Search Results for author:
Found 114 papers, 46 papers with code
Learning Progressive Joint Propagation for Human Motion Prediction
Last, in order to incorporate a general motion space for high-quality prediction, we build a memory-based dictionary, which aims to preserve the global motion patterns in training data to guide the predictions.
Distribution-Conditional Generation: From Class Distribution to Creative Generation
Building on this, we propose DisTok, an encoder-decoder framework that maps class distributions into a latent space and decodes them into tokens of creative concept.
Enhancing Multimodal In-Context Learning for Image Classification through Coreset Optimization
By leveraging untapped samples from the support set, we update the keys of selected coreset samples, enabling the randomly initialized coreset to evolve into a more informative coreset under low computational cost.
A Graph-Enhanced DeepONet Approach for Real-Time Estimating Hydrogen-Enriched Natural Gas Flow under Variable Operations
Second, a graph-enhance branch network is proposed to incorporate pipeline topology, improving the estimation accuracy in large-scale pipeline networks.
PlatMetaX: An Integrated MATLAB platform for Meta-Black-Box Optimization
The landscape of optimization problems has become increasingly complex, necessitating the development of advanced optimization techniques.
DTA: Dual Temporal-channel-wise Attention for Spiking Neural Networks
To the best of our knowledge, this is the first attempt to concentrate on both the correlation and dependency of temporal-channel using both identical and non-identical attention operations.
LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL
Enhancing reasoning in Large Multimodal Models (LMMs) faces unique challenges from the complex interplay between visual perception and logical reasoning, particularly in compact 3B-parameter architectures where architectural constraints limit reasoning capacity and modality alignment.
AppAgentX: Evolving GUI Agents as Proficient Smartphone Users
This allows the agent to focus on tasks requiring more complex reasoning, while simplifying routine actions.
Speculative Ensemble: Fast Large Language Model Ensemble via Speculation
Ensemble methods enhance Large Language Models (LLMs) by combining multiple models but suffer from high computational costs.
Reinforcement learning Based Automated Design of Differential Evolution Algorithm for Black-box Optimization
To address this challenge, we introduce a novel framework that employs reinforcement learning (RL) to automatically design DE for black-box optimization through meta-learning.
STHFL: Spatio-Temporal Heterogeneous Federated Learning
Federated learning is a new framework that protects data privacy and allows multiple devices to cooperate in training machine learning models.
Video Repurposing from User Generated Content: A Large-scale Dataset and Benchmark
The demand for producing short-form videos for sharing on social media platforms has experienced significant growth in recent times.
RL2: Reinforce Large Language Model to Assist Safe Reinforcement Learning for Energy Management of Active Distribution Networks
In this paper, we introduce the LLM to comprehend operational safety requirements in ADNs and generate corresponding penalty functions.
BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning
This reformulation enables the use of first-order optimization algorithms in calculating the backward pass gradients, allowing our framework to potentially utilize any state-of-the-art solver.
Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lens
In this paper, we address how LVLMs process visual information and whether this process causes hallucination.
Number it: Temporal Grounding Videos like Flipping Manga
Video Large Language Models (Vid-LLMs) have made remarkable advancements in comprehending video content for QA dialogue.
Ranked #8 on
Highlight Detection
on QVHighlights
Enhancing DP-SGD through Non-monotonous Adaptive Scaling Gradient Weight
In this paper, we introduce an enhanced version of DP-SGD, named Differentially Private Per-sample Adaptive Scaling Clipping (DP-PSASC).
Gradient-Guided Conditional Diffusion Models for Private Image Reconstruction: Analyzing Adversarial Impacts of Differential Privacy and Denoising
We investigate the construction of gradient-guided conditional diffusion models for reconstructing private images, focusing on the adversarial interplay between differential privacy noise and the denoising capabilities of diffusion models.
Navigating the Unknown: A Chat-Based Collaborative Interface for Personalized Exploratory Tasks
The rise of large language models (LLMs) has revolutionized user interactions with knowledge-based systems, enabling chatbots to synthesize vast amounts of information and assist with complex, exploratory tasks.
Redefining <Creative> in Dictionary: Towards an Enhanced Semantic Understanding of Creative Generation
``Creative'' remains an inherently abstract concept for both humans and diffusion models.
Optimal Hardening Strategy for Electricity-Hydrogen Networks with Hydrogen Leakage Risk Control against Extreme Weather
However, for electricity-hydrogen distribution networks (EHDNs), the leakage risk of hydrogen should be controlled to avoid severe incidents such as explosions.
