Vision-language models for vision tasks: A survey
Most visual recognition studies rely heavily on crowd-labelled data in deep neural networks
(DNNs) training, and they usually train a DNN for each single visual recognition task …
(DNNs) training, and they usually train a DNN for each single visual recognition task …
Test-time prompt tuning for zero-shot generalization in vision-language models
Pre-trained vision-language models (eg, CLIP) have shown promising zero-shot
generalization in many downstream tasks with properly designed text prompts. Instead of …
generalization in many downstream tasks with properly designed text prompts. Instead of …
Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners
Visual recognition in low-data regimes requires deep neural networks to learn generalized
representations from limited training samples. Recently, CLIP-based methods have shown …
representations from limited training samples. Recently, CLIP-based methods have shown …
Sus-x: Training-free name-only transfer of vision-language models
V Udandarao, A Gupta… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
Abstract Contrastive Language-Image Pre-training (CLIP) has emerged as a simple yet
effective way to train large-scale vision-language models. CLIP demonstrates impressive …
effective way to train large-scale vision-language models. CLIP demonstrates impressive …
Not all features matter: Enhancing few-shot clip with adaptive prior refinement
Abstract The popularity of Contrastive Language-Image Pre-training (CLIP) has propelled its
application to diverse downstream vision tasks. To improve its capacity on downstream …
application to diverse downstream vision tasks. To improve its capacity on downstream …
Calip: Zero-shot enhancement of clip with parameter-free attention
Abstract Contrastive Language-Image Pre-training (CLIP) has been shown to learn visual
representations with promising zero-shot performance. To further improve its downstream …
representations with promising zero-shot performance. To further improve its downstream …
Binding touch to everything: Learning unified multimodal tactile representations
The ability to associate touch with other modalities has huge implications for humans and
computational systems. However multimodal learning with touch remains challenging due to …
computational systems. However multimodal learning with touch remains challenging due to …
Viewrefer: Grasp the multi-view knowledge for 3d visual grounding
Understanding 3D scenes from multi-view inputs has been proven to alleviate the view
discrepancy issue in 3D visual grounding. However, existing methods normally neglect the …
discrepancy issue in 3D visual grounding. However, existing methods normally neglect the …
Prompt learning with optimal transport for vision-language models
With the increasing attention to large vision-language models such as CLIP, there has been
a significant amount of effort dedicated to building efficient prompts. Unlike conventional …
a significant amount of effort dedicated to building efficient prompts. Unlike conventional …
Tem-adapter: Adapting image-text pretraining for video question answer
Video-language pre-trained models have shown remarkable success in guiding video
question-answering (VideoQA) tasks. However, due to the length of video sequences …
question-answering (VideoQA) tasks. However, due to the length of video sequences …