Search Results for author:
Found 140 papers, 69 papers with code
Matching-oriented Embedding Quantization For Ad-hoc Retrieval
In this work, we propose the Matching-oriented Product Quantization (MoPQ), where a novel objective Multinoulli Contrastive Loss (MCL) is formulated.
Leveraging Bidding Graphs for Advertiser-Aware Relevance Modeling in Sponsored Search
As the fundamental basis of sponsored search, relevance modeling has attracted increasing attention due to the tremendous practical value.
Towards Generalizeable Semantic Product Search by Text Similarity Pre-training on Search Click Logs
Recently, semantic search has been successfully applied to E-commerce product search and the learned semantic space for query and product encoding are expected to generalize well to unseen queries or products.
STI-Bench: Are MLLMs Ready for Precise Spatial-Temporal World Understanding?
The use of Multimodal Large Language Models (MLLMs) as an end-to-end solution for Embodied AI and Autonomous Driving has become a prevailing trend.
Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models
In recent years, the rapid development of large reasoning models has resulted in the saturation of existing benchmarks for evaluating mathematical reasoning, highlighting the urgent need for more challenging and rigorous evaluation frameworks.
Video-XL-Pro: Reconstructive Token Compression for Extremely Long Video Understanding
Despite advanced token compression techniques, existing multimodal large language models (MLLMs) still struggle with hour-long video understanding.
HeGMN: Heterogeneous Graph Matching Network for Learning Graph Similarity
Graph similarity learning (GSL), also referred to as graph matching in many scenarios, is a fundamental problem in computer vision, pattern recognition, and graph learning.
An Empirical Study on Eliciting and Improving R1-like Reasoning Models
This approach achieves a remarkable accuracy of 86. 67% with greedy search on AIME 2024, underscoring its effectiveness in enhancing model capabilities.
Improving the Transferability of Adversarial Examples by Inverse Knowledge Distillation
In recent years, the rapid development of deep neural networks has brought increased attention to the security and robustness of these models.
MMTEB: Massive Multilingual Text Embedding Benchmark
MMTEB includes a diverse set of challenging, novel tasks such as instruction following, long-document retrieval, and code retrieval, representing the largest multilingual collection of evaluation tasks for embedding models to date.
HawkBench: Investigating Resilience of RAG Methods on Stratified Information-Seeking Tasks
In real-world information-seeking scenarios, users have dynamic and diverse needs, requiring RAG systems to demonstrate adaptable resilience.
MomentSeeker: A Comprehensive Benchmark and A Strong Baseline For Moment Retrieval Within Long Videos
In this work, we present MomentSeeker, a comprehensive benchmark to evaluate retrieval models' performance in handling general long-video moment retrieval (LVMR) tasks.
Any Information Is Just Worth One Single Screenshot: Unifying Search With Visualized Information Retrieval
With the popularity of multimodal techniques, it receives growing interests to acquire useful information in visual forms.
Does RAG Really Perform Bad For Long-Context Processing?
The efficient processing of long context poses a serious challenge for large language models (LLMs).
Reinforced Information Retrieval
Specifically, the generator is reinforced to generate query augmentations that enhance the retriever's performance, while the retriever is trained to better discriminate the relevant documents identified by the generator.
O1 Embedder: Let Retrievers Think Before Action
Inspired by this progress, we aim to develop similar capabilities for retrieval models, which hold great promise for tackling critical challenges in the field, including multi-task retrieval, zero-shot retrieval, and tasks requiring intensive reasoning of complex relationships.
Matryoshka Re-Ranker: A Flexible Re-Ranking Architecture With Configurable Depth and Width
Consequently, the LLM-based re-rankers can be made applicable across various real-world situations.
Virgo: A Preliminary Exploration on Reproducing o1-like MLLM
Moreover, it seems that such textual reasoning data can be even more effective than visual reasoning data in eliciting the slow-thinking capacities of MLLMs.
