Search Results for author:
Found 28 papers, 10 papers with code
One for All: Multi-Domain Joint Training for Point Cloud Based 3D Object Detection
The current trend in computer vision is to utilize one universal model to address all various tasks.
Turbo your multi-modal classification with contrastive learning
Contrastive learning has become one of the most impressive approaches for multi-modal representation learning.
Diffusion Model Meets Non-Exemplar Class-Incremental Learning and Beyond
Non-exemplar class-incremental learning (NECIL) is to resist catastrophic forgetting without saving old class samples.
Map Optical Properties to Subwavelength Structures Directly via a Diffusion Model
The inverse design methods proposed for these subwavelength structures are vital to the development of new photonic devices.
OV-Uni3DETR: Towards Unified Open-Vocabulary 3D Object Detection via Cycle-Modality Propagation
Specifically, we propose the cycle-modality propagation, aimed at propagating knowledge bridging 2D and 3D modalities, to support the aforementioned functionalities.
Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey
Semantic segmentation is an important and popular research area in computer vision that focuses on classifying pixels in an image based on their semantics.
G^3-LQ: Marrying Hyperbolic Alignment with Explicit Semantic-Geometric Modeling for 3D Visual Grounding
Specifically the Position Adaptive Geometric Exploring (PAGE) unearths underlying information of 3D objects in the geometric details and spatial relationships perspectives.
Exploring Pose-Aware Human-Object Interaction via Hybrid Learning
Towards these issues we propose a hybrid learning method based on pose-aware HOI feature refinement.
Uni3DETR: Unified 3D Detection Transformer
In this paper, we propose Uni3DETR, a unified 3D detector that addresses indoor and outdoor 3D detection within the same framework.
Identity-Seeking Self-Supervised Representation Learning for Generalizable Person Re-identification
To overcome the barriers of data and annotation, we propose to utilize large-scale unsupervised data for training.
Generalizable Person Re-identification
Representation Learning
Detecting Everything in the Open World: Towards Universal Object Detection
In this paper, we formally address universal object detection, which aims to detect every scene and predict every category.
Generalizable Re-Identification from Videos with Cycle Association
In this paper, we are interested in learning a generalizable person re-identification (re-ID) representation from unlabeled videos.
Domain Generalization
Generalizable Person Re-identification
+1
Reliability-Aware Prediction via Uncertainty Learning for Person Image Retrieval
(3) the data uncertainty and the model uncertainty are jointly learned in a unified network, and they serve as two fundamental criteria for the reliability assessment: if a probe is high-quality (low data uncertainty) and the model is confident in the prediction of the probe (low model uncertainty), the final ranking will be assessed as reliable.
Self-Supervised Learning via Maximum Entropy Coding
To cope with this issue, we propose Maximum Entropy Coding (MEC), a more principled objective that explicitly optimizes on the structure of the representation, so that the learned representation is less biased and thus generalizes better to unseen downstream tasks.
GraphCSPN: Geometry-Aware Depth Completion via Dynamic GCNs
First, unlike previous methods, we leverage convolution neural networks as well as graph neural networks in a complementary way for geometric representation learning.
Portrait Interpretation and a Benchmark
Furthermore, we focus on representation learning for portrait interpretation and propose a baseline that reflects our systematic perspective.
Hybrid Physical Metric For 6-DoF Grasp Pose Detection
6-DoF grasp pose detection of multi-grasp and multi-object is a challenge task in the field of intelligent robot.
Noisy Boundaries: Lemon or Lemonade for Semi-supervised Instance Segmentation?
We construct a framework for semi-supervised instance segmentation by assigning pixel-level pseudo labels.
R(Det)2: Randomized Decision Routing for Object Detection
In this paper, we propose a novel approach to combine decision trees and deep neural networks in an end-to-end learning manner for object detection.
OSKDet: Orientation-Sensitive Keypoint Localization for Rotated Object Detection
By proposing the orientation-sensitive heatmap, OSKDet could learn the shape and direction of rotated target implicitly and has stronger modeling capabilities for rotated representation, which improves the localization accuracy and acquires high quality detection results.
Combating Noise: Semi-supervised Learning by Region Uncertainty Quantification
To combat the noisy labeling, we propose noise-resistant semi-supervised learning by quantifying the region uncertainty.
Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentation
Evaluations are conducted on the English Wiki-727K document segmentation benchmark, a Chinese Wikipedia-based document segmentation dataset we created, and an in-house Chinese spoken document dataset.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
A2-FPN: Attention Aggregation Based Feature Pyramid Network for Instance Segmentation
Learning pyramidal feature representations is crucial for recognizing object instances at different scales.
AdaZoom: Adaptive Zoom Network for Multi-Scale Object Detection in Large Scenes
In this paper, we propose a novel Adaptive Zoom (AdaZoom) network as a selective magnifier with flexible shape and focal length to adaptively zoom the focus regions for object detection.
A^2-FPN: Attention Aggregation based Feature Pyramid Network for Instance Segmentation
Learning pyramidal feature representations is crucial for recognizing object instances at different scales.
Data-Uncertainty Guided Multi-Phase Learning for Semi-Supervised Object Detection
In this paper, we delve into semi-supervised object detection where unlabeled images are leveraged to break through the upper bound of fully-supervised object detection models.
Partial Off-Policy Learning: Balance Accuracy and Diversity for Human-Oriented Image Captioning
Human-oriented image captioning with both high diversity and accuracy is a challenging task in vision+language modeling.
Disentangled Representation for Age-Invariant Face Recognition: A Mutual Information Minimization Perspective
For quantitative measure of the degree of disentanglement, we verify that mutual information can represent as metric.
