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Haojun Fei

7 accepted papers

2026

TS-PEFT: Unveiling Token-Level Redundancy in Parameter-Efficient Fine-Tuning

IJCAI 2026

Current Parameter-Efficient Fine-Tuning (PEFT) methods typically operate under an implicit assumption: once a target module is selected, every token passing through it contributes equally to the downstream task and requires a parameter update. In this paper, we challenge this convention by revealing

Cited by 0Scholar
2025

Leveraging MLLM Embeddings and Attribute Smoothing for Compositional Zero-Shot Learning

IJCAI 2025

Compositional zero-shot learning (CZSL) aims to recognize novel compositions of attributes and objects learned from seen compositions. Previous works disentangle attributes and objects by extracting shared and exclusive parts between the image pair sharing the same attribute (object), as well as ali

2025

SFE-Net: Harnessing Biological Principles of Differential Gene Expression for Improved Feature Selection in Deep Learning Networks

ICASSP 2025accepted

In the realm of DeepFake detection, the challenge of adapting to various synthesis methodologies such as Faceswap, Deepfakes, Face2Face, and NeuralTextures significantly impacts the performance of traditional machine learning models. These models often suffer from static feature representation, whic…

Cited by 0SourceScholar
2025

SpecWav-Attack: Leveraging Spectrogram Resizing and Wav2Vec 2.0 for Attacking Anonymized Speech

ICASSP 2025accepted

This paper presents SpecWav-Attack, an adversarial model for detecting speakers in anonymized speech. It leverages Wav2Vec2 for feature extraction [1] and incorporates spectrogram resizing and incremental training for improved performance. Evaluated on librispeech-dev and librispeech-test, SpecWav-A…

Cited by 0SourceScholar
2024

MS-SENet: Enhancing Speech Emotion Recognition Through Multi-Scale Feature Fusion with Squeeze-and-Excitation Blocks

ICASSP 2024accepted

Speech Emotion Recognition (SER) has become a growing focus of research in human-computer interaction. Spatiotemporal features play a crucial role in SER, yet current research lacks comprehensive spatiotemporal feature learning. This paper focuses on addressing this gap by proposing a novel approach…

Cited by 0SourceScholar
2023

MetaZSCIL: A Meta-Learning Approach for Generalized Zero-Shot Class Incremental Learning

AAAI 2023technical

Generalized zero-shot learning (GZSL) aims to recognize samples whose categories may not have been seen at training. Standard GZSL cannot handle dynamic addition of new seen and unseen classes. In order to address this limitation, some recent attempts have been made to develop continual GZSL methods…

Cited by 14SourcePDFScholar