17 citations · 35 across the 21 of their papers we have counts for
21 papers
MulVec: Fine-Grained Role-Aware Matching for Training-Free Zero-Shot Composed Image Retrieval
Zihao Zhang, Dayan Wu, Xinze Liu +6
Training-free zero-shot composed image retrieval finds a target image in a gallery from a reference image and a text edit without learning from task-specific image triplets. Existi…
AdaptiveEmbed: Sample-Adaptive Multi-Vector Representation for Multimodal Retrieval
Xinze Liu, Lei Yang, Dayan Wu +7
Multi-vector representations have emerged as an effective paradigm for multimodal retrieval, representing each sample with multiple complementary embeddings to capture fine-grained…
Absorbing Gradient Conflicts: Modeling Semantic Variance via Kent Distributions for Cross-Modal Hashing
Hengjie Zhu, Dayan Wu, Zihao Zhang +5
Supervised proxy-based deep cross-modal hashing has become the dominant paradigm for large-scale retrieval. However, prevalent methods model class proxies as deterministic points i…
FOVEA: Focused On-Demand Visual Evidence Adaptation for Cache-Friendly Multimodal Speculative Decoding
Hengjie Zhu, Dayan Wu, Zihao Zhang +6
Multimodal speculative decoding accelerates vision-language models by allowing a lightweight draft model to propose candidate tokens for parallel verification by a larger target mo…
Hallucinations Leave a Grounding Signature:Verifier-Guided Decoding for Selective Object Correction
Lei Yang, Xinze Liu, Dayan Wu +7
Large vision-language models (LVLMs) often hallucinate objects that are absent from an image. Despite recent progress, existing mitigation methods still lack reliable object-level…
Beyond Post-Quantization: Native Hash Learning with a Dedicated HASH Token
Xinze Liu, Ding Wang, Dayan Wu +4
Efficient large-scale image retrieval requires compact representations that preserve semantic similarity under fast Hamming-space search. Deep hashing is appealing, but most existi…
Online Self-Calibration Against Hallucination in Vision-Language Models
Minghui Chen, Chenxu Yang, Hengjie Zhu +3
Large Vision-Language Models (LVLMs) often suffer from hallucinations, generating descriptions that include visual details absent from the input image. Recent preference alignment…
EagleNet: Energy-Aware Fine-Grained Relationship Learning Network for Text-Video Retrieval
Yuhan Chen, Pengwen Dai, Chuan Wang +2
Text-video retrieval tasks have seen significant improvements due to the recent development of large-scale vision-language pre-trained models. Traditional methods primarily focus o…
Critique Before Thinking: Mitigating Hallucination through Rationale-Augmented Instruction Tuning
Zexian Yang, Dian Li, Dayan Wu +2
Despite significant advancements in multimodal reasoning tasks, existing Large Vision-Language Models (LVLMs) are prone to producing visually ungrounded responses when interpreting…
RealEra: Semantic-level Concept Erasure via Neighbor-Concept Mining
Yufan Liu, Jinyang An, Wanqian Zhang +5
The remarkable development of text-to-image generation models has raised notable security concerns, such as the infringement of portrait rights and the generation of inappropriate…
Prediction Exposes Your Face: Black-box Model Inversion via Prediction Alignment
Yufan Liu, Wanqian Zhang, Dayan Wu +3
Model inversion (MI) attack reconstructs the private training data of a target model given its output, posing a significant threat to deep learning models and data privacy. On one…
Disrupting Diffusion: Token-Level Attention Erasure Attack against Diffusion-based Customization
Yisu Liu, Jinyang An, Wanqian Zhang +4
With the development of diffusion-based customization methods like DreamBooth, individuals now have access to train the models that can generate their personalized images. Despite…
Digging into Depth Priors for Outdoor Neural Radiance Fields
Chen Wang, Jiadai Sun, Lina Liu +5
Neural Radiance Fields (NeRF) have demonstrated impressive performance in vision and graphics tasks, such as novel view synthesis and immersive reality. However, the shape-radiance…
Handling Label Uncertainty for Camera Incremental Person Re-Identification
Zexian Yang, Dayan Wu, Wanqian Zhang +2
Incremental learning for person re-identification (ReID) aims to develop models that can be trained with a continuous data stream, which is a more practical setting for real-world…
Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification
Xiaohua Chen, Yucan Zhou, Dayan Wu +4
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 cat…
TPSNet: Reverse Thinking of Thin Plate Splines for Arbitrary Shape Scene Text Representation
Wei Wang, Yu Zhou, Jiahao Lv +4
The research focus of scene text detection and recognition has shifted to arbitrary shape text in recent years, where the text shape representation is a fundamental problem. An ide…
Mask is All You Need: Rethinking Mask R-CNN for Dense and Arbitrary-Shaped Scene Text Detection
Xugong Qin, Yu Zhou, Youhui Guo +5
Due to the large success in object detection and instance segmentation, Mask R-CNN attracts great attention and is widely adopted as a strong baseline for arbitrary-shaped scene te…
Rescuing Deep Hashing from Dead Bits Problem
Shu Zhao, Dayan Wu, Yucan Zhou +2
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 metho…
Exploring Relations in Untrimmed Videos for Self-Supervised Learning
Dezhao Luo, Bo Fang, Yu Zhou +3
Existing video self-supervised learning methods mainly rely on trimmed videos for model training. However, trimmed datasets are manually annotated from untrimmed videos. In this se…
Two-Level Residual Distillation based Triple Network for Incremental Object Detection
Dongbao Yang, Yu Zhou, Dayan Wu +3
Modern object detection methods based on convolutional neural network suffer from severe catastrophic forgetting in learning new classes without original data. Due to time consumpt…
FC2RN: A Fully Convolutional Corner Refinement Network for Accurate Multi-Oriented Scene Text Detection
Xugong Qin, Yu Zhou, Dayan Wu +2
Recent scene text detection works mainly focus on curve text detection. However, in real applications, the curve texts are more scarce than the multi-oriented ones. Accurate detect…