Search Results for author:
Found 19 papers, 5 papers with code
Efficient Transferability Assessment for Selection of Pre-trained Detectors
Given this zoo, we adopt 7 target datasets from 5 diverse domains as the downstream target tasks for evaluation.
Open-Vocabulary Object Detection with Meta Prompt Representation and Instance Contrastive Optimization
Without using knowledge distillation, ensemble model or extra training data during detector training, our proposed MIC outperforms previous SOTA methods trained with these complex techniques on LVIS.
Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation
Given a complex text prompt containing multiple concepts including objects, attributes, and relationships, the LLM agent initially decomposes it, which entails the extraction of individual objects, their associated attributes, and the prediction of a coherent scene layout.
CustomVideo: Customizing Text-to-Video Generation with Multiple Subjects
Customized text-to-video generation aims to generate high-quality videos guided by text prompts and subject references.
Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuning
Instruction tuning of Large Vision-language Models (LVLMs) has revolutionized the development of versatile models with zero-shot generalization across a wide range of downstream vision-language tasks.
UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View Representation
Jointly processing information from multiple sensors is crucial to achieving accurate and robust perception for reliable autonomous driving systems.
Ranked #8 on
3D Object Detection
on nuScenes
ContraNeRF: Generalizable Neural Radiance Fields for Synthetic-to-real Novel View Synthesis via Contrastive Learning
In this work, we first investigate the effects of synthetic data in synthetic-to-real novel view synthesis and surprisingly observe that models trained with synthetic data tend to produce sharper but less accurate volume densities.
CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds
We present a novel two-stage fully sparse convolutional 3D object detection framework, named CAGroup3D.
Ranked #1 on
3D Object Detection
on SUN-RGBD
STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion Detection System for Intelligent Connected Vehicles
To address these limitations, we present a novel model for automotive intrusion detection by spatial-temporal correlation features of in-vehicle communication traffic (STC-IDS).
Semi-Supervised Object Detection via Multi-Instance Alignment With Global Class Prototypes
Semi-Supervised object detection (SSOD) aims to improve the generalization ability of object detectors with large-scale unlabeled images.
Transformation Invariant Few-Shot Object Detection
To this end, we propose a simple yet effective Transformation Invariant Principle (TIP) that can be flexibly applied to various meta-learning models for boosting the detection performance on novel class objects.
Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection
In this work, aiming to fully exploit features of annotated novel object and capture fine-grained features of query object, we propose Dense Relation Distillation with Context-aware Aggregation (DCNet) to tackle the few-shot detection problem.
Boosting Few-Shot Learning With Adaptive Margin Loss
Few-shot learning (FSL) has attracted increasing attention in recent years but remains challenging, due to the intrinsic difficulty in learning to generalize from a few examples.
Ranked #1 on
Few-Shot Image Classification
on ImageNet (1-shot)
Few-Shot Learning with Global Class Representations
In this paper, we propose to tackle the challenging few-shot learning (FSL) problem by learning global class representations using both base and novel class training samples.
Large-Scale Few-Shot Learning: Knowledge Transfer With Class Hierarchy
Recently, large-scale few-shot learning (FSL) becomes topical.
Transferrable Feature and Projection Learning with Class Hierarchy for Zero-Shot Learning
Inspired by the fact that an unseen class is not exactly `unseen' if it belongs to the same superclass as a seen class, we propose a novel inductive ZSL model that leverages superclasses as the bridge between seen and unseen classes to narrow the domain gap.
Zero and Few Shot Learning with Semantic Feature Synthesis and Competitive Learning
Specifically, we assume that each synthesised data point can belong to any unseen class; and the most likely two class candidates are exploited to learn a robust projection function in a competitive fashion.
Zero-Shot Fine-Grained Classification by Deep Feature Learning with Semantics
In this paper, to address the two issues, we propose a two-phase framework for recognizing images from unseen fine-grained classes, i. e. zero-shot fine-grained classification.
Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks
Early detection of pulmonary cancer is the most promising way to enhance a patient's chance for survival.