GIMS: Image Matching System Based on Adaptive Graph Construction and Graph Neural Network
Feature-based image matching has extensive applications in computer vision.
MegaPairs: Massive Data Synthesis For Universal Multimodal Retrieval
Despite the rapidly growing demand for multimodal retrieval, progress in this field remains severely constrained by a lack of training data.
Ranked #1 on
Zero-Shot Composed Image Retrieval (ZS-CIR)
on CIRCO
Boosting Long-Context Management via Query-Guided Activation Refilling
ACRE constructs a Bi-layer KV Cache for long contexts, where the layer-1 (L1) cache compactly captures global information, and the layer-2 (L2) cache provides detailed and localized information.
AIR-Bench: Automated Heterogeneous Information Retrieval Benchmark
The testing data in AIR-Bench is generated with respect to diverse tasks, domains and languages.
Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems
We introduce an ``imitate, explore, and self-improve'' framework, denoted as \textbf{STILL-2}, as our primary technical approach to train the reasoning model.
Enhancing LLM Reasoning with Reward-guided Tree Search
This framework is implemented by integrating the policy model, reward model, and search algorithm.
Physics-informed Machine Learning for Battery Pack Thermal Management
Battery thermal management systems can effectively control the temperature of batteries; therefore, the performance and safety can be ensured.
AssistRAG: Boosting the Potential of Large Language Models with an Intelligent Information Assistant
The emergence of Large Language Models (LLMs) has significantly advanced natural language processing, but these models often generate factually incorrect information, known as "hallucination".
SEGMN: A Structure-Enhanced Graph Matching Network for Graph Similarity Learning
Equipped with a dual embedding learning module and a structure perception matching module, SEGMN achieves structure enhancement in both embedding learning and cross-graph matching.
AutoMIR: Effective Zero-Shot Medical Information Retrieval without Relevance Labels
By benchmarking ten models on CMIRB, we establish a rigorous standard for evaluating medical information retrieval systems.
Feint and Attack: Attention-Based Strategies for Jailbreaking and Protecting LLMs
Therefore, both ABA and ABD can be used to access the security of the LLMs.
Elephant in the Room: Unveiling the Impact of Reward Model Quality in Alignment
Despite the vital role reward models play in alignment, previous works have consistently overlooked their performance and used off-the-shelf reward models arbitrarily without verification, rendering the reward model ``\emph{an elephant in the room}''.
Making Text Embedders Few-Shot Learners
To this end, we introduce a novel model bge-en-icl, which employs few-shot examples to produce high-quality text embeddings.
Lighter And Better: Towards Flexible Context Adaptation For Retrieval Augmented Generation
In this approach, the retrieved contexts are compressed into compact embeddings before being encoded by the LLMs.
Video-XL: Extra-Long Vision Language Model for Hour-Scale Video Understanding
The VST module is trained by instruction fine-tuning, where two optimizing strategies are offered.
Multiple-Exit Tuning: Towards Inference-Efficient Adaptation for Vision Transformer
As the performances of linear classifiers are influenced by the relationship among samples, we employ graph regularization to improve the representations fed into the classifiers at early exits.
OmniGen: Unified Image Generation
In this work, we introduce OmniGen, a new diffusion model for unified image generation.
Trustworthiness in Retrieval-Augmented Generation Systems: A Survey
Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the development of Large Language Models (LLMs).
MemoRAG: Moving towards Next-Gen RAG Via Memory-Inspired Knowledge Discovery
Retrieval-Augmented Generation (RAG) leverages retrieval tools to access external databases, thereby enhancing the generation quality of large language models (LLMs) through optimized context.
FAST-LIVO2: Fast, Direct LiDAR-Inertial-Visual Odometry
The fusion of both visual and LiDAR measurements is based on a single unified voxel map where the LiDAR module constructs the geometric structure for registering new LiDAR scans and the visual module attaches image patches to the LiDAR points.
