Search Results for author:
Found 16 papers, 11 papers with code
Locate Anything on Earth: Advancing Open-Vocabulary Object Detection for Remote Sensing Community
Results demonstrate the advantages of the LAE-1M dataset and the effectiveness of the LAE-DINO method.
Towards a Generalist and Blind RGB-X Tracker
With the emergence of a single large model capable of successfully solving a multitude of tasks in NLP, there has been growing research interest in achieving similar goals in computer vision.
Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector
This paper studies the challenging cross-domain few-shot object detection (CD-FSOD), aiming to develop an accurate object detector for novel domains with minimal labeled examples.
Ranked #1 on
Few-Shot Object Detection
on MS-COCO (30-shot)
Cross-Domain Few-Shot
Cross-Domain Few-Shot Object Detection
+3
Test-Time Linear Out-of-Distribution Detection
We conduct a thorough investigation theoretically and empirically to analyze and understand the meaning of such a linear trend in OOD detection.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+1
Open-Vocabulary Video Relation Extraction
A comprehensive understanding of videos is inseparable from describing the action with its contextual action-object interactions.
MinD-3D: Reconstruct High-quality 3D objects in Human Brain
In this paper, we introduce Recon3DMind, an innovative task aimed at reconstructing 3D visuals from Functional Magnetic Resonance Imaging (fMRI) signals, marking a significant advancement in the fields of cognitive neuroscience and computer vision.
Learning Disentangled Identifiers for Action-Customized Text-to-Image Generation
Experimental results show that existing subject-driven customization methods fail to learn the representative characteristics of actions and struggle in decoupling actions from context features, including appearance.
On the Importance of Spatial Relations for Few-shot Action Recognition
We are thus motivated to investigate the importance of spatial relations and propose a more accurate few-shot action recognition method that leverages both spatial and temporal information.
StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning
Thus, inspired by vanilla adversarial learning, a novel model-agnostic meta Style Adversarial training (StyleAdv) method together with a novel style adversarial attack method is proposed for CD-FSL.
Ranked #1 on
Cross-Domain Few-Shot
on Plantae
ME-D2N: Multi-Expert Domain Decompositional Network for Cross-Domain Few-Shot Learning
Concretely, to solve the data imbalance problem between the source data with sufficient examples and the auxiliary target data with limited examples, we build our model under the umbrella of multi-expert learning.
TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot Learning
The proposed TGDM framework contains a Mixup-3T network for learning classifiers and a dynamic ratio generation network (DRGN) for learning the optimal mix ratio.
Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning
The key challenge of CD-FSL lies in the huge data shift between source and target domains, which is typically in the form of totally different visual styles.
Ranked #3 on
Cross-Domain Few-Shot
on CUB
Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data
Secondly, a novel disentangle module together with a domain classifier is proposed to extract the disentangled domain-irrelevant and domain-specific features.
Can Action be Imitated? Learn to Reconstruct and Transfer Human Dynamics from Videos
To achieve this, a novel Mesh-based Video Action Imitation (M-VAI) method is proposed by us.
e-ACJ: Accurate Junction Extraction For Event Cameras
Junctions reflect the important geometrical structure information of the image, and are of primary significance to applications such as image matching and motion analysis.
Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition
Humans can easily recognize actions with only a few examples given, while the existing video recognition models still heavily rely on the large-scale labeled data inputs.
Ranked #2 on
Few Shot Action Recognition
on Kinetics-100
