Search Results for author:
Found 11 papers, 6 papers with code
To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning
Existing visual instruction tuning methods typically prompt large language models with textual descriptions to generate instruction-following data.
Ranked #35 on
Visual Question Answering
on MM-Vet
Building an Open-Vocabulary Video CLIP Model with Better Architectures, Optimization and Data
Despite significant results achieved by Contrastive Language-Image Pretraining (CLIP) in zero-shot image recognition, limited effort has been made exploring its potential for zero-shot video recognition.
BMB: Balanced Memory Bank for Imbalanced Semi-supervised Learning
Exploring a substantial amount of unlabeled data, semi-supervised learning (SSL) boosts the recognition performance when only a limited number of labels are provided.
Open-VCLIP: Transforming CLIP to an Open-vocabulary Video Model via Interpolated Weight Optimization
Our framework extends CLIP with minimal modifications to model spatial-temporal relationships in videos, making it a specialized video classifier, while striving for generalization.
Semi-Supervised Vision Transformers
Surprisingly, we show Vision Transformers perform significantly worse than Convolutional Neural Networks when only a small set of labeled data is available.
A Multimodal Framework for Video Ads Understanding
There is a growing trend in placing video advertisements on social platforms for online marketing, which demands automatic approaches to understand the contents of advertisements effectively.
Cross-domain Contrastive Learning for Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a fully-labeled source domain to a different unlabeled target domain.
VideoLT: Large-scale Long-tailed Video Recognition
In this paper, we introduce VideoLT, a large-scale long-tailed video recognition dataset, as a step toward real-world video recognition.
HCMS: Hierarchical and Conditional Modality Selection for Efficient Video Recognition
Conventional video recognition pipelines typically fuse multimodal features for improved performance.
Imbalanced Gradients: A New Cause of Overestimated Adversarial Robustness
Evaluating the robustness of a defense model is a challenging task in adversarial robustness research.
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness
Evaluating the robustness of a defense model is a challenging task in adversarial robustness research.
