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Found 10 papers, 1 papers with code
IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFT
This is also a notable improvement over the Adapter and LoRA, which require 37-39 GB GPU memory and 350-380 seconds per epoch for training.
CFIR: Fast and Effective Long-Text To Image Retrieval for Large Corpora
Text-to-image retrieval aims to find the relevant images based on a text query, which is important in various use-cases, such as digital libraries, e-commerce, and multimedia databases.
The Relationship Between Speech Features Changes When You Get Depressed: Feature Correlations for Improving Speed and Performance of Depression Detection
The results show that the models used in the experiments improve in terms of training speed and performance when fed with feature correlation matrices rather than with feature vectors.
Cross-modal Semantic Enhanced Interaction for Image-Sentence Retrieval
To correlate the context of objects with the textual context, we further refine the visual semantic representation via the cross-level object-sentence and word-image based interactive attention.
MGRR-Net: Multi-level Graph Relational Reasoning Network for Facial Action Units Detection
While the region-level feature learning from local face patches features via graph neural network can encode the correlation across different AUs, the pixel-wise and channel-wise feature learning via graph attention network can enhance the discrimination ability of AU features from global face features.
Factored Attention and Embedding for Unstructured-view Topic-related Ultrasound Report Generation
Echocardiography is widely used to clinical practice for diagnosis and treatment, e. g., on the common congenital heart defects.
Automatic Facial Paralysis Estimation with Facial Action Units
In this paper, we propose a novel Adaptive Local-Global Relational Network (ALGRNet) for facial AU detection and use it to classify facial paralysis severity.
Structured Multi-modal Feature Embedding and Alignment for Image-Sentence Retrieval
In this work, we propose to address the above issue from two aspects: (i) constructing intrinsic structure (along with relations) among the fragments of respective modalities, e. g., "dog $\to$ play $\to$ ball" in semantic structure for an image, and (ii) seeking explicit inter-modal structural and semantic correspondence between the visual and textual modalities.
Variational Structured Semantic Inference for Diverse Image Captioning
To model these two inherent diversities in image captioning, we propose a Variational Structured Semantic Inferring model (termed VSSI-cap) executed in a novel structured encoder-inferer-decoder schema.
