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Found 22 papers, 17 papers with code
Mirror Gradient: Towards Robust Multimodal Recommender Systems via Exploring Flat Local Minima
This strategy can implicitly enhance the model's robustness during the optimization process, mitigating instability risks arising from multimodal information inputs.
ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and Multispectral Data Fusion
Deep learning-based hyperspectral image (HSI) super-resolution, which aims to generate high spatial resolution HSI (HR-HSI) by fusing hyperspectral image (HSI) and multispectral image (MSI) with deep neural networks (DNNs), has attracted lots of attention.
Understanding Self-attention Mechanism via Dynamical System Perspective
The self-attention mechanism (SAM) is widely used in various fields of artificial intelligence and has successfully boosted the performance of different models.
LSAS: Lightweight Sub-attention Strategy for Alleviating Attention Bias Problem
In computer vision, the performance of deep neural networks (DNNs) is highly related to the feature extraction ability, i. e., the ability to recognize and focus on key pixel regions in an image.
SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with Large Language Models
Our approach can make text-to-image diffusion models easier to use with better user experience, which demonstrates our approach has the potential for further advancing the development of user-friendly text-to-image generation models by bridging the semantic gap between simple narrative prompts and complex keyword-based prompts.
ASR: Attention-alike Structural Re-parameterization
This technique enables the mitigation of the extra costs for performance improvement during training, such as parameter size and inference time, through these transformations during inference, and therefore SRP has great potential for industrial and practical applications.
UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression
Naturally, we also present a unified multi-task Geometric Transformer framework, Geoformer, to tackle calculation and proving problems simultaneously in the form of sequence generation, which finally shows the reasoning ability can be improved on both two tasks by unifying formulation.
Ranked #3 on
Mathematical Reasoning
on PGPS9K
Deepening Neural Networks Implicitly and Locally via Recurrent Attention Strategy
More and more empirical and theoretical evidence shows that deepening neural networks can effectively improve their performance under suitable training settings.
Causal Inference for Chatting Handoff
However, most existing methods mainly focus on the dialogue context or assist with global satisfaction prediction based on multi-task learning, which ignore the grounded relationships among the causal variables, like the user state and labor cost.
Switchable Self-attention Module
Attention mechanism has gained great success in vision recognition.
Mix-Pooling Strategy for Attention Mechanism
Recently many effective attention modules are proposed to boot the model performance by exploiting the internal information of convolutional neural networks in computer vision.
Real-World Image Super-Resolution by Exclusionary Dual-Learning
Real-world image super-resolution is a practical image restoration problem that aims to obtain high-quality images from in-the-wild input, has recently received considerable attention with regard to its tremendous application potentials.
LogicSolver: Towards Interpretable Math Word Problem Solving with Logical Prompt-enhanced Learning
To address this issue and make a step towards interpretable MWP solving, we first construct a high-quality MWP dataset named InterMWP which consists of 11, 495 MWPs and annotates interpretable logical formulas based on algebraic knowledge as the grounded linguistic logic of each solution equation.
Unbiased Math Word Problems Benchmark for Mitigating Solving Bias
However, current solvers exist solving bias which consists of data bias and learning bias due to biased dataset and improper training strategy.
Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks
Previous math word problem solvers following the encoder-decoder paradigm fail to explicitly incorporate essential math symbolic constraints, leading to unexplainable and unreasonable predictions.
GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning
Therefore, we propose a Geometric Question Answering dataset GeoQA, containing 4, 998 geometric problems with corresponding annotated programs, which illustrate the solving process of the given problems.
Ranked #4 on
Mathematical Reasoning
on PGPS9K
Content-adaptive Representation Learning for Fast Image Super-resolution
In contrast to existing studies that ignore difficulty diversity, we adopt different stage of a neural network to perform image restoration.
Semantically-Aligned Universal Tree-Structured Solver for Math Word Problems
A practical automatic textual math word problems (MWPs) solver should be able to solve various textual MWPs while most existing works only focused on one-unknown linear MWPs.
Ranked #10 on
Math Word Problem Solving
on ALG514
GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems
Capitalized on the topic-level dialogue graph, we propose a new evaluation metric GRADE, which stands for Graph-enhanced Representations for Automatic Dialogue Evaluation.
Dynamic Knowledge Routing Network For Target-Guided Open-Domain Conversation
Target-guided open-domain conversation aims to proactively and naturally guide a dialogue agent or human to achieve specific goals, topics or keywords during open-ended conversations.
Difficulty-aware Image Super Resolution via Deep Adaptive Dual-Network
To identify whether a region is easy or hard, we propose a novel image difficulty recognition network based on PSNR prior.
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones.
