15 citations · 16 across the 10 of their papers we have counts for
10 papers
Beyond 3D VQAs: Injecting 3D Spatial Priors into Vision-Language Models for Enhanced Geometric Reasoning
Chun-Hsiao Yeh, Shengyi Qian, Manchen Wang +3
Vision-Language Models (VLMs) often struggle with robust 3D spatial reasoning. Prevailing methods that rely on fine-tuning with 3D visual question-answering (VQA) datasets may over…
Computer Use at the Edge of the Statistical Precipice
Pierluca D'Oro, Sneha Silwal, William Wong +6
Evaluating Computer Use Agents (CUAs) on interactive environments is fraught with methodological pitfalls that the field has yet to systematically address. We show that a 1MB repla…
Sparse CLIP: Co-Optimizing Interpretability and Performance in Contrastive Learning
Chuan Qin, Constantin Venhoff, Sonia Joseph +2
Contrastive Language-Image Pre-training (CLIP) has become a cornerstone in vision-language representation learning, powering diverse downstream tasks and serving as the default vis…
Benchmarking Egocentric Multimodal Goal Inference for Assistive Wearable Agents
Vijay Veerabadran, Fanyi Xiao, Nitin Kamra +10
There has been a surge of interest in assistive wearable agents: agents embodied in wearable form factors (e.g., smart glasses) who take assistive actions toward a user's goal/quer…
Towards Generalist Intelligence in Dentistry: Vision Foundation Models for Oral and Maxillofacial Radiology
Xinrui Huang, Fan Xiao, Dongming He +5
Oral and maxillofacial radiology plays a vital role in dental healthcare, but radiographic image interpretation is limited by a shortage of trained professionals. While AI approach…
EdgeTAM: On-Device Track Anything Model
Chong Zhou, Chenchen Zhu, Yunyang Xiong +8
On top of Segment Anything Model (SAM), SAM 2 further extends its capability from image to video inputs through a memory bank mechanism and obtains a remarkable performance compare…
LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding
Xiaoqian Shen, Yunyang Xiong, Changsheng Zhao +14
Multimodal Large Language Models (MLLMs) have shown promising progress in understanding and analyzing video content. However, processing long videos remains a significant challenge…
Gen2Det: Generate to Detect
Saksham Suri, Fanyi Xiao, Animesh Sinha +4
Recently diffusion models have shown improvement in synthetic image quality as well as better control in generation. We motivate and present Gen2Det, a simple modular pipeline to c…
Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images
Zhuoran Yu, Chenchen Zhu, Sean Culatana +3
Recent advances in generative deep learning have enabled the creation of high-quality synthetic images in text-to-image generation. Prior work shows that fine-tuning a pretrained d…
EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything
Yunyang Xiong, Bala Varadarajan, Lemeng Wu +9
Segment Anything Model (SAM) has emerged as a powerful tool for numerous vision applications. A key component that drives the impressive performance for zero-shot transfer and high…