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- SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning1,991
This paper introduces a novel convolutional neural network dubbed SCA-CNN that incorporates Spatial and Channel-wise Attentions in a CNN that significantly outperforms state-of-the-art visual attention-based image captioning methods.
- Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks1,030
A novel model named Attentional Factorization Machine (AFM), which learns the importance of each feature interaction from data via a neural attention network, which consistently outperforms the state-of-the-art deep learning methods Wide&Deep and DeepCross with a much simpler structure and fewer model parameters.
- Video Question Answering via Gradually Refined Attention over Appearance and Motion840
This paper proposes an end-to-end model which gradually refines its attention over the appearance and motion features of the video using the question as guidance and demonstrates the effectiveness of the model by analyzing the refined attention weights during the question answering procedure.
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- Counterfactual Samples Synthesizing for Robust Visual Question Answering341
A model-agnostic Counterfactual Samples Synthesizing (CSS) training scheme that significantly improves both visual-explainable and question-sensitive abilities of VQA models and, in return, the performance of these models is further boosted.
- Zero-Shot Visual Recognition Using Semantics-Preserving Adversarial Embedding Networks314
Through adversarial learning of the two subspaces, SP-AEN can transfer the semantics from the reconstructive subspace to the discriminative one, accomplishing the improved zero-shot recognition of unseen classes.
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- Boundary Proposal Network for Two-stage Natural Language Video Localization196
A novel Boundary Proposal Network (BPNet) is proposed, a universal two-stage framework that gets rid of the issues mentioned above and outperforms the state-of-the-art NLVL methods.
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- Rethinking the Bottom-Up Framework for Query-Based Video Localization189
It is argued that the performance of bottom-up framework is severely underestimated by current unreasonable designs, including both the backbone and head network, and designed a novel top-up model: Graph-FPN with Dense Predictions (GDP).
- An artificial intelligence based data-driven approach for design ideation170
An integrated approach for enhancing design ideation by applying artificial intelligence and data mining techniques, which consists of two models, a semantic ideation network and a visual concepts combination model, which provide inspiration semantically and visually based on computational creativity theory.
- Counterfactual Critic Multi-Agent Training for Scene Graph Generation167
CMAT is a multi-agent policy gradient method that frames objects into cooperative agents, and then directly maximizes a graph-level metric as the reward, and uses a counterfactual baseline that disentangles the agent-specific reward by fixing the predictions of other agents.
- Scene Dynamics: Counterfactual Critic Multi-Agent Training for Scene Graph Generation.167
CMAT is a multi-agent policy gradient method that frames objects into cooperative agents, and then directly maximizes a graph-level metric as the reward, and uses a counterfactual baseline that disentangles the agent-specific reward by fixing the predictions of other agents.
- DEBUG: A Dense Bottom-Up Grounding Approach for Natural Language Video Localization156
This paper proposes a novel dense bottom-up framework: DEnse Bottom-Up Grounding (DEBUG), which regards all frames falling in the ground truth segment as foreground, and each foreground frame regresses the unique distances from its location to bi-directional ground truth boundaries.
- Hierarchical Fashion Graph Network for Personalized Outfit Recommendation141
A new framework, Hierarchical Fashion Graph Network (HFGN), is developed, to model relationships among users, items, and outfits simultaneously, and achieves significant improvements over the state-of-the-art compatibility matching models like NGNN and outfit recommenders like FHN.
- HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation138
This work presents HealthGPT, a powerful Medical Large Vision-Language Model (Med-LVLM) that integrates medical visual comprehension and generation capabilities within a unified autoregressive paradigm through a novel heterogeneous low-rank adaptation technique and a tailored hierarchical visual perception approach.
- Ref-NMS: Breaking Proposal Bottlenecks in Two-Stage Referring Expression Grounding126
Ref-NMS is proposed, which is the first method to yield expression-aware proposals at the first stage, and introduces a lightweight module to predict a score for aligning each box with a critical object, resulting in a significantly improved grounding performance.
- Video Question Answering via Attribute-Augmented Attention Network Learning118
This paper proposes the attribute-augmented attention network learning framework that enables the joint frame-level attribute detection and unified video representation learning for video question answering and incorporates the multi-step reasoning process for the proposed attention network to further improve the performance.
- The Devil is in the Labels: Noisy Label Correction for Robust Scene Graph Generation113
This paper proposes a novel model-agnostic NoIsy label CorrEction strategy for SGG: NICE, which can not only detect noisy samples but also reassign more high-quality predicate labels to them.
- Management of male erectile dysfunction: From the past to the future107
With the rapid development of technologies, some new methods, such as low-intensity extracorporeal shock wave and stem cell injection therapy can even repair the organic damage of the corpora cavernosa and these are important directions for the treatment of male erectile dysfunction in the future.
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- CATR: Combinatorial-Dependence Audio-Queried Transformer for Audio-Visual Video Segmentation105
A decoupled audio-video transformer that combines audio and video features from their respective temporal and spatial dimensions, capturing their combined dependence is proposed, and a block is designed, which, when stacked, enables capturing audio-visual fine-grained combinatorial-dependence in a memory-efficient manner.
- De-Biased Court’s View Generation with Causality101
A novel Attentional and Counterfactual based Natural Language Generation (AC-NLG) method, consisting of an attentional encoder and a pair of innovative counterfactual decoders, to eliminate the confounding bias in data and generate judgment-discriminative court’s views by incorporating with a synergistic judgment predictive model.
- Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning98
Sh Shapley Counterfactual Credit Assignment is proposed, a novel method for explicit credit assignment which accounts for the coalition of agents and outperforms existing cooperative MARL algorithms significantly and achieves the state-of-the-art.
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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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