130 citations · 145 across the 6 of their papers we have counts for
10 papers
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…
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'…
Diffusion Model Alignment Using Direct Preference Optimization
Bram Wallace, Meihua Dang, Rafael Rafailov +7
Large language models (LLMs) are fine-tuned using human comparison data with Reinforcement Learning from Human Feedback (RLHF) methods to make them better aligned with users' prefe…
The Challenges of Continuous Self-Supervised Learning
Senthil Purushwalkam, Pedro Morgado, Abhinav Gupta
Self-supervised learning (SSL) aims to eliminate one of the major bottlenecks in representation learning - the need for human annotations. As a result, SSL holds the promise to lea…
The Functional Correspondence Problem
Zihang Lai, Senthil Purushwalkam, Abhinav Gupta
The ability to find correspondences in visual data is the essence of most computer vision tasks. But what are the right correspondences? The task of visual correspondence is well d…
Audio-Visual Floorplan Reconstruction
Senthil Purushwalkam, Sebastian Vicenc Amengual Gari, Vamsi Krishna Ithapu +4
Given only a few glimpses of an environment, how much can we infer about its entire floorplan? Existing methods can map only what is visible or immediately apparent from context, a…