activity
20222024
most citedWeakly Supervised Video Salient Object Detection via Point Supervision

1 citations · 2 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2024

All rivers run into the sea: Unified Modality Brain-like Emotional Central Mechanism

Xinji Mai, Junxiong Lin, Haoran Wang +10

In the field of affective computing, fully leveraging information from a variety of sensory modalities is essential for the comprehensive understanding and processing of human emot…

cs.MM2024

PG-Attack: A Precision-Guided Adversarial Attack Framework Against Vision Foundation Models for Autonomous Driving

Jiyuan Fu, Zhaoyu Chen, Kaixun Jiang +3

Vision foundation models are increasingly employed in autonomous driving systems due to their advanced capabilities. However, these models are susceptible to adversarial attacks, p…

cs.CV2024

Suppressing Uncertainties in Degradation Estimation for Blind Super-Resolution

Junxiong Lin, Zeng Tao, Xuan Tong +10

The problem of blind image super-resolution aims to recover high-resolution (HR) images from low-resolution (LR) images with unknown degradation modes. Most existing methods model…

cs.CV2024

ClickVOS: Click Video Object Segmentation

Pinxue Guo, Lingyi Hong, Xinyu Zhou +7

Video Object Segmentation (VOS) task aims to segment objects in videos. However, previous settings either require time-consuming manual masks of target objects at the first frame d…

cs.CV20231 cited

Towards End-to-End Unsupervised Saliency Detection with Self-Supervised Top-Down Context

Yicheng Song, Shuyong Gao, Haozhe Xing +3

Unsupervised salient object detection aims to detect salient objects without using supervision signals eliminating the tedious task of manually labeling salient objects. To improve…

cs.CV2023

Plug-and-Play Feature Generation for Few-Shot Medical Image Classification

Qianyu Guo, Huifang Du, Xing Jia +4

Few-shot learning (FSL) presents immense potential in enhancing model generalization and practicality for medical image classification with limited training data; however, it still…