Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks206 from public data
- 828
- DER: Dynamically Expandable Representation for Class Incremental Learning707
This work proposes a novel two-stage learning approach that utilizes a dynamically expandable representation for more effective incremental concept modeling and introduces an auxiliary loss to encourage the model to learn diverse and discriminate features for novel concepts.
- Shape-Aware Semi-supervised 3D Semantic Segmentation for Medical Images527
This work proposes a novel shapeaware semi-supervised segmentation strategy to leverage abundant unlabeled data and to enforce a geometric shape constraint on the segmentation output and develops a multi-task deep network that jointly predicts semantic segmentation and signed distance map(SDM) of object surfaces.
- Part-Aware Prototype Network for Few-Shot Semantic Segmentation424
A novel few-shot semantic segmentation framework based on the prototype representation, capable of capturing diverse and fine-grained object features, and a novel graph neural network model to generate and enhance the proposed part-aware prototypes based on labeled and unlabeled images.
- 382
- Distribution Alignment: A Unified Framework for Long-tail Visual Recognition376
This work develops an adaptive calibration function that enables us to adjust the classification scores for each data point in the two-stage learning to balance the class prior, which provides a flexible and unified solution to diverse scenarios in visual recognition tasks.
- SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models313
SPHINX, a versatile multi-modal large language model (MLLM) with a joint mixing of model weights, tuning tasks, and visual embeddings is presented, and an efficient strategy aiming to better capture fine-grained appearances of high-resolution images is proposed.
- Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation282
This work introduces a novel confidence-aware bipartite graph neural network with adaptive message propagation mechanism for unbiased scene graph generation and proposes an efficient bi-level data resampling strategy to alleviate the imbalanced data distribution problem in training the authors' graph network.
- SentiCap: Generating Image Descriptions with Sentiments248
This work designs a system to describe an image with emotions, and presents a model that automatically generates captions with positive or negative sentiments, and proposes a novel switching recurrent neural network with word-level regularization, able to produce emotional image captions using only 2000+ training sentences containing sentiments.
- 241
- Pose-Aware Multi-Level Feature Network for Human Object Interaction Detection239
This work develops a multi-branch deep network to learn a pose-augmented relation representation at three semantic levels, incorporating interaction context, object features and detailed semantic part cues, and demonstrates its efficacy in handling complex scenes.
- GNeRF: GAN-based Neural Radiance Field without Posed Camera234
GNeRF, a framework to marry Generative Adversarial Networks (GAN) with Neural Radiance Field reconstruction for the complex scenarios with unknown and even randomly initialized camera poses, is introduced and outperforms the baselines favorably in those scenes with repeated patterns or low textures that are regarded as extremely challenging before.
- Learning and Incorporating Top-Down Cues in Image Segmentation218
This paper proposes an approach to utilizing category-based information in segmentation, through a formulation as an image labelling problem, that exploits bottom-up image cues to create an over-segmented representation of an image.
- SGTR: End-to-end Scene Graph Generation with Transformer193
A transformer-based end-to-end framework that first generates the entity and predicate proposal set, followed by inferring directed edges to form the relation triplets, and a new entity-aware predicate representation based on a structural predicate generator that leverages the compositional property of relationships is developed.
- 161
- SemStyle: Learning to Generate Stylised Image Captions Using Unaligned Text125
A model that learns to generate visually relevant styled captions from a large corpus of styled text without aligned images, and a unified language model that decodes sentences with diverse word choices and syntax for different styles.
- 119
- 116
- 108
- HOICLIP: Efficient Knowledge Transfer for HOI Detection with Vision-Language Models105
A novel HOI detection framework that efficiently extracts prior knowledge from CLIP and achieves better generalization is proposed and a training-free enhancement to exploit global HOI predictions from ClIP is proposed.
- Learning Cross-Modal Context Graph for Visual Grounding100
A language-guided graph representation is proposed to capture the global context of grounding entities and their relations, and a cross-modal graph matching strategy for the multiple-phrase visual grounding task is developed.
- From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery98
This work establishes a domain-oriented synthesis of autonomous scientific discovery and positions Agentic Science as a structured paradigm for advancing AI-driven research.
- Deep photonic reservoir computing recurrent network98
It is found that the deep PRC owns strong ability to compensate the nonlinearity of fibers, and is applied in the real-world signal equalization of an optical fiber communication system.
- Dynamic Context Correspondence Network for Semantic Alignment98
This paper proposes a context-aware semantic representation that incorporates spatial layout for robust matching against local ambiguities and develops a novel dynamic fusion strategy based on attention mechanism to weave the advantages of both local and context features by integrating semantic cues from multiple scales.
- Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action Recognition94
A novel multi-granular spatio-temporal graph network is proposed for skeleton-based action classification that jointly models the coarse- and fine-grained skeleton motion patterns and utilises a cross-head communication strategy to mutually enhance the representations of both heads.
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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.