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Found 12 papers, 6 papers with code
Context-based Transfer and Efficient Iterative Learning for Unbiased Scene Graph Generation
Thus, we introduce a plug-and-play method named CITrans, which iteratively trains SGG models with progressively enhanced data.
ProS: Prompting-to-simulate Generalized knowledge for Universal Cross-Domain Retrieval
Then, in Context-aware Simulator Learning stage, we train a Content-aware Prompt Simulator under a simulated test scenarios to produce the corresponding CaDP.
Generalized Unbiased Scene Graph Generation
To the end, we propose the Multi-Concept Learning (MCL) framework, which ensures a balanced learning process across rare/ uncommon/ common concepts.
A Differentiable Semantic Metric Approximation in Probabilistic Embedding for Cross-Modal Retrieval
To verify the effectiveness of our approach, extensive experiments are conducted on MS-COCO, CUB Captions, and Flickr30K, which are commonly used in cross-modal retrieval.
Visual Commonsense-aware Representation Network for Video Captioning
Generating consecutive descriptions for videos, i. e., Video Captioning, requires taking full advantage of visual representation along with the generation process.
Progressive Tree-Structured Prototype Network for End-to-End Image Captioning
Specifically, we design a novel embedding method called tree-structured prototype, producing a set of hierarchical representative embeddings which capture the hierarchical semantic structure in textual space.
Dual-branch Hybrid Learning Network for Unbiased Scene Graph Generation
Experiments show that our approach achieves a new state-of-the-art performance on VG and GQA datasets and makes a trade-off between the performance of tail predicates and head ones.
Adaptive Fine-Grained Predicates Learning for Scene Graph Generation
The performance of current Scene Graph Generation (SGG) models is severely hampered by hard-to-distinguish predicates, e. g., woman-on/standing on/walking on-beach.
Learning To Generate Scene Graph from Head to Tail
SCM is proposed to relieve semantic deviation by ensuring the semantic consistency between the generated scene graph and the ground truth in global and local representations.
From Pixels to Objects: Cubic Visual Attention for Visual Question Answering
Existing visual attention models are generally planar, i. e., different channels of the last conv-layer feature map of an image share the same weight.
Structured Two-stream Attention Network for Video Question Answering
To date, visual question answering (VQA) (i. e., image QA and video QA) is still a holy grail in vision and language understanding, especially for video QA.
Support-set based Multi-modal Representation Enhancement for Video Captioning
Video captioning is a challenging task that necessitates a thorough comprehension of visual scenes.