Large Language Models as Foundations for Next-Gen Dense Retrieval: A Comprehensive Empirical Assessment
In this work, we conduct a comprehensive empirical study on a wide range of retrieval tasks, including in domain accuracy, data efficiency, zero shot generalization, lengthy retrieval, instruction based retrieval, and multi task learning.
SynthVLM: High-Efficiency and High-Quality Synthetic Data for Vision Language Models
Crucially, our method's reliance on purely generated data ensures the preservation of privacy, achieving SoTA performance with just 100k data points (only 18% of the official dataset size).
Unveiling the Ignorance of MLLMs: Seeing Clearly, Answering Incorrectly
To this end, we manually construct a benchmark with 12 categories and design evaluation metrics that assess the degree of error in MLLM responses even when the visual content is seemingly understood.
VISTA: Visualized Text Embedding For Universal Multi-Modal Retrieval
Thirdly, we introduce a multi-stage training algorithm, which first aligns the visual token embedding with the text encoder using massive weakly labeled data, and then develops multi-modal representation capability using the generated composed image-text data.
Ranked #8 on
Image Retrieval
on CIRR
MLVU: Benchmarking Multi-task Long Video Understanding
To address the above problems, we propose a new benchmark called MLVU (Multi-task Long Video Understanding Benchmark) for the comprehensive and in-depth evaluation of LVU.
Compressing Lengthy Context With UltraGist
Compressing lengthy context is a critical but technically challenging problem.
Are Long-LLMs A Necessity For Long-Context Tasks?
The learning and deployment of long-LLMs remains a challenging problem despite recent progresses.
MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels
Recent breakthroughs in large models have highlighted the critical significance of data scale, labels and modals.
Extending Llama-3's Context Ten-Fold Overnight
We extend the context length of Llama-3-8B-Instruct from 8K to 80K via QLoRA fine-tuning.
Understanding Privacy Risks of Embeddings Induced by Large Language Models
Large language models (LLMs) show early signs of artificial general intelligence but struggle with hallucinations.
ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval
Conversational search requires accurate interpretation of user intent from complex multi-turn contexts.
FineFake: A Knowledge-Enriched Dataset for Fine-Grained Multi-Domain Fake News Detection
Existing benchmarks for fake news detection have significantly contributed to the advancement of models in assessing the authenticity of news content.
Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with Objects
A holistic 3Dunderstanding of such interactions from egocentric views is important for tasks in robotics, AR/VR, action recognition and motion generation.
Negating Negatives: Alignment with Human Negative Samples via Distributional Dispreference Optimization
Large language models (LLMs) have revolutionized the role of AI, yet pose potential social risks.
Extensible Embedding: A Flexible Multipler For LLM's Context Length
2) Strong sample efficiency of training, which enables the embedding model to be learned in a cost-effective way.
BGE Landmark Embedding: A Chunking-Free Embedding Method For Retrieval Augmented Long-Context Large Language Models
In this work, we proposeExtensible Embedding, which realizes high-quality extension of LLM's context with strong flexibility and cost-effectiveness.
Metacognitive Retrieval-Augmented Large Language Models
Drawing from cognitive psychology, metacognition allows an entity to self-reflect and critically evaluate its cognitive processes.
Grounding Language Model with Chunking-Free In-Context Retrieval
These strategies not only improve the efficiency of the retrieval process but also ensure that the fidelity of the generated grounding text evidence is maintained.
BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation
It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval, which provides a unified model foundation for real-world IR applications.
Flexibly Scaling Large Language Models Contexts Through Extensible Tokenization
Extensible Tokenization stands as a midware in between of the tokenized context and the LLM, which transforms the raw token embeddings into the extensible embeddings.
INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning
Despite this, their application to information retrieval (IR) tasks is still challenging due to the infrequent occurrence of many IR-specific concepts in natural language.
Long Context Compression with Activation Beacon
In this paper, we propose Activation Beacon, a plug-in module for transformer-based LLMs that targets effective, efficient, and flexible compression of long contexts.
