7 citations · 21 across the 9 of their papers we have counts for
8 papers · 1 filter
LLaVA-Zip: Adaptive Visual Token Compression with Intrinsic Image Information
Ke Wang, Hong Xuan
Multi-modal large language models (MLLMs) utilizing instruction-following data, such as LLaVA, have achieved great progress in the industry. A major limitation in these models is t…
TinyCLIP: CLIP Distillation via Affinity Mimicking and Weight Inheritance
Kan Wu, Houwen Peng, Zhenghong Zhou +10
In this paper, we propose a novel cross-modal distillation method, called TinyCLIP, for large-scale language-image pre-trained models. The method introduces two core techniques: af…
Dissecting Deep Metric Learning Losses for Image-Text Retrieval
Hong Xuan, Xi Chen
Visual-Semantic Embedding (VSE) is a prevalent approach in image-text retrieval by learning a joint embedding space between the image and language modalities where semantic similar…
Dissecting the impact of different loss functions with gradient surgery
Hong Xuan, Robert Pless
Pair-wise loss is an approach to metric learning that learns a semantic embedding by optimizing a loss function that encourages images from the same semantic class to be mapped clo…
Hard negative examples are hard, but useful
Hong Xuan, Abby Stylianou, Xiaotong Liu +1
Triplet loss is an extremely common approach to distance metric learning. Representations of images from the same class are optimized to be mapped closer together in an embedding s…
Improved Embeddings with Easy Positive Triplet Mining
Hong Xuan, Abby Stylianou, Robert Pless
Deep metric learning seeks to define an embedding where semantically similar images are embedded to nearby locations, and semantically dissimilar images are embedded to distant loc…
Hotels-50K: A Global Hotel Recognition Dataset
Abby Stylianou, Hong Xuan, Maya Shende +3
Recognizing a hotel from an image of a hotel room is important for human trafficking investigations. Images directly link victims to places and can help verify where victims have b…
Deep Randomized Ensembles for Metric Learning
Hong Xuan, Richard Souvenir, Robert Pless
Learning embedding functions, which map semantically related inputs to nearby locations in a feature space supports a variety of classification and information retrieval tasks. In…