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Weijie Kong

7 accepted papers

2025

AffPose: An Integrated RGB-Based Framework for Simultaneous Pose Estimation and Affordance Detection in Robotic Tool Manipulation

RA-L 2025

Enabling robots to perform tool manipulation like humans remains a great challenge. A semantic understanding of tool affordances and precise spatial localization is essential for this task. Conventional methods relying on RGB-D cameras for affordance detection and tool manipulation have been proven

Cited by 2SourceScholar
2023

Seeing What You Miss: Vision-Language Pre-Training With Semantic Completion Learning

CVPR 2023poster

Cross-modal alignment is essential for vision-language pre-training (VLP) models to learn the correct corresponding information across different modalities. For this purpose, inspired by the success of masked language modeling (MLM) tasks in the NLP pre-training area, numerous masked modeling tasks…

2022

Egocentric Video-Language Pretraining

NeurIPS 2022accept

Video-Language Pretraining (VLP), which aims to learn transferable representation to advance a wide range of video-text downstream tasks, has recently received increasing attention. Best performing works rely on large-scale, 3rd-person video-text datasets, such as HowTo100M. In this work, we exploit…

2020

Regression Before Classification for Temporal Action Detection

ICASSP 2020accepted

Action classification combined with location regression is a widely-utilized mechanism in existing temporal action detection methods. However, there exists an inconsistency problem between locations and categories of action instances in this mechanism. More specifically, while the location of the pr…

Cited by 0SourceScholar
2019

BLP - Boundary Likelihood Pinpointing Networks for Accurate Temporal Action Localization

ICASSP 2019accepted

Despite tremendous progress achieved in temporal action detection, state-of-the-art methods still suffer from the sharp performance deterioration when localizing the starting and ending temporal action boundaries. Although most methods apply boundary regression paradigm to tackle this problem, we ar…

Cited by 0SourceScholar
2019

Graph Convolutional Label Noise Cleaner: Train a Plug-And-Play Action Classifier for Anomaly Detection

CVPR 2019poster

Video anomaly detection under weak labels is formulated as a typical multiple-instance learning problem in previous works. In this paper, we provide a new perspective, i.e., a supervised learning task under noisy labels. In such a viewpoint, as long as cleaning away label noise, we can directly appl…

Cited by 590PDFcodeScholar