Author name cluster

Gang Hua 0001

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6 papers

1 author row

ICML Conference 2025 Conference Paper

Componential Prompt-Knowledge Alignment for Domain Incremental Learning

Kunlun Xu
Xu Zou 0002
Gang Hua 0001
Jiahuan Zhou

Domain Incremental Learning (DIL) aims to learn from non-stationary data streams across domains while retaining and utilizing past knowledge. Although prompt-based methods effectively store multi-domain knowledge in prompt parameters and obtain advanced performance through cross-domain prompt fusion, we reveal an intrinsic limitation: component-wise misalignment between domain-specific prompts leads to conflicting knowledge integration and degraded predictions. This arises from the random positioning of knowledge components within prompts, where irrelevant component fusion introduces interference. To address this, we propose Componential Prompt-Knowledge Alignment (KA-Prompt), a novel prompt-based DIL method that introduces component-aware prompt-knowledge alignment during training, significantly improving both the learning and inference capacity of the model. KA-Prompt operates in two phases: (1) Initial Componential Structure Configuring, where a set of old prompts containing knowledge relevant to the new domain are mined via greedy search, which is then exploited to initialize new prompts to achieve reusable knowledge transfer and establish intrinsic alignment between new and old prompts. (2) Online Alignment Preservation, which dynamically identifies the target old prompts and applies adaptive componential consistency constraints as new prompts evolve. Extensive experiments on DIL benchmarks demonstrate the effectiveness of our KA-Prompt. Our source code is available at https: //github. com/zhoujiahuan1991/ICML2025-KA-Prompt.

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ICML Conference 2025 Conference Paper

Token Coordinated Prompt Attention is Needed for Visual Prompting

Zichen Liu
Xu Zou 0002
Gang Hua 0001
Jiahuan Zhou

Visual prompting techniques are widely used to efficiently fine-tune pretrained Vision Transformers (ViT) by learning a small set of shared prompts for all tokens. However, existing methods overlook the unique roles of different tokens in conveying discriminative information and interact with all tokens using the same prompts, thereby limiting the representational capacity of ViT. This often leads to indistinguishable and biased prompt-extracted features, hindering performance. To address this issue, we propose a plug-and-play Token Coordinated Prompt Attention (TCPA) module, which assigns specific coordinated prompts to different tokens for attention-based interactions. Firstly, recognizing the distinct functions of CLS and image tokens-global information aggregation and local feature extraction, we disentangle the prompts into CLS Prompts and Image Prompts, which interact exclusively with CLS tokens and image tokens through attention mechanisms. This enhances their respective discriminative abilities. Furthermore, as different image tokens correspond to distinct image patches and contain diverse information, we employ a matching function to automatically assign coordinated prompts to individual tokens. This enables more precise attention interactions, improving the diversity and representational capacity of the extracted features. Extensive experiments across various benchmarks demonstrate that TCPA significantly enhances the diversity and discriminative power of the extracted features.

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ICML Conference 2024 Conference Paper

Enhancing Implicit Shape Generators Using Topological Regularizations

Liyan Chen
Yan Zheng
Yang Li 0104
Lohit Anirudh Jagarapu
Haoxiang Li
Hao Kang
Gang Hua 0001
Qi-Xing Huang

A fundamental problem in learning 3D shapes generative models is that when the generative model is simply fitted to the training data, the resulting synthetic 3D models can present various artifacts. Many of these artifacts are topological in nature, e. g. , broken legs, unrealistic thin structures, and small holes. In this paper, we introduce a principled approach that utilizes topological regularization losses on an implicit shape generator to rectify topological artifacts. The objectives are two-fold. The first is to align the persistent diagram (PD) distribution of the training shapes with that of synthetic shapes. The second ensures that the PDs are smooth among adjacent synthetic shapes. We show how to achieve these two objectives using two simple but effective formulations. Specifically, distribution alignment is achieved to learn a generative model of PDs and align this generator with PDs of synthetic shapes. We show how to handle discrete and continuous variabilities of PDs by using a shape-regularization term when performing PD alignment. Moreover, we enforce the smoothness of the PDs using a smoothness loss on the PD generator, which further improves the behavior of PD distribution alignment. Experimental results on ShapeNet show that our approach leads to much better generalization behavior than state-of-the-art implicit shape generators.

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ICRA Conference 2019 Conference Paper

Priming Deep Pedestrian Detection with Geometric Context

Ishani Chakraborty
Gang Hua 0001

We investigate the role of geometric context in deep neural networks to establish better pedestrian detectors that are more robust to occlusions. Notwithstanding their demonstrated successes, deep object detectors under-perform in crowded scenes with high intra-category occlusions. One brute-force solution is to collect a large number of labeled training samples under occlusion, but the combinatorial increase in the labeling effort makes it an unaffordable solution. We argue that a promising and complementary direction to solve this problem is to bring geometric context to modulate feature learning in a DNN. We identify that an effective way to leverage geometric context is to induce it in two steps - through early fusion, by guiding region proposal generation to focus on occluded regions, and through late fusion, by penalizing misalignments of bounding boxes in both 2D and 3D. Our experiments on multiple state-of-the-art DNN detectors and detection benchmarks clearly demonstrates that our proposed method outperforms strong baselines by an average of 5%.

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UAI Conference 2017 Conference Paper

Green Generative Modeling: Recycling Dirty Data using Recurrent Variational Autoencoders

Yu Wang 0060
Bin Dai 0008
Gang Hua 0001
John A. D. Aston
David P. Wipf

This paper explores two useful modifications of the recent variational autoencoder (VAE), a popular deep generative modeling framework that dresses traditional autoencoders with probabilistic attire. The first involves a specially-tailored form of conditioning that allows us to simplify the VAE decoder structure while simultaneously introducing robustness to outliers. In a related vein, a second, complementary alteration is proposed to further build invariance to contaminated or dirty samples via a data augmentation process that amounts to recycling. In brief, to the extent that the VAE is legitimately a representative generative model, then each output from the decoder should closely resemble an authentic sample, which can then be resubmitted as a novel input ad infinitum. Moreover, this can be accomplished via special recurrent connections without the need for additional parameters to be trained. We evaluate these proposals on multiple practical outlier-removal and generative modeling tasks, demonstrating considerable improvements over existing algorithms.

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IROS Conference 2016 Conference Paper

An egocentric computer vision based co-robot wheelchair

Haoxiang Li
Mohammed Kutbi
Xin Li
Changjiang Cai
Philippos Mordohai
Gang Hua 0001

Motivated by the emerging needs to improve the quality of life for the elderly and disabled individuals who rely on wheelchairs for mobility, and who might have limited or no hand functionality at all, we propose an egocentric computer vision based co-robot wheelchair to enhance their mobility without hand usage. The co-robot wheelchair is built upon a typical commercial power wheelchair. The user can access 360 degrees of motion direction as well as a continuous range of speed without the use of hands via the egocentric computer vision based control we developed. The user wears an egocentric camera and collaborates with the robotic wheelchair by conveying the motion commands with head motions. Compared with previous sip-n-puff, chin-control and tongue-operated solutions to hands-free mobility, this egocentric computer vision based control system provides a more natural human robot interface. Our experiments show that this design is of higher usability and users can quickly learn to control and operate the wheelchair. Besides its convenience in manual navigation, the egocentric camera also supports novel user-robot interaction modes by enabling autonomous navigation towards a detected person or object of interest. User studies demonstrate the usability and efficiency of the proposed egocentric computer vision co-robot wheelchair.

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