Search Results for author:
Found 6 papers, 5 papers with code
Efficient Video Object Segmentation via Modulated Cross-Attention Memory
Recently, transformer-based approaches have shown promising results for semi-supervised video object segmentation.
Video-GroundingDINO: Towards Open-Vocabulary Spatio-Temporal Video Grounding
Our contributions include a novel spatio-temporal video grounding model, surpassing state-of-the-art results in closed-set evaluations on multiple datasets and demonstrating superior performance in open-vocabulary scenarios.
Hardware Resilience Properties of Text-Guided Image Classifiers
This paper presents a novel method to enhance the reliability of image classification models during deployment in the face of transient hardware errors.
Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition
Video transformer designs are based on self-attention that can model global context at a high computational cost.
Ranked #1 on
Action Recognition
on Diving-48
Self-regulating Prompts: Foundational Model Adaptation without Forgetting
To the best of our knowledge, this is the first regularization framework for prompt learning that avoids overfitting by jointly attending to pre-trained model features, the training trajectory during prompting, and the textual diversity.
Ranked #2 on
Prompt Engineering
on ImageNet V2
Vita-CLIP: Video and text adaptive CLIP via Multimodal Prompting
Through this prompting scheme, we can achieve state-of-the-art zero-shot performance on Kinetics-600, HMDB51 and UCF101 while remaining competitive in the supervised setting.
