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From image to language: A critical analysis of visual question answering (vqa) approaches, challenges, and opportunities
The multimodal task of Visual Question Answering (VQA) encompassing elements of
Computer Vision (CV) and Natural Language Processing (NLP), aims to generate answers …
Computer Vision (CV) and Natural Language Processing (NLP), aims to generate answers …
Saco loss: Sample-wise affinity consistency for vision-language pre-training
Vision-language pre-training (VLP) aims to learn joint representations of vision and
language modalities. The contrastive paradigm is currently dominant in this field. However …
language modalities. The contrastive paradigm is currently dominant in this field. However …
Mllms-augmented visual-language representation learning
Visual-language pre-training has achieved remarkable success in many multi-modal tasks,
largely attributed to the availability of large-scale image-text datasets. In this work, we …
largely attributed to the availability of large-scale image-text datasets. In this work, we …
Cosmo: Contrastive streamlined multimodal model with interleaved pre-training
In the evolution of Vision-Language Pre-training, shifting from short-text comprehension to
encompassing extended textual contexts is pivotal. Recent autoregressive vision-language …
encompassing extended textual contexts is pivotal. Recent autoregressive vision-language …
Mafa: Managing false negatives for vision-language pre-training
We consider a critical issue of false negatives in Vision-Language Pre-training (VLP) a
challenge that arises from the inherent many-to-many correspondence of image-text pairs in …
challenge that arises from the inherent many-to-many correspondence of image-text pairs in …
Data-efficient multimodal fusion on a single gpu
The goal of multimodal alignment is to learn a single latent space that is shared between
multimodal inputs. The most powerful models in this space have been trained using massive …
multimodal inputs. The most powerful models in this space have been trained using massive …
Clip-cid: Efficient clip distillation via cluster-instance discrimination
Contrastive Language-Image Pre-training (CLIP) has achieved excellent performance over
a wide range of tasks. However, the effectiveness of CLIP heavily relies on a substantial …
a wide range of tasks. However, the effectiveness of CLIP heavily relies on a substantial …
Active data curation effectively distills large-scale multimodal models
Knowledge distillation (KD) is the de facto standard for compressing large-scale models into
smaller ones. Prior works have explored ever more complex KD strategies involving different …
smaller ones. Prior works have explored ever more complex KD strategies involving different …
Code less, align more: Efficient llm fine-tuning for code generation with data pruning
Recent work targeting large language models (LLMs) for code generation demonstrated that
increasing the amount of training data through synthetic code generation often leads to …
increasing the amount of training data through synthetic code generation often leads to …
VG-CALF: A vision-guided cross-attention and late-fusion network for radiology images in Medical Visual Question Answering
Image and question matching is essential in Medical Visual Question Answering (MVQA) in
order to accurately assess the visual-semantic correspondence between an image and a …
order to accurately assess the visual-semantic correspondence between an image and a …