Search Results for author:
Found 12 papers, 7 papers with code
VISTA: Enhancing Long-Duration and High-Resolution Video Understanding by Video Spatiotemporal Augmentation
Current large multimodal models (LMMs) face significant challenges in processing and comprehending long-duration or high-resolution videos, which is mainly due to the lack of high-quality datasets.
HyperSeg: Towards Universal Visual Segmentation with Large Language Model
This paper aims to address universal segmentation for image and video perception with the strong reasoning ability empowered by Visual Large Language Models (VLLMs).
Ranked #1 on
Referring Expression Segmentation
on RefCOCO+ val
(using extra training data)
Large Language Model
Open Vocabulary Semantic Segmentation
+8
OmniEdit: Building Image Editing Generalist Models Through Specialist Supervision
This is due to the application of simple filtering methods like CLIP-score.
MANTIS: Interleaved Multi-Image Instruction Tuning
We further evaluate Mantis on single-image benchmarks and demonstrate that Mantis also maintains a strong single-image performance on par with CogVLM and Emu2.
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 Tuning-Free Framework For Any Video-to-Video Editing Tasks
AnyV2V can leverage any existing image editing tools to support an extensive array of video editing tasks, including prompt-based editing, reference-based style transfer, subject-driven editing, and identity manipulation, which were unattainable by previous methods.
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
In the rapidly advancing field of conditional image generation research, challenges such as limited explainability lie in effectively evaluating the performance and capabilities of various models.
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.
