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Dayan Wu

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6 papers

1 author row

AAAI Conference 2026 Conference Paper

Discretization Is Not Always Better: Rethinking Deep Quantization for Asymmetric Image Retrieval

Xinze Liu
Dayan Wu
Hengjie Zhu
Chenming Wu
Pengwen Dai

Asymmetric image retrieval (AIR), which typically employs a compact model for the query side and a large model for the database server, has garnered significant attention in resource-constrained environments. While deep hashing methods have shown great potential in large-scale image retrieval, current attempts for the asymmetric image retrieval overlook the differences in quantization capabilities between query and gallery networks. In AIR, the conventional quantization scheme forces the outputs of small query models to approximate the discrete outputs of large models, imposing overly rigid and stringent constraints that severely limit the optimization of small query models. Furthermore, existing deep hashing methods for AIR necessitate labeled datasets from large models, which also limits their practical applicability. To this end, we reconsider the necessity of strict discretization in AIR and propose a novel asymmetric hashing method, named Deep Correlation Alignment Hashing (DCAH). Rather than explicitly quantizing continuous query features to match discrete gallery representations, we distill the correlation across both models and introduce a Correlation Alignment based Quantization (CAQ) scheme, thereby implicitly accomplishing quantization. To preserve the similarity consistency between the query and gallery models, we further employ a correlation alignment-based knowledge distillation strategy which is intrinsically compatible with the CAQ. Notably, the proposed quantization scheme can function as a plug-and-play module that seamlessly integrates with existing AIR methods. Comprehensive evaluations on three real-world benchmark datasets demonstrate the effectiveness of the proposed quantization scheme CAQ, and also show that DCAH achieves state-of-the-art performance in asymmetric image retrieval scenarios.

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IJCAI Conference 2025 Conference Paper

Categorical Attention: Fine-grained Language-guided Noise Filtering Network for Occluded Person Re-Identification

Minghui Chen
Dayan Wu
Chenxu Yang
Qinghang Su
Zheng Lin

Person Re-Identification (ReID) aims to match individuals across different camera views, but occlusions in real-world scenarios, such as vehicles or crowds, hinder feature extraction and matching. Current occluded ReID methodologies typically leverage visual augmentation techniques in an attempt to mitigate the disruptive effects of occlusion-induced noise. However, relying solely on visual data fail to effectively filter out occlusion noise. In this paper, we introduce the Fine-grained Language-guided Noise Filtering Network (FLaN-Net) for occluded ReID. FLaN-Net innovatively employs categorical attention mechanism to generate adaptive tokens that capture the following three distinct types of visual information: comprehensive descriptions of individuals, detailed visible attributes, and characteristics of occluding objects. Subsequently, a cross-attention mechanism aligns these prompts with the image, guiding the model to focus on relevant regions. To generate robust and discriminative features for occluded pedestrians, we further introduce a dynamic weighting fusion module that integrates visual, textual, and cross-attention features based on their reliability. Experimental results demonstrate that FLaN-Net outperforms existing methods on occluded ReID benchmarks, offering a robust solution for challenging real-world conditions.

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IJCAI Conference 2025 Conference Paper

Endogenous Recovery via Within-modality Prototypes for Incomplete Multimodal Hashing

Sa Zhu
Dayan Wu
Chenming Wu
Pengwen Dai
Bo Li

Multimodal hashing projects multimodal data into compact binary codes, enabling rapid and storage-efficient retrieval of large-scale multimedia content. In practical scenarios, the issue of missing modality frequently arises when dealing with multimodal data. Existing incomplete multimodal hashing techniques directly recover missing modalities by neural networks, resulting in a disjointed representation space between the recovered and true data. In this paper, we present a novel recovery paradigm, namely Prototype-based Modality Completion Hashing (PMCH). Instead of directly synthesizing it from available modalities, PMCH adaptively aggregates associated within-modality prototypes to recover missing modality data. Specifically, PMCH introduces an within-modality prototype learning module to optimize representative prototypes for each modality. These prototypes act as recovery anchors and reside within the same representation space as their corresponding modality data. Subsequently, PMCH adaptively aggregates the associated within-modality prototypes with coefficients derived from the modality-specific Weight-Net. By utilizing prototypes from the same modality, the semantic disparity between the reconstructed and authentic data can be substantially diminished. Extensive experiments on three widely used benchmark datasets demonstrate that PMCH can effectively recover the missing modality, and attain state-of-the-art performance in both complete and incomplete multimodal retrieval scenarios. Code is available at https: //github. com/Sasa77777779/PMCH. git.

