345 citations · 719 across the 23 of their papers we have counts for
23 papers
Mind the Discriminability Trap in Source-Free Cross-domain Few-shot Learning
Zhenyu Zhang, Yixiong Zou, Yuhua Li +2
Source-Free Cross-Domain Few-Shot Learning (SF-CDFSL) focuses on fine-tuning with limited training data from target domains (e.g., medical or satellite images), where Vision-Langua…
Reclaiming Lost Text Layers for Source-Free Cross-Domain Few-Shot Learning
Zhenyu Zhang, Guangyao Chen, Yixiong Zou +2
Source-Free Cross-Domain Few-Shot Learning (SF-CDFSL) focuses on fine-tuning with limited training data from target domains (e.g., medical or satellite images), where CLIP has rece…
KTV: Keyframes and Key Tokens Selection for Efficient Training-Free Video LLMs
Baiyang Song, Jun Peng, Yuxin Zhang +3
Training-free video understanding leverages the strong image comprehension capabilities of pre-trained vision language models (VLMs) by treating a video as a sequence of static fra…
Decoupling Template Bias in CLIP: Harnessing Empty Prompts for Enhanced Few-Shot Learning
Zhenyu Zhang, Guangyao Chen, Yixiong Zou +2
The Contrastive Language-Image Pre-Training (CLIP) model excels in few-shot learning by aligning visual and textual representations. Our study shows that template-sample similarity…
Start Small, Think Big: Curriculum-based Relative Policy Optimization for Visual Grounding
Qingyang Yan, Guangyao Chen, Yixiong Zou
Chain-of-Thought (CoT) prompting has recently shown significant promise across various NLP and computer vision tasks by explicitly generating intermediate reasoning steps. However,…
When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network
Dong Xiao, Guangyao Chen, Peixi Peng +4
Anomaly detection is essential for the safety and reliability of autonomous driving systems. Current methods often focus on detection accuracy but neglect response time, which is c…
Adapter Naturally Serves as Decoupler for Cross-Domain Few-Shot Semantic Segmentation
Jintao Tong, Ran Ma, Yixiong Zou +3
Cross-domain few-shot segmentation (CD-FSS) is proposed to pre-train the model on a source-domain dataset with sufficient samples, and then transfer the model to target-domain data…
Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation
Jintao Tong, Yixiong Zou, Guangyao Chen +2
Cross-Domain Few-Shot Segmentation (CD-FSS) aims to transfer knowledge from a source-domain dataset to unseen target-domain datasets with limited annotations. Current methods typic…
From Objects to Events: Unlocking Complex Visual Understanding in Object Detectors via LLM-guided Symbolic Reasoning
Yuhui Zeng, Haoxiang Wu, Wenjie Nie +6
Current object detectors excel at entity localization and classification, yet exhibit inherent limitations in event recognition capabilities. This deficiency arises from their arch…
MICM: Rethinking Unsupervised Pretraining for Enhanced Few-shot Learning
Zhenyu Zhang, Guangyao Chen, Yixiong Zou +3
Humans exhibit a remarkable ability to learn quickly from a limited number of labeled samples, a capability that starkly contrasts with that of current machine learning systems. Un…
Learning Unknowns from Unknowns: Diversified Negative Prototypes Generator for Few-Shot Open-Set Recognition
Zhenyu Zhang, Guangyao Chen, Yixiong Zou +2
Few-shot open-set recognition (FSOR) is a challenging task that requires a model to recognize known classes and identify unknown classes with limited labeled data. Existing approac…
Adaptive Discovering and Merging for Incremental Novel Class Discovery
Guangyao Chen, Peixi Peng, Yangru Huang +2
One important desideratum of lifelong learning aims to discover novel classes from unlabelled data in a continuous manner. The central challenge is twofold: discovering and learnin…
LLaSM: Large Language and Speech Model
Yu Shu, Siwei Dong, Guangyao Chen +5
Multi-modal large language models have garnered significant interest recently. Though, most of the works focus on vision-language multi-modal models providing strong capabilities i…
AutoAgents: A Framework for Automatic Agent Generation
Guangyao Chen, Siwei Dong, Yu Shu +5
Large language models (LLMs) have enabled remarkable advances in automated task-solving with multi-agent systems. However, most existing LLM-based multi-agent approaches rely on pr…
Learning Sparse Neural Networks with Identity Layers
Mingjian Ni, Guangyao Chen, Xiawu Zheng +3
The sparsity of Deep Neural Networks is well investigated to maximize the performance and reduce the size of overparameterized networks as possible. Existing methods focus on pruni…
Picking Up Quantization Steps for Compressed Image Classification
Li Ma, Peixi Peng, Guangyao Chen +3
The sensitivity of deep neural networks to compressed images hinders their usage in many real applications, which means classification networks may fail just after taking a screens…
Large-scale Multi-Modal Pre-trained Models: A Comprehensive Survey
Xiao Wang, Guangyao Chen, Guangwu Qian +5
With the urgent demand for generalized deep models, many pre-trained big models are proposed, such as BERT, ViT, GPT, etc. Inspired by the success of these models in single domains…
Training Full Spike Neural Networks via Auxiliary Accumulation Pathway
Guangyao Chen, Peixi Peng, Guoqi Li +1
Due to the binary spike signals making converting the traditional high-power multiply-accumulation (MAC) into a low-power accumulation (AC) available, the brain-inspired Spiking Ne…
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
Jingkang Yang, Pengyun Wang, Dejian Zou +13
Out-of-distribution (OOD) detection is vital to safety-critical machine learning applications and has thus been extensively studied, with a plethora of methods developed in the lit…
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain
Guangyao Chen, Peixi Peng, Li Ma +3
Recently, the generalization behavior of Convolutional Neural Networks (CNN) is gradually transparent through explanation techniques with the frequency components decomposition. Ho…
Adversarial Reciprocal Points Learning for Open Set Recognition
Guangyao Chen, Peixi Peng, Xiangqian Wang +1
Open set recognition (OSR), aiming to simultaneously classify the seen classes and identify the unseen classes as 'unknown', is essential for reliable machine learning.The key chal…
Annotation-Efficient Untrimmed Video Action Recognition
Yixiong Zou, Shanghang Zhang, Guangyao Chen +3
Deep learning has achieved great success in recognizing video actions, but the collection and annotation of training data are still quite laborious, which mainly lies in two aspect…
Learning Open Set Network with Discriminative Reciprocal Points
Guangyao Chen, Limeng Qiao, Yemin Shi +5
Open set recognition is an emerging research area that aims to simultaneously classify samples from predefined classes and identify the rest as 'unknown'. In this process, one of t…