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Found 12 papers, 4 papers with code
PromptFix: You Prompt and We Fix the Photo
To address these limitations, we propose PromptFix, a comprehensive framework that enables diffusion models to follow human instructions to perform a wide variety of image-processing tasks.
BattleAgent: Multi-modal Dynamic Emulation on Historical Battles to Complement Historical Analysis
This novel system aims to simulate complex dynamic interactions among multiple agents, as well as between agents and their environments, over a period of time.
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).
Empowering LLMs with Pseudo-Untrimmed Videos for Audio-Visual Temporal Understanding
This deficiency hinders LLMs from learning the alignment between time, audio-visual events, and text tokens, thus impairing their ability to temporally localize audio-visual events in videos.
GaussianStyle: Gaussian Head Avatar via StyleGAN
Existing methods like Neural Radiation Fields (NeRF) and 3D Gaussian Splatting (3DGS) have made significant strides in facial attribute control such as facial animation and components editing, yet they struggle with fine-grained representation and scalability in dynamic head modeling.
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.
