Search Results for author:
Found 10 papers, 5 papers with code
Fwd2Bot: LVLM Visual Token Compression with Double Forward Bottleneck
In this work, we aim to compress the vision tokens of a Large Vision Language Model (LVLM) into a representation that is simultaneously suitable for (a) generative and (b) discriminative tasks, (c) is nearly lossless, and (d) is storage-efficient.
VladVA: Discriminative Fine-tuning of LVLMs
Contrastively-trained Vision-Language Models (VLMs) like CLIP have become the de facto approach for discriminative vision-language representation learning.
CLIP-DPO: Vision-Language Models as a Source of Preference for Fixing Hallucinations in LVLMs
Despite recent successes, LVLMs or Large Vision Language Models are prone to hallucinating details like objects and their properties or relations, limiting their real-world deployment.
FFF: Fixing Flawed Foundations in contrastive pre-training results in very strong Vision-Language models
Despite noise and caption quality having been acknowledged as important factors impacting vision-language contrastive pre-training, in this paper, we show that the full potential of improving the training process by addressing such issues is yet to be realized.
Black Box Few-Shot Adaptation for Vision-Language models
Vision-Language (V-L) models trained with contrastive learning to align the visual and language modalities have been shown to be strong few-shot learners.
Spatial Contrastive Learning for Few-Shot Classification
In this paper, we explore contrastive learning for few-shot classification, in which we propose to use it as an additional auxiliary training objective acting as a data-dependent regularizer to promote more general and transferable features.
Autoregressive Unsupervised Image Segmentation
In this work, we propose a new unsupervised image segmentation approach based on mutual information maximization between different constructed views of the inputs.
Ranked #5 on
Unsupervised Semantic Segmentation
on COCO-Stuff-3
Target Consistency for Domain Adaptation: when Robustness meets Transferability
Learning Invariant Representations has been successfully applied for reconciling a source and a target domain for Unsupervised Domain Adaptation.
An Overview of Deep Semi-Supervised Learning
Deep neural networks demonstrated their ability to provide remarkable performances on a wide range of supervised learning tasks (e. g., image classification) when trained on extensive collections of labeled data (e. g., ImageNet).
Semi-Supervised Semantic Segmentation with Cross-Consistency Training
To leverage the unlabeled examples, we enforce a consistency between the main decoder predictions and those of the auxiliary decoders, taking as inputs different perturbed versions of the encoder's output, and consequently, improving the encoder's representations.
