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- Hua Xing YuSuggested from co-authorship
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Works265 from public data
- 1,273
- Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification1,011
Through domain adaptation experiment, it is shown that images generated by SPGAN are more suitable for domain adaptation and yield consistent and competitive re-ID accuracy on two large-scale datasets.
- Conformer: Local Features Coupling Global Representations for Visual Recognition827
Experiments show that Conformer, under the comparable parameter complexity, outperforms the visual transformer (DeiT-B) by 2.3% on ImageNet and on MSCOCO, it outperforms ResNet-101 by 3.7% and 3.6% mAPs for object detection and instance segmentation, respectively, demonstrating the great potential to be a general backbone network.
- 780
- 712
- Towards Optimal Structured CNN Pruning via Generative Adversarial Learning585
This paper proposes an effective structured pruning approach that jointly prunes filters as well as other structures in an end-to-end manner and effectively solves the optimization problem by generative adversarial learning (GAL), which learns a sparse soft mask in a label-free and an end to end manner.
- Prototype Mixture Models for Few-Shot Semantic Segmentation499
Proposed prototype mixture models (PMMs), which correlate diverse image regions with multiple prototypes to enforce the prototype-based semantic representation, incorporate rich channel-wised and spatial semantics from limited support images.
- 467
- BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers456
This work proposes to use a semantic-rich visual tokenizer as the reconstruction target for masked prediction, providing a systematic way to promote MIM from pixel-level to semantic-level, and proposes vector-quantized knowledge distillation to train the tokenizer, which discretizes a continuous semantic space to compact codes.
- Component Divide-and-Conquer for Real-World Image Super-Resolution419
This paper proposes a Component Divide-and-Conquer model and a Gradient-Weighted loss for SR, exploring the utility of guiding SR model with low-level image components, and proposes a feasible way to balance the difficulties of image components for SR.
- Scale Match for Tiny Person Detection400
A simple yet effective Scale Match approach to align the object scales between the two datasets for favorable tiny-object representation and the significant performance gain of this proposed approach over state-of-the-art detectors is shown.
- Fast and robust text detection in images and video frames380
A novel coarse-to-fine algorithm that is able to locate text lines even under complex background is proposed and Experimental results show that this approach can fast and robustly detect text lines under various conditions.
- SIXray: A Large-Scale Security Inspection X-Ray Benchmark for Prohibited Item Discovery in Overlapping Images362
A large-scale dataset and establish a baseline for prohibited item discovery in Security Inspection X-ray images, in which 6 classes of 8,929 prohibited items are manually annotated, and proposes an approach named class-balanced hierarchical refinement (CHR) to deal with these difficulties.
- AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-Identification318
A novel augmented discriminative clustering (AD-Cluster) technique that estimates and augments person clusters in target domains and enforces the discrimination ability of re-ID models with the augmented clusters.
- Weakly Supervised Instance Segmentation Using Class Peak Response298
This paper addresses the challenging image-level supervised instance segmentation task by exploiting class peak responses to enable a classification network for instance mask extraction, and reports state-of-the-art results on popular benchmarks, including PASCAL VOC 2012 and MS COCO.
- Oriented Response Networks288
Over multiple state-of-the-art DCNN architectures, such as VGG, ResNet, and STN, it is consistently observed that replacing regular filters with the proposed ARFs leads to significant reduction in the number of network parameters and improvement in classification performance.
- Timestep Embedding Tells: It’s Time to Cache for Video Diffusion Model286
This study introduces Timestep Embedding Aware Cache (TeaCache), a training-free caching approach that estimates and leverages the fluctuating differences among model outputs across timesteps to improve inference efficiency and visual quality.
- 278
- TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization274
This paper introduces the token semantic coupled attention map (TS-CAM) to take full advantage of the self-attention mechanism in visual transformer for long-range dependency extraction and achieves state-of-the-art performance.
- C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection261
A continuation optimization method is introduced into MIL and thereby creating continuation multiple instance learning (C-MIL), with the intention of alleviating the non-convexity problem in a systematic way.
- 261
- Beyond Bounding-Box: Convex-hull Feature Adaptation for Oriented and Densely Packed Object Detection258
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- Min-Entropy Latent Model for Weakly Supervised Object Detection232
A min-entropy latent model (MELM) is proposed for weakly supervised object detection, unified with feature learning and optimized with a recurrent learning algorithm, which progressively transfers the weak supervision to object locations.
- Adaptive Keyframe Sampling for Long Video Understanding217
Experiments on two long video understanding benchmarks validate that AKS improves video QA accuracy (beyond strong baselines) upon selecting informative keyframes, and reveals the importance of information pre-filtering in video-based MLLMs.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. 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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