Papers Re-Ranking
“Re-Ranking” 태그가 달린 논문 743편 · 필터 해제
Visual Re-Ranking with Non-Visual Side Information
The standard approach for visual place recognition is to use global image descriptors to retrieve the most similar database images for a given query image. The results can then be further improved with re-ranking methods…
Graph Neural NetworkImage RetrievalIndoor LocalizationRe-Ranking+3StePO-Rec: Towards Personalized Outfit Styling Assistant via Knowledge-Guided Multi-Step Reasoning
Advancements in Generative AI offers new opportunities for FashionAI, surpassing traditional recommendation systems that often lack transparency and struggle to integrate expert knowledge, leaving the potential for perso…
AttributeRecommendation SystemsRe-RankingFocus on Local: Finding Reliable Discriminative Regions for Visual Place Recognition
Visual Place Recognition (VPR) is aimed at predicting the location of a query image by referencing a database of geotagged images. For VPR task, often fewer discriminative local regions in an image produce important effe…
Computational EfficiencyImage RetrievalRe-RankingRetrieval+1Iterative Self-Training for Code Generation via Reinforced Re-Ranking
Generating high-quality code that solves complex programming tasks is challenging, especially with current decoder-based models that produce highly stochastic outputs. In code generation, even minor errors can easily bre…
Code GenerationRerankingRe-RankingBreaking the Lens of the Telescope: Online Relevance Estimation over Large Retrieval Sets
Advanced relevance models, such as those that use large language models (LLMs), provide highly accurate relevance estimations. However, their computational costs make them infeasible for processing large document corpora…
Re-RankingRetrievalDiversity-aware Dual-promotion Poisoning Attack on Sequential Recommendation
Sequential recommender systems (SRSs) excel in capturing users' dynamic interests, thus playing a key role in various industrial applications. The popularity of SRSs has also driven emerging research on their security as…
Data PoisoningDiversityRecommendation SystemsRe-Ranking+1To Match or Not to Match: Revisiting Image Matching for Reliable Visual Place Recognition
Visual Place Recognition (VPR) is a critical task in computer vision, traditionally enhanced by re-ranking retrieval results with image matching. However, recent advancements in VPR methods have significantly improved pe…
Re-RankingRetrievalVisual Place RecognitionEquiCPI: SE(3)-Equivariant Geometric Deep Learning for Structure-Aware Prediction of Compound-Protein Interactions
Accurate prediction of compound-protein interactions (CPI) remains a cornerstone challenge in computational drug discovery. While existing sequence-based approaches leverage molecular fingerprints or graph representation…
Drug DiscoveryRe-RankingUnleashing the Power of LLMs in Dense Retrieval with Query Likelihood Modeling
Dense retrieval is a crucial task in Information Retrieval (IR) and is the foundation for downstream tasks such as re-ranking. Recently, large language models (LLMs) have shown compelling semantic understanding capabilit…
Information RetrievalLanguage ModelingLanguage ModellingRe-Ranking+2Towards Principled Learning for Re-ranking in Recommender Systems
As the final stage of recommender systems, re-ranking presents ordered item lists to users that best match their interests. It plays such a critical role and has become a trending research topic with much attention from …
Recommendation SystemsRe-RankingDistillation and Refinement of Reasoning in Small Language Models for Document Re-ranking
We present a novel approach for training small language models for reasoning-intensive document ranking that combines knowledge distillation with reinforcement learning optimization. While existing methods often rely on …
Document RankingInformation RetrievalKnowledge DistillationLanguage Modeling+4Exploring the Effectiveness of Multi-stage Fine-tuning for Cross-encoder Re-rankers
State-of-the-art cross-encoders can be fine-tuned to be highly effective in passage re-ranking. The typical fine-tuning process of cross-encoders as re-rankers requires large amounts of manually labelled data, a contrast…
Contrastive LearningLanguage ModelingLanguage ModellingLarge Language Model+2LOCORE: Image Re-ranking with Long-Context Sequence Modeling
We introduce LOCORE, Long-Context Re-ranker, a model that takes as input local descriptors corresponding to an image query and a list of gallery images and outputs similarity scores between the query and each gallery ima…
Image RetrievalRe-RankingRetrievalGENIUS: A Generative Framework for Universal Multimodal Search
Generative retrieval is an emerging approach in information retrieval that generates identifiers (IDs) of target data based on a query, providing an efficient alternative to traditional embedding-based retrieval methods.…
Information RetrievalQuantizationRe-RankingRetrievalLong Context Modeling with Ranked Memory-Augmented Retrieval
Effective long-term memory management is crucial for language models handling extended contexts. We introduce a novel framework that dynamically ranks memory entries based on relevance. Unlike previous works, our model i…
Information RetrievalLearning-To-RankManagementRe-Ranking+1Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities
Knowledge graphs have emerged to be promising datastore candidates for context augmentation during Retrieval Augmented Generation (RAG). As a result, techniques in graph representation learning have been simultaneously e…
Document RankingGraph LearningGraph Representation LearningInformation Retrieval+6Representation-based Reward Modeling for Efficient Safety Alignment of Large Language Model
Reinforcement Learning (RL) algorithms for safety alignment of Large Language Models (LLMs), such as Direct Preference Optimization (DPO), encounter the challenge of distribution shift. Current approaches typically addre…
Language ModelingLanguage ModellingLarge Language ModelReinforcement Learning (RL)+2External Knowledge Injection for CLIP-Based Class-Incremental Learning
Class-Incremental Learning (CIL) enables learning systems to continuously adapt to evolving data streams. With the advancement of pre-training, leveraging pre-trained vision-language models (e.g., CLIP) offers a promisin…
class-incremental learningClass Incremental LearningData AugmentationIncremental Learning+2StructVPR++: Distill Structural and Semantic Knowledge with Weighting Samples for Visual Place Recognition
Visual place recognition is a challenging task for autonomous driving and robotics, which is usually considered as an image retrieval problem. A commonly used two-stage strategy involves global retrieval followed by re-r…
Autonomous DrivingImage RetrievalRe-RankingRetrieval+1CarGait: Cross-Attention based Re-ranking for Gait recognition
Gait recognition is a computer vision task that identifies individuals based on their walking patterns. Gait recognition performance is commonly evaluated by ranking a gallery of candidates and measuring the accuracy at …
Gait RecognitionRe-Ranking