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Works557 from public data
- 1,955
- Multi-level Wavelet-CNN for Image Restoration934
This paper presents a novel multi-level wavelet CNN model for better tradeoff between receptive field size and computational efficiency, and shows the effectiveness of MWCNN for image denoising, single image super-resolution, and JPEG image artifacts removal.
- Proceedings of the 29th ACM International Conference on Multimedia922
A robust deep learning framework for real-world SR that randomly erases potential adversarial noises in the frequency domain of input images or features and is more insensitive to adversarial attacks and presents more stable SR results than existing models and defenses is proposed.
- Joint Detection and Identification Feature Learning for Person Search913
A new deep learning framework for person search that jointly handles pedestrian detection and person re-identification in a single convolutional neural network and converges much faster and better than the conventional Softmax loss.
- Is Faster R-CNN Doing Well for Pedestrian Detection?837
A very simple but effective baseline for pedestrian detection, using an RPN followed by boosted forests on shared, high-resolution convolutional feature maps, presenting competitive accuracy and good speed.
- Cost-Effective Active Learning for Deep Image Classification756
This paper proposes a novel active learning (AL) framework, which is capable of building a competitive classifier with optimal feature representation via a limited amount of labeled training instances in an incremental learning manner and incorporates deep convolutional neural networks into AL.
- Deep feature learning with relative distance comparison for person re-identification687
A scalable distance driven feature learning framework based on the deep neural network for person re-identification that achieves very promising results and outperforms other state-of-the-art approaches.
- Meta R-CNN: Towards General Solver for Instance-Level Low-Shot Learning620
This work extends Faster /Mask R-CNN by proposing meta-learning over RoI (Region-of-Interest) features instead of a full image feature, and introduces a Predictor-head Remodeling Network (PRN) that shares its main backbone with Faster / Mask R- CNN.
- Unsupervised Image Super-Resolution Using Cycle-in-Cycle Generative Adversarial Networks594
This work proposes a Cycle-in-Cycle network structure with generative adversarial networks (GAN) as the basic component to tackle the single image super-resolution problem in a more general case that the low-/high-resolution pairs and the down-sampling process are unavailable.
- 584
- Look into Person: Self-Supervised Structure-Sensitive Learning and a New Benchmark for Human Parsing516
A new benchmark Look into Person (LIP) is introduced that makes a significant advance in terms of scalability, diversity and difficulty, and a novel self-supervised structure-sensitive learning approach, which imposes human pose structures into parsing results without resorting to extra supervision.
- Toward Characteristic-Preserving Image-Based Virtual Try-On Network504
A new fully-learnable Characteristic-Preserving Virtual Try-On Network (CP-VTON) for addressing all real-world challenges in this task, and achieves the state-of-the-art virtual try-on performance both qualitatively and quantitatively.
- Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift440
This paper proposes a deep cocktail network (DCTN) to battle the domain and category shifts among multiple sources and evaluates DCTN in three domain adaptation benchmarks, which clearly demonstrate the superiority of the framework.
- Knowledge-Embedded Routing Network for Scene Graph Generation432
This work finds that the statistical correlations between object pairs and their relationships can effectively regularize semantic space and make prediction less ambiguous, and thus well address the unbalanced distribution issue.
- 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.
- 412
- 389
- Aligning Cyber Space With Physical World: A Comprehensive Survey on Embodied AI384
This survey gives a comprehensive exploration of the latest advancements in Embodied AI, exploring the complexities of MLMs in virtual and real embodied agents, highlighting their significance in facilitating interactions in digital and physical environments.
- Instance-Level Human Parsing via Part Grouping Network383
This work makes the first attempt to explore a detection-free Part Grouping Network (PGN) for efficiently parsing multiple people in an image in a single pass and outperforms all state-of-the-art methods on PASCAL-Person-Part dataset.
- Look into Person: Joint Body Parsing & Pose Estimation Network and a New Benchmark379
A new benchmark named “Look into Person (LIP)” is introduced that provides a significant advancement in terms of scalability, diversity, and difficulty, which are crucial for future developments in human-centric analysis.
- Semantic Object Parsing with Graph LSTM370
The Graph Long Short-Term Memory network is proposed, which is the generalization of LSTM from sequential data or multi-dimensional data to general graph-structured data.
- Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition366
A Semantic-Specific Graph Representation Learning (SSGRL) framework that consists of a semantic decoupling module that incorporates category semantics to guide learning semantic-specific representations and a semantic interaction module that correlates these representations with a graph built on the statistical label co-occurrence and explores their interactions via a graph propagation mechanism.
- 360
- GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning351
This paper proposes a Geometric Question Answering dataset GeoQA, containing 4,998 geometric problems with corresponding annotated programs, which illustrate the solving process of the given problems, and introduces a Neural Geometric Solver (NGS) to address geometric problems by comprehensively parsing multimodal information and generating interpretable programs.
- Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering348
Extensive experiments demonstrate the superiority of the CMCIR in discovering visual-linguistic causal structures and achieving robust event-level visual question answering.
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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