A dual contrastive framework
In current multimodal tasks, models typically freeze the encoder and decoder while adapting intermediate layers to task-specific goals, such as region captioning. Region-level visual understanding presents significant challenges for large-scale vision-language models. While limited spatial awareness is a known issue, coarse-grained pretraining, in particular, exacerbates the difficulty of optimizing latent representations for effective encoder-decoder alignment. We propose AlignCap, a framework designed to enhance region-level understanding through fine-grained alignment of latent spaces. Our approach introduces a novel latent feature refinement module that enhances conditioned latent space representations to improve region-level captioning performance. We also propose an innovative alignment strategy, the semantic space alignment module, which boosts the quality of multimodal representations. Additionally, we incorporate contrastive learning in a novel manner within both modules to further enhance region-level captioning performance. To address spatial limitations, we employ a General Object Detection (GOD) method as a data preprocessing pipeline that enhances spatial reasoning at the regional level. Extensive experiments demonstrate that our approach significantly improves region-level captioning performance across various tasks
Code (0)
등록된 구현이 없습니다.
Tasks
Contrastive LearningDecoderobject-detectionObject DetectionSpatial ReasoningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation
Contrastive learning has achieved remarkable success in representation learning via self-supervision in unsupervised settings. However, effectively adapting contrastive learning to supervised learning tasks remains as a …
ClassificationContrastive LearningData AugmentationRepresentation Learning+4Dual Cluster Contrastive learning for Object Re-Identification
Recently, cluster contrastive learning has been proven effective for object ReID by computing the contrastive loss between the individual features and the cluster memory. However, existing methods that use the individual…
Contrastive LearningObjectPerson Re-IdentificationDual-level Hypergraph Contrastive Learning with Adaptive Temperature Enhancement
Inspired by the success of graph contrastive learning, researchers have begun exploring the benefits of contrastive learning over hypergraphs. However, these works have the following limitations in modeling the high-orde…
Contrastive LearningHypergraph Contrastive LearningHypergraph representationsRepresentation LearningResidual Contrastive Learning: Unsupervised Representation Learning from Residuals
In the era of deep learning, supervised residual learning (ResL) has led to many breakthroughs in low-level vision such as image restoration and enhancement tasks. However, the question of how to formalize and take advan…
Contrastive LearningImage ReconstructionImage RestorationRepresentation Learning+1Inter-intra Variant Dual Representations forSelf-supervised Video Recognition
Contrastive learning applied to self-supervised representation learning has seen a resurgence in deep models. In this paper, we find that existing contrastive learning based solutions for self-supervised video recognitio…
Contrastive LearningRepresentation LearningRetrievalVideo Recognition+1