b'Yu-Xiong Wang'

research

∙ 08/31/2023

InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusion

This paper addresses a novel task of anticipating 3D human-object intera...

0 Sirui Xu, et al. ∙

research

∙ 08/08/2023

An Empirical Analysis of Range for 3D Object Detection

LiDAR-based 3D detection plays a vital role in autonomous navigation. Su...

0 Neehar Peri, et al. ∙

research

∙ 08/03/2023

Revisiting Deformable Convolution for Depth Completion

Depth completion, which aims to generate high-quality dense depth maps f...

0 Xinglong Sun, et al. ∙

research

∙ 07/28/2023

Semi-Supervised Object Detection in the Open World

Existing approaches for semi-supervised object detection assume a fixed ...

0 Garvita Allabadi, et al. ∙

research

∙ 06/24/2023

Is Pre-training Truly Better Than Meta-Learning?

In the context of few-shot learning, it is currently believed that a fix...

0 Brando Miranda, et al. ∙

research

∙ 06/08/2023

Stochastic Multi-Person 3D Motion Forecasting

This paper aims to deal with the ignored real-world complexities in prio...

0 Sirui Xu, et al. ∙

research

∙ 05/05/2023

DualCross: Cross-Modality Cross-Domain Adaptation for Monocular BEV Perception

Closing the domain gap between training and deployment and incorporating...

0 Yunze Man, et al. ∙

research

∙ 05/04/2023

NeuralEditor: Editing Neural Radiance Fields via Manipulating Point Clouds

This paper proposes NeuralEditor that enables neural radiance fields (Ne...

0 Jun-Kun Chen, et al. ∙

research

∙ 05/04/2023

Contrastive Mean Teacher for Domain Adaptive Object Detectors

Object detectors often suffer from the domain gap between training (sour...

0 Shengcao Cao, et al. ∙

research

∙ 03/27/2023

Object Discovery from Motion-Guided Tokens

Object discovery – separating objects from the background without manual...

1 Zhipeng Bao, et al. ∙

research

∙ 02/09/2023

Diverse Human Motion Prediction Guided by Multi-Level Spatial-Temporal Anchors

Predicting diverse human motions given a sequence of historical poses ha...

0 Sirui Xu, et al. ∙

research

∙ 02/07/2023

Standing Between Past and Future: Spatio-Temporal Modeling for Multi-Camera 3D Multi-Object Tracking

This work proposes an end-to-end multi-camera 3D multi-object tracking (...

0 Ziqi Pang, et al. ∙

research

∙ 10/27/2022

Do Pre-trained Models Benefit Equally in Continual Learning?

Existing work on continual learning (CL) is primarily devoted to develop...

0 Kuan-Ying Lee, et al. ∙

research

∙ 10/18/2022

CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations

Although reinforcement learning has found widespread use in dense reward...

0 Kai Yan, et al. ∙

research

∙ 10/10/2022

Learning with an Evolving Class Ontology

Lifelong learners must recognize concept vocabularies that evolve over t...

0 Zhiqiu Lin, et al. ∙

research

∙ 08/11/2022

PointTree: Transformation-Robust Point Cloud Encoder with Relaxed K-D Trees

Being able to learn an effective semantic representation directly on raw...

0 Jun-Kun Chen, et al. ∙

research

∙ 08/02/2022

The Curse of Low Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence

Recently, it has been observed that a transfer learning solution might b...

0 Brando Miranda, et al. ∙

research

∙ 06/10/2022

Is Self-Supervised Learning More Robust Than Supervised Learning?

Self-supervised contrastive learning is a powerful tool to learn visual ...

8 Yuanyi Zhong, et al. ∙

research

∙ 06/09/2022

Beyond RGB: Scene-Property Synthesis with Neural Radiance Fields

Comprehensive 3D scene understanding, both geometrically and semanticall...

0 Mingtong Zhang, et al. ∙

research

∙ 03/27/2022

Long-Tailed Recognition via Weight Balancing

In the real open world, data tends to follow long-tailed class distribut...

