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Found 13 papers, 8 papers with code
Power Line Aerial Image Restoration under dverse Weather: Datasets and Baselines
To circumvent this problem, we propose a new task of Power Line Aerial Image Restoration under Adverse Weather (PLAIR-AW), which aims to recover clean and high-quality images from degraded images with bad weather thus improving detection performance for PLAI.
Toward Building General Foundation Models for Language, Vision, and Vision-Language Understanding Tasks
X-FM has one language encoder, one vision encoder, and one fusion encoder, as well as a new training method.
Ranked #3 on
Visual Reasoning
on NLVR2 Test
X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks
Vision language pre-training aims to learn alignments between vision and language from a large amount of data.
Ranked #1 on
Cross-Modal Retrieval
on Flickr30k
(using extra training data)
EfficientVLM: Fast and Accurate Vision-Language Models via Knowledge Distillation and Modal-adaptive Pruning
Pre-trained vision-language models (VLMs) have achieved impressive results in a range of vision-language tasks.
Write and Paint: Generative Vision-Language Models are Unified Modal Learners
In this work, we disclose the potential of symmetric generative vision-language pre-training in learning to write and paint concurrently, and propose a new unified modal model, named DaVinci, trained with prefix language modeling and prefix image modeling, a simple generative self-supervised objective on image-text pairs.
Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-training
To this end, the cross-view language modeling framework considers both multi-modal data (i. e., image-caption pairs) and multi-lingual data (i. e., parallel sentence pairs) as two different views of the same object, and trains the model to align the two views by maximizing the mutual information between them with conditional masked language modeling and contrastive learning.
VLUE: A Multi-Task Benchmark for Evaluating Vision-Language Models
We release the VLUE benchmark to promote research on building vision-language models that generalize well to more diverse images and concepts unseen during pre-training, and are practical in terms of efficiency-performance trade-off.
Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts
Most existing methods in vision language pre-training rely on object-centric features extracted through object detection and make fine-grained alignments between the extracted features and texts.
Ranked #1 on
Image Retrieval
on Flickr30K 1K test
(using extra training data)
Active Testing: An Unbiased Evaluation Method for Distantly Supervised Relation Extraction
Distant supervision has been a widely used method for neural relation extraction for its convenience of automatically labeling datasets.
AMBERT: A Pre-trained Language Model with Multi-Grained Tokenization
In fact, both fine-grained and coarse-grained tokenizations have advantages and disadvantages for learning of pre-trained language models.
GAN Driven Semi-distant Supervision for Relation Extraction
Distant supervision has been widely used in relation extraction tasks without hand-labeled datasets recently.
Multi-labeled Relation Extraction with Attentive Capsule Network
To disclose overlapped multiple relations from a sentence still keeps challenging.
Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning
Extracting relations is critical for knowledge base completion and construction in which distant supervised methods are widely used to extract relational facts automatically with the existing knowledge bases.
Ranked #1 on
Relationship Extraction (Distant Supervised)
on New York Times Corpus
(Average Precision metric)