Making Large Language Models A Better Foundation For Dense Retrieval
LLaRA consists of two pretext tasks: EBAE (Embedding-Based Auto-Encoding) and EBAR (Embedding-Based Auto-Regression), where the text embeddings from LLM are used to reconstruct the tokens for the input sentence and predict the tokens for the next sentence, respectively.
LM-Cocktail: Resilient Tuning of Language Models via Model Merging
Despite simplicity, LM-Cocktail is surprisingly effective: the resulted model is able to achieve a strong empirical performance in the whole scope of general tasks while preserving a superior capacity in its targeted domain.
A Diffusion Model Based Quality Enhancement Method for HEVC Compressed Video
Most of the existing methods need to train corresponding models for compressed videos with different quantization parameters to improve the quality of compressed videos.
Retrieve Anything To Augment Large Language Models
On the other hand, the task-specific retrievers lack the required versatility, hindering their performance across the diverse retrieval augmentation scenarios.
C-Pack: Packed Resources For General Chinese Embeddings
Along with our resources on general Chinese embedding, we release our data and models for English text embeddings.
A Consumer-tier based Visual-Brain Machine Interface for Augmented Reality Glasses Interactions
Here we develop a consumer-tier Visual-Brain Machine Inteface(V-BMI) system specialized for Augmented Reality(AR) glasses interactions.
A Counterfactual Fair Model for Longitudinal Electronic Health Records via Deconfounder
The fairness issue of clinical data modeling, especially on Electronic Health Records (EHRs), is of utmost importance due to EHR's complex latent structure and potential selection bias.
Large Language Models for Information Retrieval: A Survey
This evolution requires a combination of both traditional methods (such as term-based sparse retrieval methods with rapid response) and modern neural architectures (such as language models with powerful language understanding capacity).
When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities
The advent of large language models marks a revolutionary breakthrough in artificial intelligence.
Multimodal Object Detection by Channel Switching and Spatial Attention
Multimodal object detection has attracted great attention in recent years since the information specific to different modalities can complement each other and effectively improve the accuracy and stability of the detection model.
Ranked #7 on
Pedestrian Detection
on LLVIP
Generative Retrieval via Term Set Generation
On top of the term-set DocID, we propose a permutation-invariant decoding algorithm, with which the term set can be generated in any permutation yet will always lead to the corresponding document.
RetroMAE-2: Duplex Masked Auto-Encoder For Pre-Training Retrieval-Oriented Language Models
It is designed to improve the quality of semantic representation where all contextualized embeddings of the pre-trained model can be leveraged.
An Efficient Plane Extraction Approach for Bundle Adjustment on LiDAR Point clouds
However, the accuracy and speed of LiDAR bundle adjustment depend on the quality of plane extraction, which provides point association for LiDAR BA.
Constructing Tree-based Index for Efficient and Effective Dense Retrieval
Unfortunately, while ANN can improve the efficiency of DR models, it usually comes with a significant price on retrieval performance.
Transformer-based models and hardware acceleration analysis in autonomous driving: A survey
The paper also highlights the challenges, trends, and current insights in Transformer-based models, addressing their hardware deployment and acceleration issues within the context of long-term autonomous driving applications.
WebBrain: Learning to Generate Factually Correct Articles for Queries by Grounding on Large Web Corpus
In this paper, we introduce a new NLP task -- generating short factual articles with references for queries by mining supporting evidence from the Web.
Structural Imbalance Aware Graph Augmentation Learning
Firstly, a Pagerank-based sampling strategy is designed to identify hub nodes and tail nodes in the graph.
Time-aware Hyperbolic Graph Attention Network for Session-based Recommendation
TA-HGAT is built in a hyperbolic space to learn the hierarchical structure of session graphs.
Document Image Shadow Removal Guided by Color-Aware Background
In this paper, we present a color-aware background extraction network (CBENet) for extracting a spatially varying background image that accurately depicts the background colors of the document.
CDSM: Cascaded Deep Semantic Matching on Textual Graphs Leveraging Ad-hoc Neighbor Selection
In the first stage, a lightweight CNN-based ad-hod neighbor selector is deployed to filter useful neighbors for the matching task with a small computation cost.
Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders
Considering the influence of historical purchases on users' future interests, the user and item representations can be viewed as unobserved confounders in the causal diagram.
RetroMAE v2: Duplex Masked Auto-Encoder For Pre-Training Retrieval-Oriented Language Models
DupMAE, which targets on improving the semantic representation capacity for the contextualized embeddings of both [CLS] and ordinary tokens.
Ranked #1 on
Information Retrieval
on MS MARCO
(MRR@10 metric)
Mitigating Health Disparities in EHR via Deconfounder
First, these methods usually mean a trade-off between the model's performance and fairness.
M3FGM:a node masking and multi-granularity message passing-based federated graph model for spatial-temporal data prediction
This paper proposes a new GNN-oriented split federated learning method, named node {\bfseries M}asking and {\bfseries M}ulti-granularity {\bfseries M}essage passing-based Federated Graph Model (M$^3$FGM) for the above issues.
Hybrid Inverted Index Is a Robust Accelerator for Dense Retrieval
Based on comprehensive experiments on popular retrieval benchmarks, we verify that clusters and terms indeed complement each other, enabling HI$^2$ to achieve lossless retrieval quality with competitive efficiency across various index settings.
Pre-training for Information Retrieval: Are Hyperlinks Fully Explored?
Hyperlinks, which are commonly used in Web pages, have been leveraged for designing pre-training objectives.
PCDNF: Revisiting Learning-based Point Cloud Denoising via Joint Normal Filtering
Motivated by the essential interplay between point cloud denoising and normal filtering, we revisit point cloud denoising from a multitask perspective, and propose an end-to-end network, named PCDNF, to denoise point clouds via joint normal filtering.
Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding
Our experiments on 13 benchmark datasets across five natural language understanding tasks demonstrate the superiority of our method.
Error-Aware Spatial Ensembles for Video Frame Interpolation
However, none of the published VFI works considers the spatially non-uniform characteristics of the interpolation error (IE).
Cooperative Retriever and Ranker in Deep Recommenders
Deep recommender systems (DRS) are intensively applied in modern web services.
Slim-neck by GSConv: A lightweight-design for real-time detector architectures
Furthermore, we provide a design suggestion based on the GSConv, Slim-Neck (SNs), to achieve a higher computational cost-effectiveness of the real-time detectors.
RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder
The sentence embedding is generated from the encoder's masked input; then, the original sentence is recovered based on the sentence embedding and the decoder's masked input via masked language modeling.
Ranked #1 on
Information Retrieval
on MSMARCO
Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential Recommendation
We first extract distribution patterns from the item candidates.
A Novel Underwater Image Enhancement and Improved Underwater Biological Detection Pipeline
For aquaculture resource evaluation and ecological environment monitoring, automatic detection and identification of marine organisms is critical.
Towards Generalizable Semantic Product Search by Text Similarity Pre-training on Search Click Logs
Recently, semantic search has been successfully applied to e-commerce product search and the learned semantic space(s) for query and product encoding are expected to generalize to unseen queries or products.
WANDS: Dataset for Product Search Relevance Assessment
We also show that the dataset is effective in evaluating and discriminating between different search models.
Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense Embeddings
We perform comprehensive explorations for the optimal conduct of knowledge distillation, which may provide useful insights for the learning of VQ based ANN index.
A Mutually Reinforced Framework for Pretrained Sentence Embeddings
Instead of relying on annotation heuristics defined by humans, it leverages the sentence representation model itself and realizes the following iterative self-supervision process: on one hand, the improvement of sentence representation may contribute to the quality of data annotation; on the other hand, more effective data annotation helps to generate high-quality positive samples, which will further improve the current sentence representation model.
Uni-Retriever: Towards Learning The Unified Embedding Based Retriever in Bing Sponsored Search
On the other hand, the capability of making high-CTR retrieval is optimized by learning to discriminate user's clicked ads from the entire corpus.
