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Found 31 papers, 7 papers with code
Enhanced Sentence Alignment Network for Efficient Short Text Matching
Cross-sentence attention has been widely applied in text matching, in which model learns the aligned information between two intermediate sequence representations to capture their semantic relationship.
Physics-informed 4D X-ray image reconstruction from ultra-sparse spatiotemporal data
Here we present 4D physics-informed optimized neural implicit X-ray imaging (4D-PIONIX), a novel physics-informed 4D X-ray image reconstruction method combining the full physical model and a state-of-the-art DL-based reconstruction method for 4D X-ray imaging from sparse views.
When 'YES' Meets 'BUT': Can Large Models Comprehend Contradictory Humor Through Comparative Reasoning?
Understanding humor-particularly when it involves complex, contradictory narratives that require comparative reasoning-remains a significant challenge for large vision-language models (VLMs).
When Words Outperform Vision: VLMs Can Self-Improve Via Text-Only Training For Human-Centered Decision Making
Embodied decision-making is fundamental for AI agents operating in real-world environments.
Synchronized Video-to-Audio Generation via Mel Quantization-Continuum Decomposition
Video-to-audio generation is essential for synthesizing realistic audio tracks that synchronize effectively with silent videos.
VIVA: A Benchmark for Vision-Grounded Decision-Making with Human Values
Large vision language models (VLMs) have demonstrated significant potential for integration into daily life, making it crucial for them to incorporate human values when making decisions in real-world situations.
Debate-to-Write: A Persona-Driven Multi-Agent Framework for Diverse Argument Generation
The results show that our framework can generate more diverse and persuasive arguments through both automatic and human evaluations.
Cracking the Code of Juxtaposition: Can AI Models Understand the Humorous Contradictions
Through extensive experimentation and analysis of recent commercial or open-sourced large (vision) language models, we assess their capability to comprehend the complex interplay of the narrative humor inherent in these comics.
SA-MDKIF: A Scalable and Adaptable Medical Domain Knowledge Injection Framework for Large Language Models
In the next stage, we train the skill router using task-specific downstream data and use this router to integrate the acquired skills with LLMs during inference.
AMERICANO: Argument Generation with Discourse-driven Decomposition and Agent Interaction
Argument generation is a challenging task in natural language processing, which requires rigorous reasoning and proper content organization.
Minimal-time Deadbeat Consensus and Individual Disagreement Degree Prediction for High-order Linear Multi-agent Systems
Sufficient conditions are derived to guarantee both the minimal-time deadbeat consensus and the instant individual disagreement degree prediction.
MOCHA: A Multi-Task Training Approach for Coherent Text Generation from Cognitive Perspective
Teaching neural models to generate narrative coherent texts is a critical problem.
PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation
Despite recent progress of pre-trained language models on generating fluent text, existing methods still suffer from incoherence problems in long-form text generation tasks that require proper content control and planning to form a coherent high-level logical flow.
Context-Aware Interaction Network for Question Matching
Impressive milestones have been achieved in text matching by adopting a cross-attention mechanism to capture pertinent semantic connections between two sentence representations.
Non-Local Spatial Propagation Network for Depth Completion
In this paper, we propose a robust and efficient end-to-end non-local spatial propagation network for depth completion.
Ranked #1 on
Depth Completion
on NYU-Depth V2
Multi-Scale Boosted Dehazing Network with Dense Feature Fusion
To address the issue of preserving spatial information in the U-Net architecture, we design a dense feature fusion module using the back-projection feedback scheme.
Ranked #11 on
Image Dehazing
on Haze4k
Gated Fusion Network for Degraded Image Super Resolution
To address this problem, we propose a dual-branch convolutional neural network to extract base features and recovered features separately.
An Entity-Driven Framework for Abstractive Summarization
Human judges further rate our system summaries as more informative and coherent than those by popular summarization models.
Efficient Super Resolution Using Binarized Neural Network
As a way to significantly reduce model size and computation time, binarized neural network has only been shown to excel on semantic-level tasks such as image classification and recognition.
Safe Navigation with Human Instructions in Complex Scenes
In this paper, we present a robotic navigation algorithm with natural language interfaces, which enables a robot to safely walk through a changing environment with moving persons by following human instructions such as "go to the restaurant and keep away from people".
Gated Fusion Network for Joint Image Deblurring and Super-Resolution
Single-image super-resolution is a fundamental task for vision applications to enhance the image quality with respect to spatial resolution.
Learning to Deblur Images with Exemplars
However, existing methods are less effective as only few edges can be restored from blurry face images for kernel estimation.
Multi-Scale Video Frame-Synthesis Network with Transitive Consistency Loss
Traditional approaches to interpolate/extrapolate frames in a video sequence require accurate pixel correspondences between images, e. g., using optical flow.
A Comparative Study for Single Image Blind Deblurring
Using these datasets, we conduct a large-scale user study to quantify the performance of several representative state-of-the-art blind deblurring algorithms.
Soft-Segmentation Guided Object Motion Deblurring
To address these problems, we propose a novel model for object motion deblurring.
Image Deblurring Using Smartphone Inertial Sensors
Removing image blur caused by camera shake is an ill-posed problem, as both the latent image and the point spread function (PSF) are unknown.
Deblurring Low-light Images with Light Streaks
Images taken in low-light conditions with handheld cameras are often blurry due to the required long exposure time.
Ranked #12 on
Deblurring
on RealBlur-R (trained on GoPro)
Deblurring Text Images via L0-Regularized Intensity and Gradient Prior
We propose a simple yet effective L_0-regularized prior based on intensity and gradient for text image deblurring.
Joint Depth Estimation and Camera Shake Removal from Single Blurry Image
The non-uniform blur effect is not only caused by the camera motion, but also the depth variation of the scene.
