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Works63 from public data
- Egocentric Video-Language Pretraining306
The recently released Ego4D dataset is exploited to pioneer Egocentric VLP along three directions, and a novel pretraining objective is proposed, dubbed EgoNCE, which adapts video-text contrastive learning to the egocentric domain by mining egOCentric-aware positive and negative samples.
- All in One: Exploring Unified Video-Language Pre-Training258
This work introduces an end-to-end VLP model, namely all-in-one Transformer, that embeds raw video and textual signals into joint representations using a unified backbone architecture and introduces a novel and effective token rolling operation to encode temporal representations from video clips in a non-parametric manner.
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- Expansion-Squeeze-Excitation Fusion Network for Elderly Activity Recognition195
This work attempts to effectively aggregate the discriminative information of actions and interactions from both RGB videos and skeleton sequences by attentively fusing multi-modal features by implementing a novel Expansion-Squeeze-Excitation Fusion Network (ESE-FN).
- Participation-Contributed Temporal Dynamic Model for Group Activity Recognition142
A novel Participation-Contributed Temporal Dynamic Model (PC-TDM) to recognize group activity, which mainly consists of a "One" network and a " one to Key" network, which aims at modeling the individual dynamic of each person.
- M3Net: Multi-view Encoding, Matching, and Fusion for Few-shot Fine-grained Action Recognition134
M3Net is proposed, a matching-based framework for FS-FG action recognition, which incorporates multi-view encoding, multi- view matching, and multi-View fusion to facilitate embedding encoding, similarity matching,and decision making across multiple viewpoints.
- Social Adaptive Module for Weakly-Supervised Group Activity Recognition121
This paper presents a new task named weakly-supervised group activity recognition (GAR) which differs from conventional GAR tasks in that only video-level labels are available, yet the important persons within each frame are not provided even in the training data.
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- Object-aware Video-language Pre-training for Retrieval92
Object-aware Transformers is presented, an object-centric approach that extends video-language transformer to incorporate object representations to leverage the bounding boxes and object tags to guide the training process.
- Dual-AI: Dual-path Actor Interaction Learning for Group Activity Recognition87
This work proposes a distinct Dual-path Actor Interaction (Dual-AI) framework, which flexibly arranges spatial and temporal transformers in two complementary orders, enhancing actor relations by integrating merits from different spatio-temporal paths, and introduces a novel Multi-scale Actor Contrastive Loss (MAC-Loss) between two interactive paths of Dual-AI.
- MVP-Shot: Multi-Velocity Progressive-Alignment Framework for Few-Shot Action Recognition69
A novel Multi-Velocity Progressive-alignment (MVP-Shot) framework to progressively learn and align semantic-related action features at multi-velocity levels and outperforms current state-of-the-art methods on multiple standard few-shot benchmarks.
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- Audio Deep Fake Detection System with Neural Stitching for ADD 202233
This paper describes the best system and methodology for ADD 2022: The First Audio Deep Synthesis Detection Challenge and outperforms all other systems with 10.1% equal error rate(EER) in Track 3.2.
- Video-Text Pre-training with Learned Regions for Retrieval30
This work proposes a simple yet effective module for video-text representation learning, namely RegionLearner, which can take into account the structure of objects during pre-training on large-scale video- text pairs and is much more computationally efficient.
- Spatio-Temporal Decoupled Knowledge Compensator for Few-Shot Action Recognition26
DiST, an innovative Decomposition-incorporation framework for FSAR that makes use of decoupled Spatial and Temporal knowledge provided by large language models to learn expressive multi-granularity prototypes, is proposed.
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- GPT4Ego: Unleashing the Potential of Pre-Trained Models for Zero-Shot Egocentric Action Recognition24
A straightforward yet remarkably potent VLM framework, GPT4Ego, designed to enhance the fine-grained alignment of concept and description between vision and language, which can sustainably evolve with the advancement of ever-growing pre-trained foundational models.
- DVF: Advancing Robust and Accurate Fine-Grained Image Retrieval with Retrieval Guidelines21
This paper designs a novel Dual Visual Filtering mechanism for the plain visual transformer, denoted as DVF, to capture subcategory-specific discrepancies and implements a discriminative model training strategy to improve the discriminability and generalization ability of DVF.
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- Look Less Think More: Rethinking Compositional Action Recognition19
A simple yet effective framework, Look Less Think More (LLTM), to reduce the strong association between visual objects and action-level labels (Look Less), and then discover the commonsense relationships between object categories and human actions (Think More).
- DiffusionVMR: Diffusion Model for Joint Video Moment Retrieval and Highlight Detection17
A novel framework, DiffusionVMR, is proposed to redefine the two tasks as a unified conditional denoising generation process by combining the diffusion model, inheriting the advantages of diffusion models that allow for iteratively refined results during inference, enhancing the boundary transition from coarse to fine.
- VideoExpert: Augmented LLM for Temporal-Sensitive Video Understanding16
VideoExpert is a general-purpose MLLM suitable for several temporal-sensitive video tasks and integrates two parallel modules: the Temporal Expert and the Spatial Expert, which handles specially designed spatial tokens and language input, aiming to generate content-related responses.
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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 2026-10-12. 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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