3 citations · 8 across the 19 of their papers we have counts for
5 papers · 2 filters
VisionArena: 230K Real World User-VLM Conversations with Preference Labels
Christopher Chou, Lisa Dunlap, Koki Mashita +5
With the growing adoption and capabilities of vision-language models (VLMs) comes the need for benchmarks that capture authentic user-VLM interactions. In response, we create Visio…
Visual Haystacks: A Vision-Centric Needle-In-A-Haystack Benchmark
Tsung-Han Wu, Giscard Biamby, Jerome Quenum +4
Large Multimodal Models (LMMs) have made significant strides in visual question-answering for single images. Recent advancements like long-context LMMs have allowed them to ingest…
Segment Anything without Supervision
XuDong Wang, Jingfeng Yang, Trevor Darrell
The Segmentation Anything Model (SAM) requires labor-intensive data labeling. We present Unsupervised SAM (UnSAM) for promptable and automatic whole-image segmentation that does no…
ALOHa: A New Measure for Hallucination in Captioning Models
Suzanne Petryk, David M. Chan, Anish Kachinthaya +4
Despite recent advances in multimodal pre-training for visual description, state-of-the-art models still produce captions containing errors, such as hallucinating objects not prese…
InstanceDiffusion: Instance-level Control for Image Generation
Xudong Wang, Trevor Darrell, Sai Saketh Rambhatla +2
Text-to-image diffusion models produce high quality images but do not offer control over individual instances in the image. We introduce InstanceDiffusion that adds precise instanc…