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Found 7 papers, 3 papers with code
PixelLM: Pixel Reasoning with Large Multimodal Model
PixelLM excels across various pixel-level image reasoning and understanding tasks, outperforming well-established methods in multiple benchmarks, including MUSE, single- and multi-referring segmentation.
VLAB: Enhancing Video Language Pre-training by Feature Adapting and Blending
Towards this goal, we propose a novel video-text pre-training method dubbed VLAB: Video Language pre-training by feature Adapting and Blending, which transfers CLIP representations to video pre-training tasks and develops unified video multimodal models for a wide range of video-text tasks.
Ranked #1 on
Visual Question Answering (VQA)
on MSVD-QA
(using extra training data)
CMAE-V: Contrastive Masked Autoencoders for Video Action Recognition
Contrastive Masked Autoencoder (CMAE), as a new self-supervised framework, has shown its potential of learning expressive feature representations in visual image recognition.
Contrastive Masked Autoencoders are Stronger Vision Learners
The momentum encoder, fed with the full images, enhances the feature discriminability via contrastive learning with its online counterpart.
Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning
As region-based visual features usually represent parts of an image, it is challenging for existing vision-language models to fully understand the semantics from paired natural languages.
Ranked #5 on
Visual Entailment
on SNLI-VE val
Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers
We aim to build a more accurate and thorough connection between image pixels and language semantics directly from image and sentence pairs instead of using region-based image features as the most recent vision and language tasks.
Learning Rich Image Region Representation for Visual Question Answering
We propose to boost VQA by leveraging more powerful feature extractors by improving the representation ability of both visual and text features and the ensemble of models.
