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Found 10 papers, 4 papers with code
Towards Cross-modal Backward-compatible Representation Learning for Vision-Language Models
In vision, Backward-compatible Training (BT) has been proposed to ensure that the new model aligns with the old model's embeddings.
Distilling Vision-Language Pretraining for Efficient Cross-Modal Retrieval
We introduce a novel method named Distillation for Cross-Modal Quantization (DCMQ), which leverages the rich semantic knowledge of VLP models to improve hash representation learning.
Spherical Linear Interpolation and Text-Anchoring for Zero-shot Composed Image Retrieval
However, we conjecture that this approach has a downside: the projection module distorts the original image representation and confines the resulting composed embeddings to the text-side.
Visual Delta Generator with Large Multi-modal Models for Semi-supervised Composed Image Retrieval
Composed Image Retrieval (CIR) is a task that retrieves images similar to a query, based on a provided textual modification.
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
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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.
