Search Results for author:
Found 16 papers, 7 papers with code
Unveiling the Invisible: Reasoning Complex Occlusions Amodally with AURA
To address these limitations, we propose a novel task named amodal reasoning segmentation, aiming to predict the complete amodal shape of occluded objects while providing answers with elaborations based on user text input.
BEAT: Balanced Frequency Adaptive Tuning for Long-Term Time-Series Forecasting
Time-series forecasting is crucial for numerous real-world applications including weather prediction and financial market modeling.
Ranked #1 on
Time Series Forecasting
on ETTh1
TurboHopp: Accelerated Molecule Scaffold Hopping with Consistency Models
Scaffold hopping is an efficient strategy that facilitates the identification of similar active compounds by strategically modifying the core structure of molecules, effectively narrowing the wide chemical space and enhancing the discovery of drug-like products.
AVIN-Chat: An Audio-Visual Interactive Chatbot System with Emotional State Tuning
This work presents an audio-visual interactive chatbot (AVIN-Chat) system that allows users to have face-to-face conversations with 3D avatars in real-time.
Learning-enabled Flexible Job-shop Scheduling for Scalable Smart Manufacturing
In smart manufacturing systems (SMSs), flexible job-shop scheduling with transportation constraints (FJSPT) is essential to optimize solutions for maximizing productivity, considering production flexibility based on automated guided vehicles (AGVs).
MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker Joints
Molecular core structures and R-groups are essential concepts in drug development.
Camera-Driven Representation Learning for Unsupervised Domain Adaptive Person Re-identification
To address the camera bias problem, we also introduce a camera-diversity (CD) loss encouraging person images of the same pseudo label, but captured across various cameras, to involve more for discriminative feature learning, providing person representations robust to inter-camera variations.
Domain Adaptation
Domain Adaptive Person Re-Identification
+4
Extracting Structured Seed-Mediated Gold Nanorod Growth Procedures from Literature with GPT-3
To that end, we present an approach using the powerful GPT-3 language model to extract structured multi-step seed-mediated growth procedures and outcomes for gold nanorods from unstructured scientific text.
Structured information extraction from complex scientific text with fine-tuned large language models
Here, we present a simple sequence-to-sequence approach to joint named entity recognition and relation extraction for complex hierarchical information in scientific text.
Decomposed Knowledge Distillation for Class-Incremental Semantic Segmentation
We introduce a CISS framework that alleviates the forgetting problem and facilitates learning novel classes effectively.
Class-Incremental Semantic Segmentation
Knowledge Distillation
OIMNet++: Prototypical Normalization and Localization-aware Learning for Person Search
To this end, we introduce a novel normalization layer, dubbed ProtoNorm, that calibrates features from pedestrian proposals, while considering a long-tail distribution of person IDs, enabling L2 normalized person representations to be discriminative.
A Brand New Dance Partner: Music-Conditioned Pluralistic Dancing Controlled by Multiple Dance Genres
When coming up with phrases of movement, choreographers all have their habits as they are used to their skilled dance genres.
Ranked #6 on
Motion Synthesis
on FineDance
Learning by Aligning: Visible-Infrared Person Re-identification using Cross-Modal Correspondences
We address the problem of visible-infrared person re-identification (VI-reID), that is, retrieving a set of person images, captured by visible or infrared cameras, in a cross-modal setting.
HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detection
To this end, we propose a new convolutional neural network (CNN) architecture, dubbed HVPR, that integrates both features into a single 3D representation effectively and efficiently.
Unsupervised Learning of Deep-Learned Features from Breast Cancer Images
We demonstrate the effectiveness of the proposed approach for cancer detection in BRCA and show how the machine can choose the most appropriate clusters during the unsupervised learning procedure.
HistomicsML2.0: Fast interactive machine learning for whole slide imaging data
Extracting quantitative phenotypic information from whole-slide images presents significant challenges for investigators who are not experienced in developing image analysis algorithms.
