Search Results for author:
Found 15 papers, 6 papers with code
Image Segmentation in Foundation Model Era: A Survey
This survey seeks to fill this gap by providing a thorough review of cutting-edge research centered around FM-driven image segmentation.
General Impedance Modeling for Modular Multilevel Converter with Grid-forming and Grid-following Control
However, the existing MMC impedance models usually lack explicit expressions and general modeling procedure for different control strategies.
Audio-Visual Segmentation via Unlabeled Frame Exploitation
NFs, temporally adjacent to the labeled frame, often contain rich motion information that assists in the accurate localization of sounding objects.
AttrSeg: Open-Vocabulary Semantic Segmentation via Attribute Decomposition-Aggregation
The results show the superior performance of attribute decomposition-aggregation.
Exploiting Counter-Examples for Active Learning with Partial labels
In this setting, an oracle annotates the query samples with partial labels, relaxing the oracle from the demanding accurate labeling process.
Multi-Modal Prototypes for Open-World Semantic Segmentation
To be specific, unlike the straightforward combination of bi-modal clues, we decompose the high-level language information as multi-aspect prototypes and aggregate the low-level visual information as more semantic prototypes, on basis of which, a fine-grained complementary fusion makes the multi-modal prototypes more powerful and accurate to promote the prediction.
Prediction of Post-Operative Renal and Pulmonary Complications Using Transformers
This work highlights the potential of deep learning techniques, specifically transformer-based models, in revolutionizing the healthcare industry's approach to postoperative care.
Monte Carlo Linear Clustering with Single-Point Supervision is Enough for Infrared Small Target Detection
The core idea of this work is to recover the per-pixel mask of each target from the given single point label by using clustering approaches, which looks simple but is indeed challenging since targets are always insalient and accompanied with background clutters.
DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discovery
However, the challenges exist as there is one structural difference between generative and discriminative models, which limits the direct use.
Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation
Motivated by this simple but effective learning pattern, we propose a General-Specific Learning Mechanism (GSLM) to explicitly drive a coarse-grained CAM to a fine-grained pseudo mask.
Exploiting Class Activation Value for Partial-Label Learning
As the first contribution, we empirically show that the class activation map (CAM), a simple technique for discriminating the learning patterns of each class in images, is surprisingly better at making accurate predictions than the model itself on selecting the true label from candidate labels.
Complementary Patch for Weakly Supervised Semantic Segmentation
Therefore, a CAM with more information related to object seeds can be obtained by narrowing down the gap between the sum of CAMs generated by the CP Pair and the original CAM.
An Improved Algorithm of Robot Path Planning in Complex Environment Based on Double DQN
Deep Q Network (DQN) has several limitations when applied in planning a path in environment with a number of dilemmas according to our experiment.
Adversarial Feature Selection against Evasion Attacks
Pattern recognition and machine learning techniques have been increasingly adopted in adversarial settings such as spam, intrusion and malware detection, although their security against well-crafted attacks that aim to evade detection by manipulating data at test time has not yet been thoroughly assessed.
Relative distance features for gait recognition with Kinect
This study focuses on human recognition with gait feature obtained by Kinect and shows that gait feature can effectively distinguish from different human beings through a novel representation -- relative distance-based gait features.
