identifying target samples in scenarios where data is limited and labels are unreliable. This research holds significant practical implications and is highly relevant to real-world applications. Recently, the adv…
Open-World Few-Shot Learning (OFSL) is a critical field of research concentrating on the precise identification of target samples in environments with scarce data and unreliable labels thus possessing substantial practical significance. Recently the evolution of foundation models like CLIP has revea…
Modern Natural Language Processing (NLP) models expose under-sensitivity towards text rubbish examples. The text rubbish example is the heavily modified input text which is nonsensical to humans but does not change the model’s prediction. Prior work crafts rubbish examples by iteratively deleting wo…
Underwater image restoration is a fundamental problem in image processing and computer vision. It has broad application prospects for underwater operations, especially underwater robot operations. The challenging work is how to keep the color authenticity of the captured underwater image. In this pa…
Recently, many existing fully supervised methods for point cloud classification have strongly promoted the development of point cloud learning. However, these methods require a lot of labeled data as support, which is challenging to obtain. To alleviate this problem, we propose a novel few-shot poin…