Search Results for author:
Found 8 papers, 3 papers with code
Segment Any 3D Object with Language
In addition, to align the 3D segmentation model with various language instructions and enhance the mask quality, we introduce three types of multimodal associations as supervision.
Universal Noise Annotation: Unveiling the Impact of Noisy annotation on Object Detection
For object detection task with noisy labels, it is important to consider not only categorization noise, as in image classification, but also localization noise, missing annotations, and bogus bounding boxes.
Inducing Point Operator Transformer: A Flexible and Scalable Architecture for Solving PDEs
Solving partial differential equations (PDEs) by learning the solution operators has emerged as an attractive alternative to traditional numerical methods.
Consistency Learning via Decoding Path Augmentation for Transformers in Human Object Interaction Detection
Motivated by various inference paths for HOI detection, we propose cross-path consistency learning (CPC), which is a novel end-to-end learning strategy to improve HOI detection for transformers by leveraging augmented decoding paths.
Ranked #1 on
Human-Object Interaction Detection
on V-COCO
(MAP metric)
A Self-Supervised Automatic Post-Editing Data Generation Tool
Data building for automatic post-editing (APE) requires extensive and expert-level human effort, as it contains an elaborate process that involves identifying errors in sentences and providing suitable revisions.
Identifying Physical Law of Hamiltonian Systems via Meta-Learning
We propose that meta-learning algorithms can be potentially powerful data-driven tools for identifying the physical law governing Hamiltonian systems without any mathematical assumptions on the representation, but with observations from a set of systems governed by the same physical law.
Learning to Identify Physical Laws of Hamiltonian Systems via Meta-Learning
We propose that meta-learning algorithms can be potentially powerful data-driven tools for identifying the physical law governing Hamiltonian systems without any mathematical assumptions on the representation, but with observations from a set of systems governed by the same physical law.
Deep Learning on Radar Centric 3D Object Detection
Even though many existing 3D object detection algorithms rely mostly on camera and LiDAR, camera and LiDAR are prone to be affected by harsh weather and lighting conditions.
