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Found 6 papers, 4 papers with code
MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding
However, existing LLM-based large multimodal models (e. g., Video-LLaMA, VideoChat) can only take in a limited number of frames for short video understanding.
Ranked #1 on
Video Classification
on COIN
On the Robustness of Large Multimodal Models Against Image Adversarial Attacks
Recent advances in instruction tuning have led to the development of State-of-the-Art Large Multimodal Models (LMMs).
Deep Hash Distillation for Image Retrieval
To mitigate this issue, data augmentation can be applied during training.
Self-supervised Product Quantization for Deep Unsupervised Image Retrieval
Supervised deep learning-based hash and vector quantization are enabling fast and large-scale image retrieval systems.
Similarity Guided Deep Face Image Retrieval
Face image retrieval, which searches for images of the same identity from the query input face image, is drawing more attention as the size of the image database increases rapidly.
Generalized Product Quantization Network for Semi-supervised Image Retrieval
Image retrieval methods that employ hashing or vector quantization have achieved great success by taking advantage of deep learning.
