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Research Area

Computer vision

Helping devices see and understand our visual world.

Recent publications

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  • Zechen Bai, Tong He, Haiyang Mei, Pichao Wang, Ziteng Gao, Joya Chen, Lei Liu, Pichao Wang, Zheng Zhang, Mike Zheng Shou
    2024
    We introduce VideoLISA, a video-based multimodal large language model designed to tackle the problem of language-instructed reasoning segmentation in videos. Leveraging the reasoning capabilities and world knowledge of large language models, and augmented by the Segment Anything Model, VideoLISA generates temporally consistent segmentation masks in videos based on language instructions. Existing image-based
  • Yifan Li, Yikai Wang, Yanwei Fu, Dongyu Ru, Zheng Zhang, Tong He
    2024
    Visual-Language Alignment (VLA) has gained a lot of attention since CLIP’s groundbreaking work. Although CLIP performs well, the typical direct latent feature alignment lacks clarity in its representation and similarity scores. On the other hand, lexical representation, a vector whose element represents the similarity between the sample and a word from the vocabulary, is a natural sparse representation
  • Amazon Artificial General Intelligence
    Amazon Technical Reports
    2024
    We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highly capable multimodal model with the best combination of accuracy, speed, and cost for a wide range of tasks. Amazon Nova Lite is a low-cost multimodal model that is lightning fast for processing images, video, documents and text
  • Koushik Manjunatha, Morris Hsu, Rohit Kumar
    MLTEC 2024
    2024
    The increasing popularity of wireless sensing applications has led to a growing demand for large datasets of realistic wireless data. However, collecting such wireless data is often time-consuming and expensive. To address this challenge, we propose a synthetic data generation pipeline using human mesh generated from videos that can generate data at scale. The pipeline first generates a 3D mesh of the human
  • Seon Ho Lee, Jue Wang, Zhikang Zhang, David Fan, Xinyu (Arthur) Li
    2024
    As the scale of data and models for video understanding rapidly expand, handling long-form video input in transformer-based models presents a practical challenge. Rather than resorting to input sampling or token dropping, which may result in information loss, token merging shows promising results when used in collaboration with transformers. However, the application of token merging for long-form video

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