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- Yu–Gang JiangSuggested from co-authorship
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Works45 from public data
- Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition124
A depth guided Adaptive Meta-Fusion Network for few-shot video recognition which is termed as AMeFu-Net, which is trained in the meta-learning way and demonstrates the effectiveness of the model on several action recognition benchmarks.
- StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning110
A novel model-agnostic meta Style Adversarial training (StyleAdv) method together with a novel style adversarial attack method is proposed for CD-FSL, which boosts the generalization ability for novel target datasets.
- Generalized Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data101
A novel Generalized Meta-learning based Feature-Disentangled Mixup network, namely GMeta-FDMixup is proposed to address Cross-Domain Few-Shot Learning (CD-FSL), and a novel feature disentanglement module is contributed to narrow the domain gap explicitly.
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- Embodied One-Shot Video Recognition45
A novel setting is introduced, termed as embodied agents based one- shot learning, which leverages synthetic videos produced in a virtual environment to understand realistic videos of target classes and proposes a general video segment augmentation method, which significantly facilitates a variety of one-shot learning tasks.
- NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results44
An overview of the 1st NTIRE 2025 CD-FSOD Challenge is presented, highlighting the proposed solutions and summarizing the results submitted by the participants.
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- Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning39
This paper studies the problem of CD-FSL by spanning the style distributions of the source dataset, and proposes a novel Style Augmentation (StyleAug) module and a Self-Supervised Learning (SSL) module to ensure the predictions of style-augmented images are semantically similar to the unchanged ones.
- TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot Learning30
This paper proposes a novel target guided dynamic mixup (TGDM) framework that leverages the target data to guide the generation of mixed images via dynamic mix up and introduces an intermediate domain generated by mixing images in the source and the target domain.
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- ME-D2N: Multi-Expert Domain Decompositional Network for Cross-Domain Few-Shot Learning27
To solve the data imbalance problem between the source data with sufficient examples and the auxiliary target data with limited examples, a novel Multi-Expert Domain Decompositional Network (ME-D2N) is built under the umbrella of multi-expert learning.
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- On the Importance of Spatial Relations for Few-shot Action Recognition21
It is found that the spatial misalignment between objects also occurs in videos, notably more common than the temporal inconsistency, and a novel Spatial Alignment Cross Transformer (SA-CT) which learns to re-adjust the spatial relations and incorporates the temporal information is contributed.
- V$^{2}$-SAM: Marrying SAM2 with Multi-Prompt Experts for Cross-View Object Correspondence15
V2-SAM is presented, a unified cross-view object correspondence framework that adapts SAM2 from single-view segmentation to cross-view correspondence through two complementary prompt generators and introduces a Post-hoc Cyclic Consistency Selector (PCCS) that adaptively selects the most reliable expert based on cyclic consistency.
- Sequential Multi-Object Grasping with One Dexterous Hand12
This paper proposes SeqMultiGrasp, a system for sequentially grasping objects with a four-fingered Allegro Hand, which focuses on sequentially grasping two objects, ensuring that the hand fully encloses one object before lifting it and then grasps the second object without dropping the first.
- MinD-3D++: Advancing fMRI-Based 3D Reconstruction With High-Quality Textured Mesh Generation and a Comprehensive Dataset11
The fMRI-3D dataset is presented, a novel framework for decoding textured 3D visual information from fMRI signals is proposed, and the framework evaluates the feasibility of not only reconstructing 3D objects from the human mind but also generating, for the first time, 3D textured meshes with detailed textures from fMRI data.
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- Unified View Empirical Study for Large Pretrained Model on Cross-Domain Few-Shot Learning7
This study presents an exhaustive empirical assessment of the Contrastive Language–Image Pre-Training model within the CD-FSL task, and establishes a straightforward baseline model, E-base, based on the authors' empirical analysis, underscoring the importance of this investigation.
- Cross-View Multi-Modal Segmentation @ Ego-Exo4D Challenges 20255
A cross-view multi-modal object segmentation approach that enhances object localization by leveraging both visual masks and textual descriptions as segmentation conditions and introduces a cross-view object alignment module that enforces object-level consistency across perspectives, thereby improving the model's robustness to viewpoint changes.
- Can Action be Imitated? Learn to Reconstruct and Transfer Human Dynamics from Videos3
This paper introduces a novel task, dubbedmeshbased action imitation, to enable an arbitrary target human mesh to perform the same action shown on the video demonstration, using a novel Mesh-based Video Action Imitation (M-VAI) method.
- RAG-6DPose: Retrieval-Augmented 6D Pose Estimation via Leveraging CAD as Knowledge Base2
RAG-6DPose is presented, a retrieval-augmented approach that leverages 3D CAD models as a knowledge base by integrating both visual and geometric cues, demonstrating the effectiveness and robustness of the approach, particularly in handling occlusions and novel viewpoints.
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- Students Report of ACM Multimedia 2021–
Due to the COVID-19, the annual ACM Multimedia Conference was held in a hybrid mode - onsite in Chengdu, China, and online jointly this year.
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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 2026-10-13. 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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