Papers Re-Ranking
“Re-Ranking” 태그가 달린 논문 743편 · 필터 해제
Efficient large-scale image retrieval with deep feature orthogonality and Hybrid-Swin-Transformers
We present an efficient end-to-end pipeline for largescale landmark recognition and retrieval. We show how to combine and enhance concepts from recent research in image retrieval and introduce two architectures especiall…
Image RetrievalLandmark RecognitionRe-RankingRetrievalSDR: Efficient Neural Re-ranking using Succinct Document Representation
BERT based ranking models have achieved superior performance on various information retrieval tasks. However, the large number of parameters and complex self-attention operation come at a significant latency overhead. To…
Information RetrievalQuantizationRerankingRe-Ranking+1Building an Efficient and Effective Retrieval-based Dialogue System via Mutual Learning
Establishing retrieval-based dialogue systems that can select appropriate responses from the pre-built index has gained increasing attention from researchers. For this task, the adoption of pre-trained language models (s…
Re-RankingRetrievalA Technical Report for ICCV 2021 VIPriors Re-identification Challenge
Person re-identification has always been a hot and challenging task. This paper introduces our solution for the re-identification track in VIPriors Challenge 2021. In this challenge, the difficulty is how to train the mo…
Ensemble LearningImage AugmentationPerson Re-IdentificationRe-RankingMultilingual Fact Linking
Knowledge-intensive NLP tasks can benefit from linking natural language text with facts from a Knowledge Graph (KG). Although facts themselves are language-agnostic, the fact labels (i.e., language-specific representatio…
Re-RankingRetrievalSentencevalidGenerating Scenes with Latent Object Models
We introduce a structured latent variable model that learns the underlying data-generating process for a dataset of scenes. Our goals are to obtain a compositional scene representation and to perform scene generation by …
ObjectRe-RankingRetrievalScene Generation+1Deep Fusion of Multi-attentive Local and Global Features with Higher Efficiency for Image Retrieval
Image retrieval is to search images similar to the given query image by extracting features. Previously, methods that firstly search by global features then re-rank images using local feature matching were proposed, whic…
Image RetrievalRe-RankingRetrievalPPL-MCTS: Constrained Textual Generation Through Discriminator-Guided MCTS Decoding
Large language models (LM) based on Transformers allow to generate plausible long texts. In this paper, we explore how this generation can be further controlled at decoding time to satisfy certain constraints (e.g. being…
Language ModellingRe-RankingViewpoint Invariant Dense Matching for Visual Geolocalization
In this paper we propose a novel method for image matching based on dense local features and tailored for visual geolocalization. Dense local features matching is robust against changes in illumination and occlusions, bu…
Re-RankingRetrievalMeasuring Factual Consistency of Abstractive Summaries
Recent abstractive summarization systems fail to generate factual consistent -- faithful -- summaries, which heavily limits their practical application. Commonly, these models tend to mix concepts from the source or hal…
Abstractive Text SummarizationRe-RankingCompact Binary Fingerprint for Image Copy Re-Ranking
Image copy detection is challenging and appealing topic in computer vision and signal processing. Recent advancements in multimedia have made distribution of image across the global easy and fast: that leads to many othe…
Copy DetectionRe-RankingRetrievalLooking for Confirmations: An Effective and Human-Like Visual Dialogue Strategy
Generating goal-oriented questions in Visual Dialogue tasks is a challenging and long-standing problem. State-Of-The-Art systems are shown to generate questions that, although grammatically correct, often lack an effecti…
Re-RankingYou Get What You Chat: Using Conversations to Personalize Search-based Recommendations
Prior work on personalized recommendations has focused on exploiting explicit signals from user-specific queries, clicks, likes, and ratings. This paper investigates tapping into a different source of implicit signals of…
Re-RankingPersonalized Entity Search by Sparse and Scrutable User Profiles
Prior work on personalizing web search results has focused on considering query-and-click logs to capture users individual interests. For product search, extensive user histories about purchases and ratings have been exp…
Re-RankingD-REX: Dialogue Relation Extraction with Explanations
Existing research studies on cross-sentence relation extraction in long-form multi-party conversations aim to improve relation extraction without considering the explainability of such methods. This work addresses that g…
Dialog Relation ExtractionRelationrelation explanationRelation Extraction+2Enhancing Self-Disclosure In Neural Dialog Models By Candidate Re-ranking
Neural language modelling has progressed the state-of-the-art in different downstream Natural Language Processing (NLP) tasks. One such area is of open-domain dialog modelling, neural dialog models based on GPT-2 such as…
Language ModellingOpen-Domain DialogRe-RankingSideControl: Controlled Open-domain Dialogue Generation via Additive Side Networks
Transformer-based pre-trained language models boost the performance of open-domain dialogue systems. Prior works leverage Transformer-based pre-trained language models to generate texts with desired attributes in two gen…
AttributeDialogue GenerationRe-RankingMulti-modal Representation Learning for Video Advertisement Content Structuring
Video advertisement content structuring aims to segment a given video advertisement and label each segment on various dimensions, such as presentation form, scene, and style. Different from real-life videos, video advert…
Representation LearningRe-RankingVideo UnderstandingSpelling Correction for Russian: A Comparative Study of Datasets and Methods
We develop a minimally-supervised model for spelling correction and evaluate its performance on three datasets annotated for spelling errors in Russian. The first corpus is a dataset of Russian social media data that was…
Cross-corpusMachine TranslationRe-RankingSpelling Correction+1mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset
The MS MARCO ranking dataset has been widely used for training deep learning models for IR tasks, achieving considerable effectiveness on diverse zero-shot scenarios. However, this type of resource is scarce in languages…
Information RetrievalMachine TranslationPassage RankingReranking+3