activity
20142025
most citedWhy M Heads are Better than One: Training a Diverse Ensemble of Deep Networks

197 citations · 432 across the 18 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

cs.CV2024

BLIP3-KALE: Knowledge Augmented Large-Scale Dense Captions

Anas Awadalla, Le Xue, Manli Shu +13

We introduce BLIP3-KALE, a dataset of 218 million image-text pairs that bridges the gap between descriptive synthetic captions and factual web-scale alt-text. KALE augments synthet…

cs.CV2024

Trust but Verify: Programmatic VLM Evaluation in the Wild

Viraj Prabhu, Senthil Purushwalkam, An Yan +2

Vision-Language Models (VLMs) often generate plausible but incorrect responses to visual queries. However, reliably quantifying the effect of such hallucinations in free-form respo…

cs.CL2024★ 3 cited

FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows"

Yifei Ming, Senthil Purushwalkam, Shrey Pandit +4

Ensuring faithfulness to context in large language models (LLMs) and retrieval-augmented generation (RAG) systems is crucial for reliable deployment in real-world applications, as…

cs.CL2024★ 2 cited

SFR-RAG: Towards Contextually Faithful LLMs

Xuan-Phi Nguyen, Shrey Pandit, Senthil Purushwalkam +7

Retrieval Augmented Generation (RAG), a paradigm that integrates external contextual information with large language models (LLMs) to enhance factual accuracy and relevance, has em…

cs.CV2024

xGen-VideoSyn-1: High-fidelity Text-to-Video Synthesis with Compressed Representations

Can Qin, Congying Xia, Krithika Ramakrishnan +16

We present xGen-VideoSyn-1, a text-to-video (T2V) generation model capable of producing realistic scenes from textual descriptions. Building on recent advancements, such as OpenAI'…

cs.CV2024★ 1 cited

xGen-MM (BLIP-3): A Family of Open Large Multimodal Models

Le Xue, Manli Shu, Anas Awadalla +30

This paper introduces BLIP-3, an open framework for developing Large Multimodal Models (LMMs). The framework comprises meticulously curated datasets, a training recipe, model archi…

cs.CV2024

BootPIG: Bootstrapping Zero-shot Personalized Image Generation Capabilities in Pretrained Diffusion Models

Senthil Purushwalkam, Akash Gokul, Shafiq Joty +1

Recent text-to-image generation models have demonstrated incredible success in generating images that faithfully follow input prompts. However, the requirement of using words to de…