Reinforcement Routing on Proximity Graph for Efficient Recommendation
Instead of searching the nearest neighbor for the query, we search the item with maximum inner product with query on the proximity graph.
Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval
In this work, we tackle this problem with Bi-Granular Document Representation, where the lightweight sparse embeddings are indexed and standby in memory for coarse-grained candidate search, and the heavyweight dense embeddings are hosted in disk for fine-grained post verification.
GateFormer: Speeding Up News Feed Recommendation with Input Gated Transformers
News feed recommendation is an important web service.
An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering
Thus, graph pre-training has the great potential to alleviate data sparsity in GNN-based recommendations.
Pre-training Recommender Systems via Reinforced Attentive Multi-relational Graph Neural Network
In this paper, we propose a novel Reinforced Attentive Multi-relational Graph Neural Network (RAM-GNN) to the pre-train user and item embeddings on the user and item graph prior to the recommendation step.
Certifiable Artificial Intelligence Through Data Fusion
This paper reviews and proposes concerns in adopting, fielding, and maintaining artificial intelligence (AI) systems.
GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph
The representation learning on textual graph is to generate low-dimensional embeddings for the nodes based on the individual textual features and the neighbourhood information.
Locality Constrained Analysis Dictionary Learning via K-SVD Algorithm
With the learned analysis dictionary, test samples can be transformed into a sparse subspace for classification efficiently.
AdsGNN: Behavior-Graph Augmented Relevance Modeling in Sponsored Search
Our motivation lies in incorporating the tremendous amount of unsupervised user behavior data from the historical search logs as the complementary graph to facilitate relevance modeling.
Hybrid Encoder: Towards Efficient and Precise Native AdsRecommendation via Hybrid Transformer Encoding Networks
For the sake of efficient recommendation, conventional methods would generate user and advertisement embeddings independently with a siamese transformer encoder, such that approximate nearest neighbour search (ANN) can be leveraged.
Matching-oriented Product Quantization For Ad-hoc Retrieval
In this work, we propose the Matching-oriented Product Quantization (MoPQ), where a novel objective Multinoulli Contrastive Loss (MCL) is formulated.
Multi-Interest-Aware User Modeling for Large-Scale Sequential Recommendations
User states in different channels are updated by an \emph{erase-and-add} paradigm with interest- and instance-level attention.
Training Large-Scale News Recommenders with Pretrained Language Models in the Loop
Secondly, it improves the data efficiency of the training workflow, where non-informative data can be eliminated from encoding.
Robust W-GAN-Based Estimation Under Wasserstein Contamination
Robust estimation is an important problem in statistics which aims at providing a reasonable estimator when the data-generating distribution lies within an appropriately defined ball around an uncontaminated distribution.
Heterogeneous Similarity Graph Neural Network on Electronic Health Records
EHRs contain multiple entities and relations and can be viewed as a heterogeneous graph.
Dynamic Graph Collaborative Filtering
Here we propose Dynamic Graph Collaborative Filtering (DGCF), a novel framework leveraging dynamic graphs to capture collaborative and sequential relations of both items and users at the same time.
Van der Waals Heterostructure Pt$_{2}$HgSe$_{3}$/CrI$_3$ for Topological Valleytronics
We identify a valley-polarized Chern insulator in the van der Waals heterostructure, Pt$_{2}$HgSe$_{3}$/CrI$_3$, for potential applications with interplay between electric, magnetic, optical, and mechanical effects.
Band Gap
Materials Science
Shuffle and Learn: Minimizing Mutual Information for Unsupervised Hashing
Proof on $\epsilon$-Convergence of joint probability with approximated derivatives is provided to guarantee the preciseness on update applied on the mutual information.
Sampling-Decomposable Generative Adversarial Recommender
The GAN-style recommenders (i. e., IRGAN) addresses the challenge by learning a generator and a discriminator adversarially, such that the generator produces increasingly difficult samples for the discriminator to accelerate optimizing the discrimination objective.
