Search Results for author:
Found 11 papers, 6 papers with code
Dynamic Generation of Personalities with Large Language Models
We propose a new metric to assess personality generation capability based on this evaluation method.
Debiased Novel Category Discovering and Localization
In recent years, object detection in deep learning has experienced rapid development.
Read to Play (R2-Play): Decision Transformer with Multimodal Game Instruction
Drawing inspiration from the success of multimodal instruction tuning in visual tasks, we treat the visual-based RL task as a long-horizon vision task and construct a set of multimodal game instructions to incorporate instruction tuning into a decision transformer.
Deep Reinforcement Learning with Task-Adaptive Retrieval via Hypernetwork
Deep reinforcement learning algorithms are usually impeded by sampling inefficiency, heavily depending on multiple interactions with the environment to acquire accurate decision-making capabilities.
Online Open-set Semi-supervised Object Detection with Dual Competing Head
To alleviate these issues, this paper proposes an end-to-end online OSSOD framework that improves performance and efficiency: 1) We propose a semi-supervised outlier filtering method that more effectively filters the OOD instances using both labeled and unlabeled data.
Box-Level Active Detection
Active learning selects informative samples for annotation within budget, which has proven efficient recently on object detection.
LODE: Deep Local Deblurring and A New Benchmark
While recent deep deblurring algorithms have achieved remarkable progress, most existing methods focus on the global deblurring problem, where the image blur mostly arises from severe camera shake.
Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification
We refer to these models as 'Experts', and the proposed LFME framework aggregates the knowledge from multiple 'Experts' to learn a unified student model.
Ranked #25 on
Long-tail Learning
on Places-LT
Incremental Few-Shot Learning for Pedestrian Attribute Recognition
Pedestrian attribute recognition has received increasing attention due to its important role in video surveillance applications.
Adaptive Region Embedding for Text Classification
Deep learning models such as convolutional neural networks and recurrent networks are widely applied in text classification.
