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Raviteja Vemulapalli

20 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

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2026

Learning to Reason for Hallucination Span Detection

Hsuan Su, Ting-Yao Hu, Hema Swetha Koppula, Kundan Krishna, Hadi Pouransari, Cheng-Yu Hsieh +4
ICLR 2026poster

Large language models (LLMs) often generate hallucinations---unsupported content that undermines reliability. While most prior works frame hallucination detection as a binary task, many real-world applications require identifying hallucinated spans, which is a multi-step decision making process. Thi…

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2025

Mutual Reinforcement of LLM Dialogue Synthesis and Summarization Capabilities for Few-Shot Dialogue Summarization

Yen-Ju Lu, Ting-Yao Hu, Hema Swetha Koppula, Hadi Pouransari, Jen-Hao Rick Chang, Yin Xia +5
NAACL 2025findings

In this work, we propose Mutual Reinforcing Data Synthesis (MRDS) within LLMs to improve few-shot dialogue summarization task. Unlike prior methods that require external knowledge, we mutually reinforce the LLM’s dialogue synthesis and summarization capabilities, allowing them to complement each oth…

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2025

Proxy-FDA: Proxy-based Feature Distribution Alignment for Fine-tuning Vision Foundation Models without Forgetting

Chen Huang, Skyler Seto, Hadi Pouransari, Mehrdad Farajtabar, Raviteja Vemulapalli, Fartash Faghri +3
ICML 2025poster

Vision foundation models pre-trained on massive data encode rich representations of real-world concepts, which can be adapted to downstream tasks by fine-tuning. However, fine-tuning foundation models on one task often leads to the issue of *concept forgetting* on other tasks. Recent methods of robu…

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2025

TiC-LM: A Web-Scale Benchmark for Time-Continual LLM Pretraining

Jeffrey Li, Mohammadreza Armandpour, Seyed Iman Mirzadeh, Sachin Mehta, Vaishaal Shankar, Raviteja Vemulapalli +5
ACL 2025long

Large Language Models (LLMs) trained on historical web data inevitably become outdated. We investigate evaluation strategies and update methods for LLMs as new data becomes available. We introduce a web-scale dataset for time-continual pretraining of LLMs derived from 114 dumps of Common Crawl (CC)…

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2024

Corpus Synthesis for Zero-Shot ASR Domain Adaptation Using Large Language Models

Hsuan Su, Ting-Yao Hu, Hema Swetha Koppula, Raviteja Vemulapalli, Jen-Hao Rick Chang, Karren D. Yang +2
ICASSP 2024accepted

While Automatic Speech Recognition (ASR) systems are widely used in many real-world applications, they often do not generalize well to new domains and need to be fine-tuned on data from these domains. However, target-domain data usually are not readily available in many scenarios. In this paper, we…

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2024

Knowledge Transfer from Vision Foundation Models for Efficient Training of Small Task-specific Models

Raviteja Vemulapalli, Hadi Pouransari, Fartash Faghri, Sachin Mehta, Mehrdad Farajtabar, Mohammad Rastegari +1
ICML 2024poster

Vision Foundation Models (VFMs) pretrained on massive datasets exhibit impressive performance on various downstream tasks, especially with limited labeled target data. However, due to their high inference compute cost, these models cannot be deployed for many real-world applications. Motivated by th…

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2024

MUSCLE: A Model Update Strategy for Compatible LLM Evolution

Jessica Maria Echterhoff, Fartash Faghri, Raviteja Vemulapalli, Ting-Yao Hu, Chun-Liang Li, Oncel Tuzel +1
EMNLP 2024finding

Large Language Models (LLMs) are regularly updated to enhance performance, typically through changes in data or architecture. Within the update process, developers often prioritize improving overall performance metrics, paying less attention to maintaining compatibility with earlier model versions.…

Cited by 4Source ↗PDF ↗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…

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2024

Probabilistic Speech-Driven 3D Facial Motion Synthesis: New Benchmarks Methods and Applications

Karren D. Yang, Anurag Ranjan, Jen-Hao Rick Chang, Raviteja Vemulapalli, Oncel Tuzel
CVPR 2024poster

We consider the task of animating 3D facial geometry from speech signal. Existing works are primarily deterministic focusing on learning a one-to-one mapping from speech signal to 3D face meshes on small datasets with limited speakers. While these models can achieve high-quality lip articulation for…

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2024

TiC-CLIP: Continual Training of CLIP Models

Saurabh Garg, Mehrdad Farajtabar, Hadi Pouransari, Raviteja Vemulapalli, Sachin Mehta, Oncel Tuzel +2
ICLR 2024poster

Keeping large foundation models up to date on latest data is inherently expensive. To avoid the prohibitive costs of constantly retraining, it is imperative to continually train these models. This problem is exacerbated by the lack of any large scale continual learning benchmarks or baselines. We in…

Cited by 35Source ↗PDF ↗code ↗Scholar ↗
2021

Boosting Image-based Mutual Gaze Detection using Pseudo 3D Gaze

Bardia Doosti, Ching-Hui Chen, Raviteja Vemulapalli, Xuhui Jia, Yukun Zhu, Bradley Green
AAAI 2021technical

Mutual gaze detection, i.e., predicting whether or not two people are looking at each other, plays an important role in understanding human interactions. In this work, we focus on the task of image-based mutual gaze detection, and propose a simple and effective approach to boost the performance by u…

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2021

Contrastive Learning for Label Efficient Semantic Segmentation

Xiangyun Zhao, Raviteja Vemulapalli, Philip Andrew Mansfield, Boqing Gong, Bradley Green, Lior Shapira +1
ICCV 2021poster

Collecting labeled data for the task of semantic segmentation is expensive and time-consuming, as it requires dense pixel-level annotations. While recent Convolutional Neural Network (CNN) based semantic segmentation approaches have achieved impressive results by using large amounts of labeled train…

Cited by 209PDF ↗code ↗Scholar ↗
2020

Search to Distill: Pearls Are Everywhere but Not the Eyes

Yu Liu, Xuhui Jia, Mingxing Tan, Raviteja Vemulapalli, Yukun Zhu, Bradley Green +1
CVPR 2020oral

Standard Knowledge Distillation (KD) approaches distill the knowledge of a cumbersome teacher model into the parameters of a student model with a pre-defined architecture. However, the knowledge of a neural network, which is represented by the network's output distribution conditioned on its input,…

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2016

Deep Gaussian Conditional Random Field Network: A Model-Based Deep Network for Discriminative Denoising

Raviteja Vemulapalli, Oncel Tuzel, Ming-Yu Liu
CVPR 2016poster

We propose a novel end-to-end trainable deep network architecture for image denoising based on a Gaussian Conditional Random Field (GCRF) model. In contrast to the existing discriminative denoising methods that train a separate model for each individual noise level, the proposed deep network explici…

Cited by 84PDF ↗Scholar ↗
2016

Gaussian Conditional Random Field Network for Semantic Segmentation

Raviteja Vemulapalli, Oncel Tuzel, Ming-Yu Liu, Rama Chellapa
CVPR 2016spotlight

In contrast to the existing approaches that use discrete Conditional Random Field (CRF) models, we propose to use a Gaussian CRF model for the task of semantic segmentation. We propose a novel deep network, which we refer to as Gaussian Mean Field (GMF) network, whose layers perform mean field infer…

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