Search Results for author:
Found 9 papers, 3 papers with code
FINEMATCH: Aspect-based Fine-grained Image and Text Mismatch Detection and Correction
To address this, we propose FineMatch, a new aspect-based fine-grained text and image matching benchmark, focusing on text and image mismatch detection and correction.
V2Xum-LLM: Cross-Modal Video Summarization with Temporal Prompt Instruction Tuning
Recent efforts have been made to expand from unimodal to multimodal video summarization, categorizing the task into three sub-tasks based on the summary's modality: video-to-video (V2V), video-to-text (V2T), and a combination of video and text summarization (V2VT).
Emo-Avatar: Efficient Monocular Video Style Avatar through Texture Rendering
To address the above problems, we propose the Efficient Monotonic Video Style Avatar (Emo-Avatar) through deferred neural rendering that enhances StyleGAN's capacity for producing dynamic, drivable portrait videos.
VideoXum: Cross-modal Visual and Textural Summarization of Videos
We propose a new joint video and text summarization task.
Ranked #1 on
Video Summarization
on videoxum
PromptCap: Prompt-Guided Image Captioning for VQA with GPT-3
PromptCap outperforms generic captions by a large margin and achieves state-of-the-art accuracy on knowledge-based VQA tasks (60. 4% on OK-VQA and 59. 6% on A-OKVQA).
PromptCap: Prompt-Guided Task-Aware Image Captioning
PromptCap outperforms generic captions by a large margin and achieves state-of-the-art accuracy on knowledge-based VQA tasks (60. 4% on OK-VQA and 59. 6% on A-OKVQA).
Ranked #1 on
Visual Question Answering
on TextVQA test-standard
Improving Pre-trained Language Model Fine-tuning with Noise Stability Regularization
The advent of large-scale pre-trained language models has contributed greatly to the recent progress in natural language processing.
Noise Stability Regularization for Improving BERT Fine-tuning
The brittleness of this process is often reflected by the sensitivity to random seeds.
Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation
Unsupervised text attribute transfer automatically transforms a text to alter a specific attribute (e. g. sentiment) without using any parallel data, while simultaneously preserving its attribute-independent content.
