7 papers · 1 filter
VSAS-Bench: Real-Time Evaluation of Visual Streaming Assistant Models
Pavan Kumar Anasosalu Vasu, Cem Koc, Fartash Faghri +6
Streaming vision-language models (VLMs) continuously generate responses given an instruction prompt and an online stream of input frames. This is a core mechanism for real-time vis…
MobileCLIP2: Improving Multi-Modal Reinforced Training
Fartash Faghri, Pavan Kumar Anasosalu Vasu, Cem Koc +4
Foundation image-text models such as CLIP with zero-shot capabilities enable a wide array of applications. MobileCLIP is a recent family of image-text models at 3-15ms latency and…
FastVLM: Efficient Vision Encoding for Vision Language Models
Pavan Kumar Anasosalu Vasu, Fartash Faghri, Chun-Liang Li +8
Scaling the input image resolution is essential for enhancing the performance of Vision Language Models (VLMs), particularly in text-rich image understanding tasks. However, popula…
FocalLens: Instruction Tuning Enables Zero-Shot Conditional Image Representations
Cheng-Yu Hsieh, Pavan Kumar Anasosalu Vasu, Fartash Faghri +5
Visual understanding is inherently contextual -- what we focus on in an image depends on the task at hand. For instance, given an image of a person holding a bouquet of flowers, we…
Graph-Based Captioning: Enhancing Visual Descriptions by Interconnecting Region Captions
Yu-Guan Hsieh, Cheng-Yu Hsieh, Shih-Ying Yeh +7
Humans describe complex scenes with compositionality, using simple text descriptions enriched with links and relationships. While vision-language research has aimed to develop mode…
SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding
Haoxiang Wang, Pavan Kumar Anasosalu Vasu, Fartash Faghri +6
The landscape of publicly available vision foundation models (VFMs), such as CLIP and Segment Anything Model (SAM), is expanding rapidly. VFMs are endowed with distinct capabilitie…