0 Shaden Alshammari, et al. ∙

research

∙ 03/18/2022

Discovering Objects that Can Move

This paper studies the problem of object discovery – separating objects ...

13 Zhipeng Bao, et al. ∙

research

∙ 12/24/2021

Does MAML Only Work via Feature Re-use? A Data Centric Perspective

Recent work has suggested that a good embedding is all we need to solve ...

0 Brando Miranda, et al. ∙

research

∙ 12/24/2021

The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalence

Recently, it has been observed that a transfer learning solution might b...

0 Brando Miranda, et al. ∙

research

∙ 12/13/2021

Embracing Single Stride 3D Object Detector with Sparse Transformer

In LiDAR-based 3D object detection for autonomous driving, the ratio of ...

3 Lue Fan, et al. ∙

research

∙ 10/06/2021

On the Importance of Firth Bias Reduction in Few-Shot Classification

Learning accurate classifiers for novel categories from very few example...

12 Saba Ghaffari, et al. ∙

research

∙ 09/20/2021

On the Importance of Distractors for Few-Shot Classification

Few-shot classification aims at classifying categories of a novel task b...

0 Rajshekhar Das, et al. ∙

research

∙ 08/20/2021

Pixel Contrastive-Consistent Semi-Supervised Semantic Segmentation

We present a novel semi-supervised semantic segmentation method which jo...

0 Yuanyi Zhong, et al. ∙

research

∙ 06/25/2021

Generative Modeling for Multi-task Visual Learning

Generative modeling has recently shown great promise in computer vision,...

0 Zhipeng Bao, et al. ∙

research

∙ 05/04/2021

Hallucination Improves Few-Shot Object Detection

Learning to detect novel objects from few annotated examples is of great...

0 Weilin Zhang, et al. ∙

research

∙ 04/12/2021

Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection

Training on datasets with long-tailed distributions has been challenging...

27 Nadine Chang, et al. ∙

research

∙ 03/30/2021

DAP: Detection-Aware Pre-training with Weak Supervision

This paper presents a detection-aware pre-training (DAP) approach, which...

0 Yuanyi Zhong, et al. ∙

research

∙ 11/19/2020

Cooperating RPN's Improve Few-Shot Object Detection

Learning to detect an object in an image from very few training examples...

0 Weilin Zhang, et al. ∙

research

∙ 08/22/2020

Few-Shot Learning with Intra-Class Knowledge Transfer

We consider the few-shot classification task with an unbalanced dataset,...

23 Vivek Roy, et al. ∙

research

∙ 08/17/2020

Alpha Net: Adaptation with Composition in Classifier Space

Deep learning classification models typically train poorly on classes wi...

0 Nadine Chang, et al. ∙

research

∙ 08/16/2020

Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis

Generative modeling has recently shown great promise in computer vision,...

0 Zhipeng Bao, et al. ∙

research

∙ 05/21/2020

Towards Streaming Image Understanding

Embodied perception refers to the ability of an autonomous agent to perc...

0 Mengtian Li, et al. ∙

research

∙ 11/29/2019

Learning Generalizable Representations via Diverse Supervision

The problem of rare category recognition has received a lot of attention...

9 Ziqi Pang, et al. ∙

research

∙ 07/18/2019

Growing a Brain: Fine-Tuning by Increasing Model Capacity

CNNs have made an undeniable impact on computer vision through the abili...

2 Yu-Xiong Wang, et al. ∙

research

∙ 05/28/2019

Image Deformation Meta-Networks for One-Shot Learning

Humans can robustly learn novel visual concepts even when images undergo...

0 Zitian Chen, et al. ∙

research

∙ 12/21/2018

Learning Compositional Representations for Few-Shot Recognition

One of the key limitations of modern deep learning based approaches lies...

10 Pavel Tokmakov, et al. ∙

research

∙ 01/16/2018

Low-Shot Learning from Imaginary Data

Humans can quickly learn new visual concepts, perhaps because they can e...

0 Yu-Xiong Wang, et al. ∙

Yu-Xiong Wang

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