BALM: Bundle Adjustment for Lidar Mapping
We propose a framework for bundle adjustment (BA) on sparse lidar points and incorporate it to a lidar odometry and mapping (LOAM) to lower the drift.
Robotics
All at Once: Temporally Adaptive Multi-Frame Interpolation with Advanced Motion Modeling
Recent advances in high refresh rate displays as well as the increased interest in high rate of slow motion and frame up-conversion fuel the demand for efficient and cost-effective multi-frame video interpolation solutions.
Fine-grained Interest Matching for Neural News Recommendation
Existing studies generally represent each user as a single vector and then match the candidate news vector, which may lose fine-grained information for recommendation.
Lightrec: A memory and search-efficient recommender system
On top of such a structure, LightRec will have an item represented as additive composition of B codewords, which are optimally selected from each of the codebooks.
Machine learning driven synthesis of few-layered WTe2
Reducing the lateral scale of two-dimensional (2D) materials to one-dimensional (1D) has attracted substantial research interest not only to achieve competitive electronic device applications but also for the exploration of fundamental physical properties.
Vehicle Re-identification with Viewpoint-aware Metric Learning
This paper considers vehicle re-identification (re-ID) problem.
OD-GCN: Object Detection Boosted by Knowledge GCN
Classical CNN based object detection methods only extract the objects' image features, but do not consider the high-level relationship among objects in context.
Robustifying deep networks for image segmentation
Materials and Methods: In this retrospective study, the accuracy of brain tumor segmentation was studied in subjects with low- and high-grade gliomas.
A Novel User Representation Paradigm for Making Personalized Candidate Retrieval
Our work is undergoing a anonymous review, and it will soon be released after the notification.
Neural News Recommendation with Long- and Short-term User Representations
In this paper, we propose a neural news recommendation approach which can learn both long- and short-term user representations.
Ranked #7 on
News Recommendation
on MIND
A Symmetric Encoder-Decoder with Residual Block for Infrared and Visible Image Fusion
For the fusion stage, first, the trained model is utilized to extract the intermediate features and compensation features of two source images.
Machine learning-guided synthesis of advanced inorganic materials
Synthesis of advanced inorganic materials with minimum number of trials is of paramount importance towards the acceleration of inorganic materials development.
Feedback Network for Image Super-Resolution
In this paper, we propose an image super-resolution feedback network (SRFBN) to refine low-level representations with high-level information.
Towards end-to-end pulsed eddy current classification and regression with CNN
Pulsed eddy current (PEC) is an effective electromagnetic non-destructive inspection (NDI) technique for metal materials, which has already been widely adopted in detecting cracking and corrosion in some multi-layer structures.
Generic Model-Agnostic Convolutional Neural Network for Single Image Dehazing
Haze and smog are among the most common environmental factors impacting image quality and, therefore, image analysis.
Ranked #24 on
Image Dehazing
on SOTS Outdoor
IR2VI: Enhanced Night Environmental Perception by Unsupervised Thermal Image Translation
Context enhancement is critical for night vision (NV) applications, especially for the dark night situation without any artificial lights.
Efficient Traffic-Sign Recognition with Scale-aware CNN
The paper presents a Traffic Sign Recognition (TSR) system, which can fast and accurately recognize traffic signs of different sizes in images.
Adversarial Binary Coding for Efficient Person Re-identification
Specifically, instead of learning explicit projections or adding fully-connected mapping layers, the proposed Adversarial Binary Coding (ABC) framework guides the extraction of binary codes implicitly and effectively.
Multi-Channel CNN-based Object Detection for Enhanced Situation Awareness
In this study, we propose a novel detection algorithm for military objects by fusing multi-channel CNNs.
Feature Map Pooling for Cross-View Gait Recognition Based on Silhouette Sequence Images
In this paper, we develop a novel convolutional neural network based approach to extract and aggregate useful information from gait silhouette sequence images instead of simply representing the gait process by averaging silhouette images.
