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Found 7 papers, 4 papers with code
UNIMO-2: End-to-End Unified Vision-Language Grounded Learning
In particular, we propose to conduct grounded learning on both images and texts via a sharing grounded space, which helps bridge unaligned images and texts, and align the visual and textual semantic spaces on different types of corpora.
DU-VLG: Unifying Vision-and-Language Generation via Dual Sequence-to-Sequence Pre-training
To bridge the gap between image understanding and generation, we further design a novel commitment loss.
Learning with Noisy Correspondence for Cross-modal Matching
Based on this observation, we reveal and study a latent and challenging direction in cross-modal matching, named noisy correspondence, which could be regarded as a new paradigm of noisy labels.
Weakly Supervised Dense Video Captioning via Jointly Usage of Knowledge Distillation and Cross-modal Matching
This paper proposes an approach to Dense Video Captioning (DVC) without pairwise event-sentence annotation.
UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning
Existed pre-training methods either focus on single-modal tasks or multi-modal tasks, and cannot effectively adapt to each other.
Ranked #3 on
Image Captioning
on MS COCO
Enhancing Local Feature Extraction with Global Representation for Neural Text Classification
For text classification, traditional local feature driven models learn long dependency by deeply stacking or hybrid modeling.
A New Method of Region Embedding for Text Classification
In this paper, we propose a new method of learning and utilizing task-specific distributed representations of n-grams, referred to as “region embeddings”.
