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- 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.
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- Dilated Context Integrated Network with Cross-Modal Consensus for Temporal Emotion Localization in Videos17
A cross-modal consensus learning paradigm is introduced, which leverages the inherent semantic consensus between the aligned video and subtitle to achieve weakly-supervised learning on temporal emotion localization.
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- Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks16
This paper trains a graph convolutional network to fit the performance of sampled sub-networks so that the impact of random errors becomes minimal and achieves a higher rank correlation coefficient in the selected set of candidates, which consequently leads to better performance of the final architecture.
- LLaVA-c: Continual Improved Visual Instruction Tuning15
This work proposes a simple but effective method with two modifications on LLaVA-1.5: spectral-aware consolidation for improved task balance and unsupervised inquiry regularization to prevent base model degradation.
- Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio15
A new framework named network adjustment is presented, which considers network accuracy as a function ofFLOPs, so that under each network configuration, one can estimate the FLOPs utilization ratio (FUR) for each layer and use it to determine whether to increase or decrease the number of channels on the layer.
- Heterogeneous Contrastive Learning: Encoding Spatial Information for Compact Visual Representations14
This paper presents heterogeneous contrastive learning (HCL), an effective approach that adds spatial information to the encoding stage to alleviate the learning inconsistency between the contrastive objective and strong data augmentation operations.
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- OVMR: Open-Vocabulary Recognition with Multi-Modal References11
This method, named OVMR, adopts two innovative components to pursue a more robust category cues embedding, and is a plug-and-play module that works well with exemplar images randomly crawled from the Internet.
- Efficient Multi-modal Long Context Learning for Training-free Adaptation10
This approach is the first to seamlessly integrate compression and pruning techniques for multi-modal long-context learning, offering a scalable and efficient solution for real-world applications.
- Diverse part attentive network for video-based person re-identification10
A Diverse Part Attentive Network (DPAN) to exploit discriminative and diverse body cues and achieves competitive performance on these datasets compared with state-of-the-art methods.
- Towards AGI in Computer Vision: Lessons Learned from GPT and Large Language Models9
It is pointed out that the essential weakness of CV lies in lacking a paradigm to learn from environments, yet NLP has accomplished the task in the text world and is still far from a system like GPT that naturally integrates all tasks.
- PSR: Scaling Multi-Subject Personalized Image Generation with Pairwise Subject-Consistency Rewards8
A scalable multi-subject data generation pipeline that leverages powerful single-subject generation models to construct diverse and high-quality multi-subject training data and design a set of Pairwise Subject-Consistency Rewards and general-purpose rewards, which are incorporated into a refined reinforcement learning stage.
- VoxSeP: semi-positive voxels assist self-supervised 3D medical segmentation8
This paper investigates self-supervised pre-training, making use of unlabeled medical data to initialize 3D segmentation networks, and presents a novel proxy task that takes advantage of the property of human body similarity in medical scans, and defines the sub-volumes from the same position of different cases as Semi-Positive samples.
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- Learning Transferable Pedestrian Representation from Multimodal Information Supervision6
VAL-PAT is proposed, a novel framework that learns transferable representations to enhance various pedestrian analysis tasks with multimodal information and facilitates the learning of general pedestrian representations and thus leads to promising results on various pedestrianAnalysis tasks.
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- Boosting Segment Anything Model Towards Open-Vocabulary Learning5
This paper introduces a novel SideFormer module designed to acquire SAM features adept at perceiving objects and inject comprehensive semantic information for recognition, and presents Sambor to seamlessly integrate SAM with the open-vocabulary object detector in an end-to-end framework.
- Lformer: Text-to-Image Generation with L-shape Block Parallel Decoding5
Lformer, a semi-autoregressive text-to-image generation model that achieves faster speed than the existing transformer-based methods while keeping good generation quality and can edit images without the requirement for finetuning.
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- Exploring the diversity and invariance in yourself for visual pre-training task4
This paper introduces a simple but effective mechanism, called Exploring the Diversity and Invariance in Yourself E-DIY, which maximally extracts the multi-grained visual information inside each image.
- Network Adjustment: Channel and Block Search Guided by Resource Utilization Ratio4
A novel framework named network adjustment is presented which considers accuracy as a function of the computational resource (e.g., FLOPs or parameters) so that architecture design becomes an optimization problem and can be solved with the gradient-based optimization method.
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- Disassembling the Dataset: A Camera Alignment Mechanism for Multiple Tasks in Person Re-identification1
A fine-grained distribution alignment formulation, which disassembles the dataset and aligns all training and testing cameras, and devise the Camera-based Batch Normalization, which is easy for integration and nearly cost-free for existing ReID methods.
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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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