Unlabeled Action Quality Assessment Based on Multi-dimensional Adaptive Constrained Dynamic Time Warping
The adaptive constraint scheme enhances the discriminability of action quality assessment by approximately 30%.
Flexible Operation of Electricity-HCNG Networks with Variable Hydrogen Fraction: A Distributionally Robust Joint Chance-Constrained Approach
Hydrogen-enriched compressed natural gas (HCNG) is a promising way to utilize surplus renewable energy through hydrogen electrolysis and blending it into natural gas.
Boosting Open-Vocabulary Object Detection by Handling Background Samples
This limitation results in suboptimal performance for open-vocabulary detectors that rely on CLIP when processing background samples.
GraphIC: A Graph-Based In-Context Example Retrieval Model for Multi-Step Reasoning
However, the effectiveness of ICL heavily relies on the selection of ICEs, and conventional text-based embedding methods are often inadequate for tasks that require multi-step reasoning, such as mathematical and logical problem solving.
First Place Solution to the Multiple-choice Video QA Track of The Second Perception Test Challenge
In this report, we present our first-place solution to the Multiple-choice Video Question Answering (QA) track of The Second Perception Test Challenge.
Exploring RAG-based Vulnerability Augmentation with LLMs
More specifically, we explore three strategies to augment both single and multi-statement vulnerabilities, with LLMs, namely Mutation, Injection, and Extension.
Ranked #1 on
Vulnerability Detection
on VulScribeR
(using extra training data)
Collaborative Evolving Strategy for Automatic Data-Centric Development
By leveraging the strong complex problem-solving capabilities of large language models (LLMs), we propose an LLM-based autonomous agent, equipped with a strategy named Collaborative Knowledge-STudying-Enhanced Evolution by Retrieval (Co-STEER), to simultaneously address all the challenges.
Generalized Averaging Method for Power Electronics Modeling from DC to above Half the Switching Frequency
Modeling power electronic converters at frequencies close to or above half the switching frequency has been difficult due to the time-variant and discontinuous switching actions.
Zero-Shot Long-Form Video Understanding through Screenplay
The Long-form Video Question-Answering task requires the comprehension and analysis of extended video content to respond accurately to questions by utilizing both temporal and contextual information.
SimClone: Detecting Tabular Data Clones using Value Similarity
In this paper, we propose a novel method called SimClone for data clone detection in tabular datasets without relying on structural information.
Machine Unlearning with Minimal Gradient Dependence for High Unlearning Ratios
This lightweight, scalable method significantly enhances model accuracy and strengthens resistance to membership inference attacks.
LIVE: Learnable In-Context Vector for Visual Question Answering
However, applying ICL usually faces two major challenges: 1) using more ICDs will largely increase the inference time and 2) the performance is sensitive to the selection of ICDs.
Free Performance Gain from Mixing Multiple Partially Labeled Samples in Multi-label Image Classification
However, the powerful Mixup sample-mixing data augmentation cannot be well utilized to address this challenge, as it cannot perform linear interpolation on the unknown labels to construct augmented samples.
Unlearning Concepts in Diffusion Model via Concept Domain Correction and Concept Preserving Gradient
Text-to-image diffusion models have achieved remarkable success in generating photorealistic images.
Exploring the Distinctiveness and Fidelity of the Descriptions Generated by Large Vision-Language Models
Large Vision-Language Models (LVLMs) are gaining traction for their remarkable ability to process and integrate visual and textual data.
Exploring Learngene via Stage-wise Weight Sharing for Initializing Variable-sized Models
When initializing variable-sized models adapting for different resource constraints, SWS achieves better results while reducing around 20x parameters stored to initialize these models and around 10x pre-training costs, in contrast to the pre-training and fine-tuning approach.
FedMPQ: Secure and Communication-Efficient Federated Learning with Multi-codebook Product Quantization
In federated learning, particularly in cross-device scenarios, secure aggregation has recently gained popularity as it effectively defends against inference attacks by malicious aggregators.
Towards Data-Centric Automatic R&D
We appeal to future work to take developing techniques for tackling automatic R&D into consideration, thus bringing the opportunities of the potential revolutionary upgrade to human productivity.
Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-On
Second, we propose a novel diffusion-based method that predicts a precise inpainting mask based on the person and reference garment images, further enhancing the reliability of the try-on results.
DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning
In this paper, we propose a novel Dynamic Affinity-based Personalized Federated Learning model (DA-PFL) to alleviate the class imbalanced problem during federated learning.
FedHCDR: Federated Cross-Domain Recommendation with Hypergraph Signal Decoupling
Specifically, to address the data heterogeneity across domains, we introduce an approach called hypergraph signal decoupling (HSD) to decouple the user features into domain-exclusive and domain-shared features.
Analysis of the Two-Step Heterogeneous Transfer Learning for Laryngeal Blood Vessel Classification: Issue and Improvement
Accurate classification of laryngeal vascular as benign or malignant is crucial for early detection of laryngeal cancer.
MemoNav: Working Memory Model for Visual Navigation
Subsequently, a graph attention module encodes the retained STM and the LTM to generate working memory (WM) which contains the scene features essential for efficient navigation.
A Lightweight Inception Boosted U-Net Neural Network for Routability Prediction
As the modern CPU, GPU, and NPU chip design complexity and transistor counts keep increasing, and with the relentless shrinking of semiconductor technology nodes to nearly 1 nanometer, the placement and routing have gradually become the two most pivotal processes in modern very-large-scale-integrated (VLSI) circuit back-end design.
Do You Guys Want to Dance: Zero-Shot Compositional Human Dance Generation with Multiple Persons
Human dance generation (HDG) aims to synthesize realistic videos from images and sequences of driving poses.
A Versatile Framework for Continual Test-Time Domain Adaptation: Balancing Discriminability and Generalizability
Such bias exacerbates catastrophic forgetting and diminishes the generalization ability to future domains.
Long-Tail Class Incremental Learning via Independent Sub-prototype Construction
In this paper we constructed two parallel spaces simultaneously: 1) Sub-prototype space and 2) Reminiscence space to learn robust representations while alleviating forgetfulness.
Unveiling the Unknown: Unleashing the Power of Unknown to Known in Open-Set Source-Free Domain Adaptation
In contrast this paper advocates that exploring unknown classes can better identify known ones and proposes a domain adaptation model to transfer knowledge on known and unknown classes jointly.
Lever LM: Configuring In-Context Sequence to Lever Large Vision Language Models
As Archimedes famously said, ``Give me a lever long enough and a fulcrum on which to place it, and I shall move the world'', in this study, we propose to use a tiny Language Model (LM), \eg, a Transformer with 67M parameters, to lever much larger Vision-Language Models (LVLMs) with 9B parameters.
Building Variable-sized Models via Learngene Pool
To overcome these challenges, motivated by the recently proposed Learngene framework, we propose a novel method called Learngene Pool.
Transformer as Linear Expansion of Learngene
Under the situation where we need to produce models of varying depths adapting for different resource constraints, TLEG achieves comparable results while reducing around 19x parameters stored to initialize these models and around 5x pre-training costs, in contrast to the pre-training and fine-tuning approach.
How to Configure Good In-Context Sequence for Visual Question Answering
Inspired by the success of Large Language Models in dealing with new tasks via In-Context Learning (ICL) in NLP, researchers have also developed Large Vision-Language Models (LVLMs) with ICL capabilities.
Manipulating the Label Space for In-Context Classification
Specifically, using our approach on ImageNet, we increase accuracy from 74. 70\% in a 4-shot setting to 76. 21\% with just 2 shots.
ChartLlama: A Multimodal LLM for Chart Understanding and Generation
Next, we introduce ChartLlama, a multi-modal large language model that we've trained using our created dataset.
Category-Wise Fine-Tuning for Image Multi-label Classification with Partial Labels
A single model submitted to the competition server for the official evaluation achieves mAUC 91. 82% on the test set, which is the highest single model score in the leaderboard and literature.
Ranked #1 on
Multi-Label Classification
on CheXpert
Rethinking Residual Connection in Training Large-Scale Spiking Neural Networks
Spiking Neural Network (SNN) is known as the most famous brain-inspired model, but the non-differentiable spiking mechanism makes it hard to train large-scale SNNs.
Rethinking Evaluation Metrics of Open-Vocabulary Segmentaion
We benchmarked the proposed evaluation metrics on 12 open-vocabulary methods of three segmentation tasks.
Leveraging Large Language Model for Automatic Evolving of Industrial Data-Centric R&D Cycle
In the wake of relentless digital transformation, data-driven solutions are emerging as powerful tools to address multifarious industrial tasks such as forecasting, anomaly detection, planning, and even complex decision-making.