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AAAI Conference 2024 Conference Paper

Pairwise-Label-Based Deep Incremental Hashing with Simultaneous Code Expansion

Dayan Wu
Qinghang Su
Bo Li
Weiping Wang

Deep incremental hashing has become a subject of considerable interest due to its capability to learn hash codes in an incremental manner, eliminating the need to generate codes for classes that have already been learned. However, accommodating more classes requires longer hash codes, and regenerating database codes becomes inevitable when code expansion is required. In this paper, we present a unified deep hash framework that can simultaneously learn new classes and increase hash code capacity. Specifically, we design a triple-channel asymmetric framework to optimize a new CNN model with a target code length and a code projection matrix. This enables us to directly generate hash codes for new images, and efficiently generate expanded hash codes for original database images from the old ones with the learned projection matrix. Meanwhile, we propose a pairwise-label-based incremental similarity-preserving loss to optimize the new CNN model, which can incrementally preserve new similarities while maintaining the old ones. Additionally, we design a double-end quantization loss to reduce the quantization error from new and original query images. As a result, our method efficiently embeds both new and original similarities into the expanded hash codes, while keeping the original database codes unchanged. We conduct extensive experiments on three widely-used image retrieval benchmarks, demonstrating that our method can significantly reduce the time required to expand existing database codes, while maintaining state-of-the-art retrieval performance.

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AAAI Conference 2022 Conference Paper

Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification

Xiaohua Chen
Yucan Zhou
Dayan Wu
Wanqian Zhang
Yu Zhou
Bo Li
Weiping Wang

Real-world data often follows a long-tailed distribution, which makes the performance of existing classification algorithms degrade heavily. A key issue is that samples in tail categories fail to depict their intra-class diversity. Humans can imagine a sample in new poses, scenes, and view angles with their prior knowledge even if it is the first time to see this category. Inspired by this, we propose a novel reasoning-based implicit semantic data augmentation method to borrow transformation directions from other classes. Since the covariance matrix of each category represents the feature transformation directions, we can sample new directions from similar categories to generate definitely different instances. Specifically, the long-tailed distributed data is first adopted to train a backbone and a classifier. Then, a covariance matrix for each category is estimated, and a knowledge graph is constructed to store the relations of any two categories. Finally, tail samples are adaptively enhanced via propagating information from all the similar categories in the knowledge graph. Experimental results on CIFAR-100-LT, ImageNet-LT, and iNaturalist 2018 have demonstrated the effectiveness of our proposed method compared with the state-of-the-art methods.

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IJCAI Conference 2021 Conference Paper

Rescuing Deep Hashing from Dead Bits Problem

Shu Zhao
Dayan Wu
Yucan Zhou
Bo Li
Weiping Wang

Deep hashing methods have shown great retrieval accuracy and efficiency in large-scale image retrieval. How to optimize discrete hash bits is always the focus in deep hashing methods. A common strategy in these methods is to adopt an activation function, e. g. sigmoid() or tanh(), and minimize a quantization loss to approximate discrete values. However, this paradigm may make more and more hash bits stuck into the wrong saturated area of the activation functions and never escaped. We call this problem "Dead Bits Problem (DBP)". Besides, the existing quantization loss will aggravate DBP as well. In this paper, we propose a simple but effective gradient amplifier which acts before activation functions to alleviate DBP. Moreover, we devise an error-aware quantization loss to further alleviate DBP. It avoids the negative effect of quantization loss based on the similarity between two images. The proposed gradient amplifier and error-aware quantization loss are compatible with a variety of deep hashing methods. Experimental results on three datasets demonstrate the efficiency of the proposed gradient amplifier and the error-aware quantization loss.

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