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- Qi TianSuggested from co-authorship
- Qi Chuan TianSuggested from co-authorship
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Works54 from public data
- Person Transfer GAN to Bridge Domain Gap for Person Re-identification2,033
A Person Transfer Generative Adversarial Network (PTGAN) is proposed to relieve the expensive costs of annotating new training samples and comprehensive experiments show that the domain gap could be substantially narrowed-down by the PTGAN.
- GLAD410
A Global-Local-Alignment Descriptor (GLAD) and an efficient indexing and retrieval framework that leverages the local and global cues in human body to generate a discriminative and robust representation are proposed.
- Visformer: The Vision-friendly Transformer328
A new architecture named Visformer is proposed, which is abbreviated from the ‘Vision-friendly Transformer’, which outperforms both the Transformer-based and convolution-based models in terms of ImageNet classification accuracy, and the advantage becomes more significant when the model complexity is lower or the training set is smaller.
- Rethinking the Distribution Gap of Person Re-identification with Camera-Based Batch Normalization191
This paper rethinks the working mechanism of conventional ReID approaches and puts forward a new solution, with an effective operator named Camera-based Batch Normalization (CBN), that forces the image data of all cameras to fall onto the same subspace, so that the distribution gap between any camera pair is largely shrunk.
- HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data167
This work presents a novel hallucination detection and elimination framework, HalluciDoctor, based on the cross-checking paradigm, and uses this framework to identify and eliminate hallucinations in the training data automatically, and indicates that spurious correlations arising from long-tail object cooccurrences contribute to hallucinations.
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- Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap132
A literature review on the application of NAS to computer vision problems is provided and existing approaches are summarized into several categories according to their efforts in bridging the gap.
- UnrealPerson: An Adaptive Pipeline towards Costless Person Re-identification84
UnrealPerson is presented, a novel pipeline that makes full use of unreal image data to decrease the costs in both the training and deployment stages and is a system that can generate synthesized images of high-quality and from controllable distributions.
- Circumventing Outliers of AutoAugment with Knowledge Distillation68
It is revealed that AutoAugment may remove part of discriminative information from the training image and so insisting on the ground-truth label is no longer the best option, and knowledge distillation is made use that refers to the output of a teacher model to guide network training.
- Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model60
The empirical results demonstrate that Diff-Mix achieves a better balance between faith-fulness and diversity, leading to a marked improvement in performance across diverse image classification scenarios, including few-shot, conventional, and long-tail classifications for domain-specific datasets.
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- Continual Vision-Language Representation Learning with Off-Diagonal Information50
A new continual vision-language representation learning framework Mod-X is proposed: Maintain off-diagonal information-matriX, which improves the capability of the multi-modal model by maintaining the multi -modal representation space alignment on the old data domain during continuously fitting the new training data domain.
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- Gradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Models41
A novel Gradient-RegulAted Meta-prompt learning (GRAM) framework that jointly meta-learns an efficient soft prompt initialization for better adaptation and a lightweight gradient regulating function for strong cross-domain generalizability in a meta-learning paradigm using only the unlabeled image-text pre-training data.
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- Degeneration-Tuning: Using Scrambled Grid shield Unwanted Concepts from Stable Diffusion34
A novel strategy named Degeneration-Tuning (DT) is proposed to shield contents of unwanted concepts from SD weights by utilizing Scrambled Grid to reconstruct the correlation between undesired concepts and their corresponding image domain, and guide SD to generate meaningless content when such textual concepts are provided as input.
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- VP-ReID30
A robust and efficient person and video ReID system named as VP-ReID is developed, build based on recent works including Deep Convolutional Neural Network design for discriminative feature extraction, efficient off-line indexing, as well as distance metric optimization for deep feature learning.
- Can Semantic Labels Assist Self-Supervised Visual Representation Learning?29
A new algorithm named Supervised Contrastive Adjustment in Neighborhood (SCAN) is presented that maximally prevents the semantic guidance from damaging the appearance feature embedding and reveals that semantic labels are useful in assisting self-supervised methods, opening a new direction for the community.
- Single Camera Training for Person Re-Identification29
This paper investigates ReID in an unexplored single-camera-training (SCT) setting, where each person in the training set appears in only one camera, and proposes a novel loss function named multi- camera negative loss (MCNL), a metric learning loss motivated by probability suggesting that in a multi-camera system, one image is more likely to be closer to the most similar negative sample in other cameras than to themost similarnegative sample in the same camera.
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- SDDM: Score-Decomposed Diffusion Models on Manifolds for Unpaired Image-to-Image Translation26
This work presents a new score-decomposed diffusion model (SDDM) on manifolds to explicitly optimize the tangled distributions during image generation to outperforms existing SBDM-based methods with much fewer diffusion steps on several I2I benchmarks.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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