SeisT: A foundational deep learning model for earthquake monitoring tasks
The most significant improvements, in comparison to existing models, are observed in phase-P picking, phase-S picking, and magnitude estimation, with gains of 1. 7%, 9. 5%, and 8. 0%, respectively.
FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation Learning
Nonetheless, the sequence feature heterogeneity across different domains significantly impacts the overall performance of FL.
Temporal Difference Learning for High-Dimensional PIDEs with Jumps
In this paper, we propose a deep learning framework for solving high-dimensional partial integro-differential equations (PIDEs) based on the temporal difference learning.
Genes in Intelligent Agents
Inspired by the biological intelligence, artificial intelligence (AI) has devoted to building the machine intelligence.
Exploring Diverse In-Context Configurations for Image Captioning
After discovering that Language Models (LMs) can be good in-context few-shot learners, numerous strategies have been proposed to optimize in-context sequence configurations.
Learngene: Inheriting Condensed Knowledge from the Ancestry Model to Descendant Models
(i) Accumulating: the knowledge is accumulated during the continuous learning of an ancestry model.
SC-ML: Self-supervised Counterfactual Metric Learning for Debiased Visual Question Answering
Unfortunately, language bias is a common problem in VQA, which refers to the model generating answers only by associating with the questions while ignoring the visual content, resulting in biased results.
Spatial Attention and Syntax Rule Enhanced Tree Decoder for Offine Handwritten Mathematical Expression Recognition
In this paper, we propose a novel model called Spatial Attention and Syntax Rule Enhanced Tree Decoder (SS-TD), which is equipped with spatial attention mechanism to alleviate the prediction error of tree structure and use syntax masks (obtained from the transformation of syntax rules) to constrain the occurrence of ungrammatical mathematical expression.
Learning Trajectory-Word Alignments for Video-Language Tasks
To amend this, we propose a novel TW-BERT to learn Trajectory-Word alignment by a newly designed trajectory-to-word (T2W) attention for solving video-language tasks.
Adaptively Clustering Neighbor Elements for Image-Text Generation
In this gradual clustering process, a parsing tree is generated which embeds the hierarchical knowledge of the input sequence.
Spikeformer: A Novel Architecture for Training High-Performance Low-Latency Spiking Neural Network
Spiking neural networks (SNNs) have made great progress on both performance and efficiency over the last few years, but their unique working pattern makes it hard to train a high-performance low-latency SNN. Thus the development of SNNs still lags behind traditional artificial neural networks (ANNs). To compensate this gap, many extraordinary works have been proposed. Nevertheless, these works are mainly based on the same kind of network structure (i. e. CNN) and their performance is worse than their ANN counterparts, which limits the applications of SNNs. To this end, we propose a novel Transformer-based SNN, termed "Spikeformer", which outperforms its ANN counterpart on both static dataset and neuromorphic dataset and may be an alternative architecture to CNN for training high-performance SNNs. First, to deal with the problem of "data hungry" and the unstable training period exhibited in the vanilla model, we design the Convolutional Tokenizer (CT) module, which improves the accuracy of the original model on DVS-Gesture by more than 16%. Besides, in order to better incorporate the attention mechanism inside Transformer and the spatio-temporal information inherent to SNN, we adopt spatio-temporal attention (STA) instead of spatial-wise or temporal-wise attention. With our proposed method, we achieve competitive or state-of-the-art (SOTA) SNN performance on DVS-CIFAR10, DVS-Gesture, and ImageNet datasets with the least simulation time steps (i. e. low latency). Remarkably, our Spikeformer outperforms other SNNs on ImageNet by a large margin (i. e. more than 5%) and even outperforms its ANN counterpart by 3. 1% and 2. 2% on DVS-Gesture and ImageNet respectively, indicating that Spikeformer is a promising architecture for training large-scale SNNs and may be more suitable for SNNs compared to CNN. We believe that this work shall keep the development of SNNs in step with ANNs as much as possible. Code will be available.
Image Projective Transformation Rectification with Synthetic Data for Smartphone-captured Chest X-ray Photos Classification
To the best of our knowledge, it is the first work to predict the projective transformation matrix as the learning goal for photo rectification.
