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Found 8 papers, 3 papers with code
LaSagnA: Language-based Segmentation Assistant for Complex Queries
Recent advancements have empowered Large Language Models for Vision (vLLMs) to generate detailed perceptual outcomes, including bounding boxes and masks.
AnyV2V: A Plug-and-Play Framework For Any Video-to-Video Editing Tasks
In the second stage, AnyV2V can plug in any existing image-to-video models to perform DDIM inversion and intermediate feature injection to maintain the appearance and motion consistency with the source video.
ConsistI2V: Enhancing Visual Consistency for Image-to-Video Generation
To verify the effectiveness of our method, we propose I2V-Bench, a comprehensive evaluation benchmark for I2V generation.
VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation
We evaluate VIESCORE on seven prominent tasks in conditional image tasks and found: (1) VIESCORE (GPT4-v) achieves a high Spearman correlation of 0. 3 with human evaluations, while the human-to-human correlation is 0. 45.
UniIR: Training and Benchmarking Universal Multimodal Information Retrievers
Existing information retrieval (IR) models often assume a homogeneous format, limiting their applicability to diverse user needs, such as searching for images with text descriptions, searching for a news article with a headline image, or finding a similar photo with a query image.
MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning.
DreamEdit: Subject-driven Image Editing
In this work, we aspire to fill the void and propose two novel subject-driven sub-tasks, i. e., Subject Replacement and Subject Addition.
Sparsifiner: Learning Sparse Instance-Dependent Attention for Efficient Vision Transformers
Equipped with the learned unstructured attention pattern, sparse attention ViT (Sparsifiner) produces a superior Pareto-optimal trade-off between FLOPs and top-1 accuracy on ImageNet compared to token sparsity.
