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Pavan Kumar Anasosalu Vasu

7 accepted papers

2026

AMusE: Audio-Visual Benchmark and Alignment Framework for Agentic Multi-Speaker Understanding

Sanjoy Chowdhury, Karren D Yang, Xudong Liu, Fartash Faghri, Pavan Kumar Anasosalu Vasu, Oncel Tuzel +3
CVPR 2026

Recent multimodal large language models (MLLMs) such as GPT-4o and Qwen3-Omni show strong perception but struggle in multi-speaker, dialogue-centric settings that demand agentic reasoning, tracking who speaks, maintaining roles, and grounding events across time. These scenarios are central to multim

Cited by 0Source ↗Scholar ↗
2025

FastVLM: Efficient Vision Encoding for Vision Language Models

Pavan Kumar Anasosalu Vasu, Fartash Faghri, Chun-Liang Li, Cem Koc, Nate True, Albert Antony +5
CVPR 2025poster

Vision Language Models (VLMs) like LLaVA encode images into tokens aligned to the word embedding space of the LLM decoder. Scaling input image resolution is essential for improving performance, especially in text-rich image understanding tasks. However, popular visual encoders such as CLIP-pretraine…

Cited by 7Source ↗PDF ↗code ↗Scholar ↗
2024

Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculum

Hadi Pouransari, Chun-Liang Li, Jen-Hao Rick Chang, Pavan Kumar Anasosalu Vasu, Cem Koc, Vaishaal Shankar +1
NeurIPS 2024poster

Large language models (LLMs) are commonly trained on datasets consisting of fixed-length token sequences. These datasets are created by randomly concatenating documents of various lengths and then chunking them into sequences of a predetermined target length (concat-and-chunk). Recent attention impl…

Cited by 10Source ↗PDF ↗code ↗Scholar ↗
2024

MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training

Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Raviteja Vemulapalli, Oncel Tuzel
CVPR 2024poster

Contrastive pre-training of image-text foundation models such as CLIP demonstrated excellent zero-shot performance and improved robustness on a wide range of downstream tasks. However these models utilize large transformer-based encoders with significant memory and latency overhead which pose challe…

Cited by 46Source ↗PDF ↗code ↗Scholar ↗
2023

FastViT: A Fast Hybrid Vision Transformer Using Structural Reparameterization

Pavan Kumar Anasosalu Vasu, James Gabriel, Jeff Zhu, Oncel Tuzel, Anurag Ranjan
ICCV 2023poster

The recent amalgamation of transformer and convolutional designs has led to steady improvements in accuracy and efficiency of the models. In this work, we introduce FastViT, a hybrid vision transformer architecture that obtains the state-of-the-art latency-accuracy trade-off. To this end, we intro…

Cited by 231PDF ↗code ↗Scholar ↗
2023

MobileOne: An Improved One Millisecond Mobile Backbone

Pavan Kumar Anasosalu Vasu, James Gabriel, Jeff Zhu, Oncel Tuzel, Anurag Ranjan
CVPR 2023poster

Efficient neural network backbones for mobile devices are often optimized for metrics such as FLOPs or parameter count. However, these metrics may not correlate well with latency of the network when deployed on a mobile device. Therefore, we perform extensive analysis of different metrics by deployi…

Cited by 270Source ↗PDF ↗code ↗Scholar ↗
2022

Forward Compatible Training for Large-Scale Embedding Retrieval Systems

Vivek Ramanujan, Pavan Kumar Anasosalu Vasu, Ali Farhadi, Oncel Tuzel, Hadi Pouransari
CVPR 2022poster

In visual retrieval systems, updating the embedding model requires recomputing features for every piece of data. This expensive process is referred to as backfilling. Recently, the idea of backward compatible training (BCT) was proposed. To avoid the cost of backfilling, BCT modifies training of the…

Cited by 23PDF ↗code ↗Scholar ↗