Ranked #1 on
Medical Image Classification
on CheXphoto
Learning to Collocate Visual-Linguistic Neural Modules for Image Captioning
This is because the language is only partially observable, for which we need to dynamically collocate the modules during the process of image captioning.
MemoNav: Selecting Informative Memories for Visual Navigation
To address this limitation, we present the MemoNav, a novel memory mechanism for image-goal navigation, which retains the agent's informative short-term memory and long-term memory to improve the navigation performance on a multi-goal task.
Automatically Discovering Novel Visual Categories with Self-supervised Prototype Learning
In the first stage, we obtain a robust feature extractor, which could serve for all images with base and novel categories.
Siamese Contrastive Embedding Network for Compositional Zero-Shot Learning
Some methods recognize state and object with two trained classifiers, ignoring the impact of the interaction between object and state; the other methods try to learn the joint representation of the state-object compositions, leading to the domain gap between seen and unseen composition sets.
iExam: A Novel Online Exam Monitoring and Analysis System Based on Face Detection and Recognition
In this paper, we propose iExam, an intelligent online exam monitoring and analysis system that can not only use face detection to assist invigilators in real-time student identification, but also be able to detect common abnormal behaviors (including face disappearing, rotating faces, and replacing with a different person during the exams) via a face recognition-based post-exam video analysis.
Unseen Object Instance Segmentation with Fully Test-time RGB-D Embeddings Adaptation
In this paper, we emphasize the adaptation process across sim2real domains and model it as a learning problem on the BatchNorm parameters of a simulation-trained model.
Weakly Aligned Feature Fusion for Multimodal Object Detection
In this article, we propose a general multimodal detector named aligned region CNN (AR-CNN) to tackle the position shift problem.
Show, Deconfound and Tell: Image Captioning With Causal Inference
In the encoding stage, the IOD is able to disentangle the region-based visual features by deconfounding the visual confounder.
Not Just Selection, but Exploration: Online Class-Incremental Continual Learning via Dual View Consistency
Unfortunately, these methods only focus on selecting samples from the memory bank for replay and ignore the adequate exploration of semantic information in the single-pass data stream, leading to poor classification accuracy.
Towards End-to-End Image Compression and Analysis with Transformers
Meanwhile, we propose a feature aggregation module to fuse the compressed features with the selected intermediate features of the Transformer, and feed the aggregated features to a deconvolutional neural network for image reconstruction.
Auto-Encoding Score Distribution Regression for Action Quality Assessment
Traditionally, AQA is treated as a regression problem to learn the underlying mappings between videos and action scores.
Ranked #1 on
Action Quality Assessment
on AQA-7
EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding Matching
The datasets will be released to facilitate the development of video captioning metrics.
Sliding Sequential CVAE with Time Variant Socially-aware Rethinking for Trajectory Prediction
First, with the continuation of time, the prediction error at each time step increases significantly, causing the final displacement error to be impossible to ignore.
Can AI detect pain and express pain empathy? A review from emotion recognition and a human-centered AI perspective
Furthermore, we discuss challenges for responsible evaluation of cognitive methods and computational techniques and show approaches to future work to contribute to affective assistants capable of empathy.
Text-Driven Image Manipulation via Semantic-Aware Knowledge Transfer
And on this basis, a novel attribute transfer method, named semantic directional decomposition network (SDD-Net), is proposed to achieve semantic-level facial attribute transfer by latent semantic direction decomposition, improving the interpretability and editability of our method.
Open Set Domain Adaptation with Zero-shot Learning on Graph
Thus the classification ability of the source domain is transferred to the target domain and the model can distinguish the unknown classes with prior knowledge.
Auto-Parsing Network for Image Captioning and Visual Question Answering
We propose an Auto-Parsing Network (APN) to discover and exploit the input data's hidden tree structures for improving the effectiveness of the Transformer-based vision-language systems.
Towards Unbiased Visual Emotion Recognition via Causal Intervention
In this work, we scrutinize this problem from the perspective of causal inference, where such dataset characteristic is termed as a confounder which misleads the system to learn the spurious correlation.
Nearest Neighbor Matching for Deep Clustering
Specifically, for the local level, we match the nearest neighbors based on batch embedded features, as for the global one, we match neighbors from overall embedded features.
SelfSAGCN: Self-Supervised Semantic Alignment for Graph Convolution Network
Specifically, the Identity Aggregation is applied to extract semantic features from labeled nodes, the Semantic Alignment is utilized to align node features obtained from different aspects using the class central similarity.
