Search Results for author:
Found 16 papers, 5 papers with code
How to Complete Domain Tuning while Keeping General Ability in LLM: Adaptive Layer-wise and Element-wise Regularization
Large Language Models (LLMs) exhibit strong general-purpose language capabilities.
Gradual Vigilance and Interval Communication: Enhancing Value Alignment in Multi-Agent Debates
To apply MAD to value alignment, we examine the relationship between the helpfulness and harmlessness of debate outcomes and individual responses, and propose a MAD based framework Gradual Vigilance and Interval Communication (GVIC).
Localization, balance and affinity: a stronger multifaceted collaborative salient object detector in remote sensing images
Despite significant advancements in salient object detection(SOD) in optical remote sensing images(ORSI), challenges persist due to the intricate edge structures of ORSIs and the complexity of their contextual relationships.
COMET: "Cone of experience" enhanced large multimodal model for mathematical problem generation
However, the traditional method of separating problem solving from problem generation and the mainstream fine-tuning framework of monotonous data structure with homogeneous training objectives limit the application of large multimodal model in mathematical problem generation.
PTA: Enhancing Multimodal Sentiment Analysis through Pipelined Prediction and Translation-based Alignment
In contrast, a pipeline framework first identifies aspects through MATE (Multimodal Aspect Term Extraction) and then aligns these aspects with image patches for sentiment classification (MASC: Multimodal Aspect-Oriented Sentiment Classification).
Aspect-Based Sentiment Analysis
Multimodal Sentiment Analysis
+2
YOLOOC: YOLO-based Open-Class Incremental Object Detection with Novel Class Discovery
Because of its use in practice, open-world object detection (OWOD) has gotten a lot of attention recently.
Metamorpheus: Interactive, Affective, and Creative Dream Narration Through Metaphorical Visual Storytelling
It also remains unknown how technologies such as generative AI models can facilitate the meaning making process, and ultimately support affective mindfulness.
"It Felt Like Having a Second Mind": Investigating Human-AI Co-creativity in Prewriting with Large Language Models
This collaborative process champions the human in a dominant role, in addition to mixed and shifting levels of initiative that exist between humans and LLMs.
Saliency Regularization for Self-Training with Partial Annotations
In this paper, we propose saliency regularization (SR) for a novel self-training framework.
Informative knowledge distillation for image anomaly segmentation
However, rare attention has been paid to the overfitting problem caused by the inconsistency between the capacity of the neural network and the amount of knowledge in this scheme.
Ranked #44 on
Anomaly Detection
on MVTec AD
(Segmentation AUPRO metric)
Position Encoding Enhanced Feature Mapping for Image Anomaly Detection
Image anomaly detection is an important stage for automatic visual inspection in intelligent manufacturing systems.
Ranked #17 on
Anomaly Detection
on MVTec AD
(Segmentation AUPRO metric)
Coarse-To-Fine Incremental Few-Shot Learning
Such images form a new training set (i. e., support set) so that the incremental model is hoped to recognize a basenji (i. e., query) as a basenji next time.
Unsupervised Image Anomaly Detection and Segmentation Based on Pre-trained Feature Mapping
This paper proposes a novel framework, named as Pre-trained Feature Mapping (PFM), for unsupervised image anomaly detection and segmentation.
Ranked #75 on
Anomaly Detection
on MVTec AD
(using extra training data)
Industrial Image Anomaly Localization Based on Gaussian Clustering of Pretrained Feature
Anomaly localization is valuable for improvement of complex production processing in smart manufacturing system.
Ranked #99 on
Anomaly Detection
on MVTec AD
DexPilot: Vision Based Teleoperation of Dexterous Robotic Hand-Arm System
Teleoperation offers the possibility of imparting robotic systems with sophisticated reasoning skills, intuition, and creativity to perform tasks.
Robust Bayesian Compressed sensing
We consider the problem of robust compressed sensing whose objective is to recover a high-dimensional sparse signal from compressed measurements corrupted by outliers.
