12 papers
MMOU: A Massive Multi-Task Omni Understanding and Reasoning Benchmark for Long and Complex Real-World Videos
Arushi Goel, Sreyan Ghosh, Vatsal Agarwal +16
Multimodal Large Language Models (MLLMs) have shown strong performance in visual and audio understanding when evaluated in isolation. However, their ability to jointly reason over…
VeriGraph: Scene Graphs for Execution Verifiable Robot Planning
Daniel Ekpo, Mara Levy, Saksham Suri +3
Recent progress in vision-language models (VLMs) has opened new possibilities for robot task planning, but these models often produce incorrect action sequences. To address these l…
Efficient and High-Fidelity Omni Modality Retrieval
Chuong Huynh, Manh Luong, Abhinav Shrivastava
Multimodal retrieval is the task of aggregating information from queries across heterogeneous modalities to retrieve desired targets. State-of-the-art multimodal retrieval models c…
Going Down Memory Lane: Scaling Tokens for Video Stream Understanding with Dynamic KV-Cache Memory
Vatsal Agarwal, Saksham Suri, Matthew Gwilliam +2
Streaming video understanding requires models to robustly encode, store, and retrieve information from a continuous video stream to support accurate video question answering (VQA).…
All-in-One Conditioning for Text-to-Image Synthesis
Hirunima Jayasekara, Chuong Huynh, Yixuan Ren +2
Accurate interpretation and visual representation of complex prompts involving multiple objects, attributes, and spatial relationships is a critical challenge in text-to-image synt…
Imagine, Verify, Execute: Memory-guided Agentic Exploration with Vision-Language Models
Seungjae Lee, Daniel Ekpo, Haowen Liu +3
Exploration is essential for general-purpose robotic learning, especially in open-ended environments where dense rewards, explicit goals, or task-specific supervision are scarce. V…