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Haiyan Jiang

7 accepted papers

2026

HOG-Layout: Hierarchical 3D Scene Generation, Optimization and Editing via Vision-Language Models

CVPR 2026

3D layout generation and editing play a crucial role in Embodied AI and immersive VR interaction. However, manual creation requires tedious labor, while data-driven generation often lacks diversity. The emergence of large models introduces new possibilities for 3D scene synthesis. We present HOG-Lay

Cited by 0SourceScholar
2025

Infant Cry Detection Using Causal Temporal Representation

ICASSP 2025accepted

This paper addresses a major challenge in acoustic event detection, in particular infant cry detection in the presence of other sounds and background noises: the lack of precise annotated data. We present two contributions for supervised and unsupervised infant cry detection. The first is an annotat…

Cited by 0SourceScholar
2024

G–LIME: Statistical Learning for Local Interpretations of Deep Neural Networks Using Global Priors (Abstract Reprint)

AAAI 2024technical

To explain the prediction result of a Deep Neural Network (DNN) model based on a given sample, LIME [1] and its derivatives have been proposed to approximate the local behavior of the DNN model around the data point via linear surrogates. Though these algorithms interpret the DNN by finding the key…

Cited by 1SourcePDFScholar
2024

NDOT: Neuronal Dynamics-based Online Training for Spiking Neural Networks

ICML 2024poster

Spiking Neural Networks (SNNs) are attracting great attention for their energy-efficient and fast-inference properties in neuromorphic computing. However, the efficient training of deep SNNs poses challenges in gradient calculation due to the non-differentiability of their binary spike-generating ac…

2024

TAB: Temporal Accumulated Batch Normalization in Spiking Neural Networks

ICLR 2024poster

Spiking Neural Networks (SNNs) are attracting growing interest for their energy-efficient computing when implemented on neuromorphic hardware. However, directly training SNNs, even adopting batch normalization (BN), is highly challenging due to their non-differentiable activation function and the te…

Cited by 14SourcePDFScholar
2023

A Unified Optimization Framework of ANN-SNN Conversion: Towards Optimal Mapping from Activation Values to Firing Rates

ICML 2023poster

Spiking Neural Networks (SNNs) have gained significant attention for their energy-efficient and fast-inference capabilities, but training SNNs from scratch can be challenging due to the discrete nature of spikes. One alternative method is to convert an Artificial Neural Network (ANN) into an SNN, kn…

2022

AutoMS: Automatic Model Selection for Novelty Detection with Error Rate Control

NeurIPS 2022accept

Given an unsupervised novelty detection task on a new dataset, how can we automatically select a ''best'' detection model while simultaneously controlling the error rate of the best model? For novelty detection analysis, numerous detectors have been proposed to detect outliers on a new unseen datase…