7 papers
CompLLM: Compression for Long Context Q&A
Gabriele Berton, Jayakrishnan Unnikrishnan, Son Tran +1
Large Language Models (LLMs) face significant computational challenges when processing long contexts due to the quadratic complexity of self-attention. While soft context compressi…
ViLL-E: Video LLM Embeddings for Retrieval
Rohit Gupta, Jayakrishnan Unnikrishnan, Fan Fei +3
Video Large Language Models (VideoLLMs) excel at video understanding tasks where outputs are textual, such as Video Question Answering and Video Captioning. However, they underperf…
Dictionary-Aligned Concept Control for Safeguarding Multimodal LLMs
Jinqi Luo, Jinyu Yang, Tal Neiman +5
Multimodal Large Language Models (MLLMs) have been shown to be vulnerable to malicious queries that can elicit unsafe responses. Recent work uses prompt engineering, response class…
Open Vocabulary Multi-Label Video Classification
Rohit Gupta, Mamshad Nayeem Rizve, Jayakrishnan Unnikrishnan +5
Pre-trained vision-language models (VLMs) have enabled significant progress in open vocabulary computer vision tasks such as image classification, object detection and image segmen…
M-LLM Based Video Frame Selection for Efficient Video Understanding
Kai Hu, Feng Gao, Xiaohan Nie +8
Recent advances in Multi-Modal Large Language Models (M-LLMs) show promising results in video reasoning. Popular Multi-Modal Large Language Model (M-LLM) frameworks usually apply n…
CoLLM: A Large Language Model for Composed Image Retrieval
Chuong Huynh, Jinyu Yang, Ashish Tawari +5
Composed Image Retrieval (CIR) is a complex task that aims to retrieve images based on a multimodal query. Typical training data consists of triplets containing a reference image,…