Search Results for author: Tao Kong

Found 23 papers, 11 papers with code

Self-Supervised Learning by Estimating Twin Class Distributions

2 code implementations14 Oct 2021 Feng Wang, Tao Kong, Rufeng Zhang, Huaping Liu, Hang Li

Different from the clustering-based methods which alternate between clustering and learning, our method is a single learning process guided by a unified loss function.

Fine-Grained Image Classification Representation Learning +5

Simultaneous Semantic and Collision Learning for 6-DoF Grasp Pose Estimation

no code implementations5 Aug 2021 Yiming Li, Tao Kong, Ruihang Chu, Yifeng Li, Peng Wang, Lei LI

In a unified framework, we jointly predict the feasible 6-DoF grasp poses, instance semantic segmentation, and collision information.

Multi-Task Learning Pose Estimation +1

Learning to Design and Construct Bridge without Blueprint

no code implementations5 Aug 2021 Yunfei Li, Tao Kong, Lei LI, Yifeng Li, Yi Wu

In this task, the robot needs to first design a feasible bridge architecture for arbitrarily wide cliffs and then manipulate the blocks reliably to construct a stable bridge according to the proposed design.

Curriculum Learning Motion Planning

SOLO: A Simple Framework for Instance Segmentation

no code implementations30 Jun 2021 Xinlong Wang, Rufeng Zhang, Chunhua Shen, Tao Kong, Lei LI

Besides instance segmentation, our method yields state-of-the-art results in object detection (from our mask byproduct) and panoptic segmentation.

Image Matting Instance Segmentation +2

Adversarial Option-Aware Hierarchical Imitation Learning

1 code implementation10 Jun 2021 Mingxuan Jing, Wenbing Huang, Fuchun Sun, Xiaojian Ma, Tao Kong, Chuang Gan, Lei LI

In particular, we propose an Expectation-Maximization(EM)-style algorithm: an E-step that samples the options of expert conditioned on the current learned policy, and an M-step that updates the low- and high-level policies of agent simultaneously to minimize the newly proposed option-occupancy measurement between the expert and the agent.

Imitation Learning

Sparse R-CNN: End-to-End Object Detection with Learnable Proposals

4 code implementations CVPR 2021 Peize Sun, Rufeng Zhang, Yi Jiang, Tao Kong, Chenfeng Xu, Wei Zhan, Masayoshi Tomizuka, Lei LI, Zehuan Yuan, Changhu Wang, Ping Luo

In our method, however, a fixed sparse set of learned object proposals, total length of $N$, are provided to object recognition head to perform classification and location.

Object Detection Object Recognition

Dense Contrastive Learning for Self-Supervised Visual Pre-Training

3 code implementations CVPR 2021 Xinlong Wang, Rufeng Zhang, Chunhua Shen, Tao Kong, Lei LI

Compared to the baseline method MoCo-v2, our method introduces negligible computation overhead (only <1% slower), but demonstrates consistently superior performance when transferring to downstream dense prediction tasks including object detection, semantic segmentation and instance segmentation; and outperforms the state-of-the-art methods by a large margin.

Contrastive Learning Image Classification +4

SOLOv2: Dynamic and Fast Instance Segmentation

11 code implementations NeurIPS 2020 Xinlong Wang, Rufeng Zhang, Tao Kong, Lei LI, Chunhua Shen

Importantly, we take one step further by dynamically learning the mask head of the object segmenter such that the mask head is conditioned on the location.

Instance Segmentation Object Detection +1

FoveaBox: Beyound Anchor-based Object Detection

no code implementations ICLR 2020 Tao Kong, Fuchun Sun, Huaping Liu, Yuning Jiang, Lei LI, Jianbo Shi

While almost all state-of-the-art object detectors utilize predefined anchors to enumerate possible locations, scales and aspect ratios for the search of the objects, their performance and generalization ability are also limited to the design of anchors.

Object Detection

Task-Aware Monocular Depth Estimation for 3D Object Detection

1 code implementation17 Sep 2019 Xinlong Wang, Wei Yin, Tao Kong, Yuning Jiang, Lei LI, Chunhua Shen

In this paper, we first analyse the data distributions and interaction of foreground and background, then propose the foreground-background separated monocular depth estimation (ForeSeE) method, to estimate the foreground depth and background depth using separate optimization objectives and depth decoders.

3D Object Detection 3D Object Recognition +1

Attention-based Transfer Learning for Brain-computer Interface

no code implementations25 Apr 2019 Chuanqi Tan, Fuchun Sun, Tao Kong, Bin Fang, Wenchang Zhang

Different functional areas of the human brain play different roles in brain activity, which has not been paid sufficient research attention in the brain-computer interface (BCI) field.

Classification EEG +2

FoveaBox: Beyond Anchor-based Object Detector

6 code implementations8 Apr 2019 Tao Kong, Fuchun Sun, Huaping Liu, Yuning Jiang, Lei LI, Jianbo Shi

In FoveaBox, an instance is assigned to adjacent feature levels to make the model more accurate. We demonstrate its effectiveness on standard benchmarks and report extensive experimental analysis.

Ranked #86 on Object Detection on COCO test-dev (APM metric)

Object Detection

Deep Feature Pyramid Reconfiguration for Object Detection

no code implementations ECCV 2018 Tao Kong, Fuchun Sun, Wenbing Huang, Huaping Liu

In this paper, we begin by investigating current feature pyramids solutions, and then reformulate the feature pyramid construction as the feature reconfiguration process.

Object Detection

A Survey on Deep Transfer Learning

no code implementations6 Aug 2018 Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, Chunfang Liu

As a new classification platform, deep learning has recently received increasing attention from researchers and has been successfully applied to many domains.

General Classification Transfer Learning

HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection

no code implementations CVPR 2016 Tao Kong, Anbang Yao, Yurong Chen, Fuchun Sun

Almost all of the current top-performing object detection networks employ region proposals to guide the search for object instances.

Object Detection Region Proposal

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