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Claim this profileWorks82 from public data
- DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting886
This work presents a new framework for dense prediction by implicitly and explicitly leveraging the pre-trained knowledge from CLIP and converts the original image-text matching problem in CLIP to a pixel- text matching problem and uses the pixel-text score maps to guide the learning of dense prediction models.
- LAVT: Language-Aware Vision Transformer for Referring Image Segmentation569
This work shows that significantly better cross-modal alignments can be achieved through the early fusion of linguistic and visual features in intermediate layers of a vision Transformer encoder network.
- COIN: A Large-Scale Dataset for Comprehensive Instructional Video Analysis477
A simple yet effective method to capture the dependencies among different steps, which can be easily plugged into conventional proposal-based action detection methods for localizing important steps in instruction videos is proposed.
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- Uncertainty-Aware Score Distribution Learning for Action Quality Assessment202
An uncertainty-aware score distribution learning (USDL) approach for action quality assessment (AQA) is proposed, which regards an action as an instance associated with a score distribution, which describes the probability of different evaluated scores.
- FineDance: A Fine-grained Choreography Dataset for 3D Full Body Dance Generation135
A full-body dance generation network, which utilizes the diverse generation capabilities of the diffusion model to solve monotonous problems, and use expert nets to solve unreal problems, and a novel metric named Genre Matching Score to evaluate the genre-matching degree between dance and music are proposed.
- ATP-LLaVA: Adaptive Token Pruning for Large Vision Language Models104
This work proposes ATP-LLaVA, a novel approach that adaptively determines instance-specific token pruning ratios for each LLM layer, and introduces an Adaptive Token Pruning (ATP) module, which computes the importance score and pruning threshold based on input instance adaptively.
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- LOGO: A Long-Form Video Dataset for Group Action Quality Assessment67
This work designs a group-aware attention module, which can be easily plugged into existing AQA methods, to enrich the clip-wise representations based on contextual group information and achieves state-of-the-art on the LOGO dataset.
- Global Knowledge Calibration for Fast Open-Vocabulary Segmentation64
A core idea of preserving the generalizable representation when fine-tuning on known classes is proposed, and a text diversification strategy that generates a set of synonyms for each training category is introduced, which prevents the learned representation from collapsing onto specific known category names.
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- BNV-Fusion: Dense 3D Reconstruction using Bi-level Neural Volume Fusion56
This work proposes a novel bi-level fusion strategy that considers both efficiency and reconstruction quality by design, and evaluates the proposed method on multiple datasets quantitatively and qualitatively, demonstrating a significant improvement over existing methods.
- Mining Semantics-Preserving Attention for Group Activity Recognition56
A Semantics-Preserving Teacher-Student (SPTS) model for group activity recognition in videos, which aims to mine the semantics-preserving attention to automatically seek the key people and discard the misleading people.
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- Comprehensive Instructional Video Analysis: The COIN Dataset and Performance Evaluation45
This article proposes a large-scale dataset called “COIN” for COmprehensive INstructional video analysis, which contains 11,827 videos of 180 tasks in 12 domains related to the authors' daily life and proposes two simple yet effective methods, which can be easily plugged into conventional proposal-based action detection models.
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- Improved Classification for Problem Involving Overlapping Patterns37
Simulation results indicate that more reasonable and accurate information about the pattern's category could be obtained by use of DR-SVM, a novel multi-model classifier which combines SVM classifier with kNN algorithm under rough set technique.
- Tem-adapter: Adapting Image-Text Pretraining for Video Question Answer34
The proposed Tem-adapter enables the learning of temporal dynamics and complex semantics by a visual Temporal Aligner and a textual Semantic Aligner that first designs a template to fuse question and answer pairs as event descriptions and then learns a Transformer decoder with the whole video sequence as guidance for refinement.
- FlexiAct: Towards Flexible Action Control in Heterogeneous Scenarios32
RefAdapter is introduced, a lightweight image-conditioned adapter that excels in spatial adaptation and consistency preservation, surpassing existing methods in balancing appearance consistency and structural flexibility.
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Publication data from OpenAlex; 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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