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- Graph-to-Tree Learning for Solving Math Word Problems213
Graph2Tree is proposed, a novel deep learning architecture that combines the merits of the graph-based encoder and tree-based decoder to generate better solution expressions for math word problem (MWP) solution expressions.
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- Bidirectional Long-Short Term Memory for Video Description71
A novel video captioning framework, termed as BiLSTM, which deeply captures bidirectional global temporal structure in video, and which is comprehensively preserving sequential and visual information and adaptively learning dense visual features and sparse semantic representations for videos and sentences.
- Exploring Deeper! Segment Anything Model with Depth Perception for Camouflaged Object Detection66
Experimental results on COD benchmarks demonstrate that DSAM achieves excellent segmentation performance and reaches the state-of-the-art (SOTA) on COD benchmarks with less consumption of training resources.
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- GalleryGPT: Analyzing Paintings with Large Multimodal Models55
A superior large multimodal model for painting analysis composing, dubbed GalleryGPT, is introduced, slightly modified and fine-tuned based on LLaVA architecture leveraging the authors' collected data, demonstrating its superb ability of art analysis and generalization.
- NTIRE 2021 Challenge on Perceptual Image Quality Assessment55
The NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021, achieved much better results than existing IQA methods, while the winning method can demonstrate state-of-the-art performance.
- Your Negative May not Be True Negative: Boosting Image-Text Matching with False Negative Elimination53
This paper proposes a novel False Negative Elimination (FNE) strategy to select negatives via sampling, which could alleviate the problem introduced by false negatives.
- Progressive Graph Attention Network for Video Question Answering53
A novel model, termed Progressive Graph Attention Network (PGAT), which can jointly explore the multiple visual relations on object- level, frame-level and clip-level, and the experimental results demonstrate that the model significantly outperforms other state-of-the-art models.
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- Adaptively Attending to Visual Attributes and Linguistic Knowledge for Captioning40
This work designs a key control unit, termed visual gate, to adaptively decide "when" and "what" the language generator attend to during the word generation process, and employs a bottom-up workflow to learn a pool of semantic attributes for serving as the propositional attention resources.
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- Unifying Two-Stream Encoders with Transformers for Cross-Modal Retrieval33
A cross-modal retrieval framework purely based on two-stream Transformers, dubbed Hierarchical Alignment Transformers (HAT), which consists of an image Transformer, a text Transformer and a hierarchical alignment module, which demonstrates that HAT outperforms SOTA baselines by a large margin.
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- Word-to-region attention network for visual question answering31
A novel Word-to-Region Attention Network (WRAN) is proposed, which can simultaneously locate pertinent object regions instead of a uniform grid of image regions of euqal size and identify the corresponding words of the reference question; as well as enforce consistency between image object regions and core semantics in questions.
- Dynamic Multimodal Evaluation with Flexible Complexity by Vision-Language Bootstrapping30
VLB dynamically generates new visual question-answering samples through a multimodal bootstrapping module that modifies both images and language, while ensuring that newly generated samples remain consistent with the original ones by a judge module.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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