Doubly Contrastive Deep Clustering
In this paper, we present a novel Doubly Contrastive Deep Clustering (DCDC) framework, which constructs contrastive loss over both sample and class views to obtain more discriminative features and competitive results.
Causal Attention for Vision-Language Tasks
Specifically, CATT is implemented as a combination of 1) In-Sample Attention (IS-ATT) and 2) Cross-Sample Attention (CS-ATT), where the latter forcibly brings other samples into every IS-ATT, mimicking the causal intervention.
A Distributed Implementation of Steady-State Kalman Filter
This paper studies the distributed state estimation in sensor network, where $m$ sensors are deployed to infer the $n$-dimensional state of a linear time-invariant (LTI) Gaussian system.
Incremental Embedding Learning via Zero-Shot Translation
Different from traditional incremental classification networks, the semantic gap between the embedding spaces of two adjacent tasks is the main challenge for embedding networks under incremental learning setting.
Adversarial Learning for Robust Deep Clustering
Meanwhile, we devise an adversarial attack strategy to explore samples that easily fool the clustering layers but do not impact the performance of the deep embedding.
Cloud Cover and Aurora Contamination at Dome A in 2017 from KLCAM
Dome A in Antarctica has many characteristics that make it an excellent site for astronomical observations, from the optical to the terahertz.
Instrumentation and Methods for Astrophysics
Finding It at Another Side: A Viewpoint-Adapted Matching Encoder for Change Captioning
In this paper, we propose a novel visual encoder to explicitly distinguish viewpoint changes from semantic changes in the change captioning task.
Learning to Scan: A Deep Reinforcement Learning Approach for Personalized Scanning in CT Imaging
It is more desirable to design a personalized scanning strategy for each subject to obtain better reconstruction result.
Deconfounded Image Captioning: A Causal Retrospect
Dataset bias in vision-language tasks is becoming one of the main problems which hinders the progress of our community.
Classical limit for the varying-mass Schrödinger equation with random inhomogeneities
The varying-mass Schr\"odinger equation (VMSE) has been successfully applied to model electronic properties of semiconductor hetero-structures, for example, quantum dots and quantum wells.
Numerical Analysis Numerical Analysis
Automated Pavement Crack Segmentation Using U-Net-based Convolutional Neural Network
In this paper, we propose a deep learning technique based on a convolutional neural network to perform segmentation tasks on pavement crack images.
TBC-Net: A real-time detector for infrared small target detection using semantic constraint
Meanwhile, we propose a joint loss function and a training method.
mu-Forcing: Training Variational Recurrent Autoencoders for Text Generation
It has been previously observed that training Variational Recurrent Autoencoders (VRAE) for text generation suffers from serious uninformative latent variables problem.
Deep Spectral Clustering using Dual Autoencoder Network
In this paper, we propose a joint learning framework for discriminative embedding and spectral clustering.
Learning to Collocate Neural Modules for Image Captioning
To this end, we make the following technical contributions for CNM training: 1) compact module design --- one for function words and three for visual content words (eg, noun, adjective, and verb), 2) soft module fusion and multi-step module execution, robustifying the visual reasoning in partial observation, 3) a linguistic loss for module controller being faithful to part-of-speech collocations (eg, adjective is before noun).
Unpaired Image Captioning via Scene Graph Alignments
Most of current image captioning models heavily rely on paired image-caption datasets.
Weakly Aligned Cross-Modal Learning for Multispectral Pedestrian Detection
In this paper, we propose a novel Aligned Region CNN (AR-CNN) to handle the weakly aligned multispectral data in an end-to-end way.
Ranked #5 on
2D Object Detection
on DroneVehicle
(Val/mAP50 metric)
Auto-Encoding Scene Graphs for Image Captioning
We propose Scene Graph Auto-Encoder (SGAE) that incorporates the language inductive bias into the encoder-decoder image captioning framework for more human-like captions.
Shuffle-Then-Assemble: Learning Object-Agnostic Visual Relationship Features
By "agnostic", we mean that the feature is less likely biased to the classes of paired objects.
Face Photo Sketch Synthesis via Larger Patch and Multiresolution Spline
In order to get a smoother sketch, we propose a new method to reduce such jagged parts and mottled points.
A Weighted Common Subgraph Matching Algorithm
We propose a weighted common subgraph (WCS) matching algorithm to find the most similar subgraphs in two labeled weighted graphs.
