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Found 14 papers, 6 papers with code
Content-Rich AIGC Video Quality Assessment via Intricate Text Alignment and Motion-Aware Consistency
These models substantially mitigate flickering artifacts prevalent in prior models, enable longer and complex text prompts and generate longer videos with intricate, diverse motion patterns.
Dynamic Token Reduction during Generation for Vision Language Models
Vision-Language Models (VLMs) have achieved notable success in multimodal tasks but face practical limitations due to the quadratic complexity of decoder attention mechanisms and autoregressive generation.
IE-Bench: Advancing the Measurement of Text-Driven Image Editing for Human Perception Alignment
In this work, we introduce the Text-driven Image Editing Benchmark suite (IE-Bench) to enhance the assessment of text-driven edited images.
Enhancing Facial Consistency in Conditional Video Generation via Facial Landmark Transformation
Existing methods often fail to maintain facial feature consistency due to mismatches between the facial landmarks extracted from source videos and the target facial features in the reference image.
KnowledgeSG: Privacy-Preserving Synthetic Text Generation with Knowledge Distillation from Server
The success of large language models (LLMs) facilitate many parties to fine-tune LLMs on their own private data.
Minstrel: Structural Prompt Generation with Multi-Agents Coordination for Non-AI Experts
LLMs have demonstrated commendable performance across diverse domains.
Mitigating Hallucination in Visual-Language Models via Re-Balancing Contrastive Decoding
Concurrently, the visual branch focuses on the selection of significant tokens, refining the attention mechanism to highlight the primary subject.
VE-Bench: Subjective-Aligned Benchmark Suite for Text-Driven Video Editing Quality Assessment
To the best of our knowledge, VE-Bench introduces the first quality assessment dataset for video editing and an effective subjective-aligned quantitative metric for this domain.
FALIP: Visual Prompt as Foveal Attention Boosts CLIP Zero-Shot Performance
CLIP has achieved impressive zero-shot performance after pre-training on a large-scale dataset consisting of paired image-text data.
NTIRE 2024 Quality Assessment of AI-Generated Content Challenge
A total of 196 participants have registered in the video track.
Exploring AIGC Video Quality: A Focus on Visual Harmony, Video-Text Consistency and Domain Distribution Gap
The recent advancements in Text-to-Video Artificial Intelligence Generated Content (AIGC) have been remarkable.
LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language
Experiments illustrate that LangGPT significantly enhances the performance of LLMs.
TeG-DG: Textually Guided Domain Generalization for Face Anti-Spoofing
Enhancing the domain generalization performance of Face Anti-Spoofing (FAS) techniques has emerged as a research focus.
Towards the Desirable Decision Boundary by Moderate-Margin Adversarial Training
Adversarial training, as one of the most effective defense methods against adversarial attacks, tends to learn an inclusive decision boundary to increase the robustness of deep learning models.
