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Found 18 papers, 9 papers with code
Memory Enhanced Global-Local Aggregation for Video Object Detection
We argue that there are two important cues for humans to recognize objects in videos: the global semantic information and the local localization information.
Ranked #12 on
Video Object Detection
on ImageNet VID
You Impress Me: Dialogue Generation via Mutual Persona Perception
Despite the continuing efforts to improve the engagingness and consistency of chit-chat dialogue systems, the majority of current work simply focus on mimicking human-like responses, leaving understudied the aspects of modeling understanding between interlocutors.
Ranked #2 on
Dialogue Generation
on Persona-Chat
(using extra training data)
RepPoints V2: Verification Meets Regression for Object Detection
Though RepPoints provides high performance, we find that its heavy reliance on regression for object localization leaves room for improvement.
Ranked #27 on
Object Detection
on COCO-O
Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations
Learning good representations on multi-relational graphs is essential to knowledge base completion (KBC).
Ranked #1 on
Link Prediction
on CoDEx Small
Learnable Embedding Sizes for Recommender Systems
Existing works that try to address the problem always cause a significant drop in recommendation performance or suffers from the limitation of unaffordable training time cost.
LambdaOpt: Learn to Regularize Recommender Models in Finer Levels
We show how to employ LambdaOpt on matrix factorization, a classical model that is representative of a large family of recommender models.
Boosting 3D Object Detection via Object-Focused Image Fusion
Given a set of point features and image feature maps, DeMF adaptively aggregates image features by taking the projected 2D location of the 3D point as reference.
Ranked #5 on
3D Object Detection
on SUN-RGBD val
PointScatter: Point Set Representation for Tubular Structure Extraction
Inspired by this, we propose PointScatter, an alternative to the segmentation models for the tubular structure extraction task.
HDI-Forest: Highest Density Interval Regression Forest
By seeking the narrowest prediction intervals (PIs) that satisfy the specified coverage probability requirements, the recently proposed quality-based PI learning principle can extract high-quality PIs that better summarize the predictive certainty in regression tasks, and has been widely applied to solve many practical problems.
Learning-to-Ask: Knowledge Acquisition via 20 Questions
Almost all the knowledge empowered applications rely upon accurate knowledge, which has to be either collected manually with high cost, or extracted automatically with unignorable errors.
A Domain Generalization Perspective on Listwise Context Modeling
As one of the most popular techniques for solving the ranking problem in information retrieval, Learning-to-rank (LETOR) has received a lot of attention both in academia and industry due to its importance in a wide variety of data mining applications.
Session-based Sequential Skip Prediction via Recurrent Neural Networks
The focus of WSDM cup 2019 is session-based sequential skip prediction, i. e. predicting whether users will skip tracks, given their immediately preceding interactions in their listening session.
ReFactor GNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective
Factorisation-based Models (FMs), such as DistMult, have enjoyed enduring success for Knowledge Graph Completion (KGC) tasks, often outperforming Graph Neural Networks (GNNs).
Check and Link: Pairwise Lesion Correspondence Guides Mammogram Mass Detection
In mammogram mass detection, modeling pairwise lesion correspondence explicitly is particularly important.
Mini-Model Adaptation: Efficiently Extending Pretrained Models to New Languages via Aligned Shallow Training
Prior work shows that it is possible to expand pretrained Masked Language Models (MLMs) to new languages by learning a new set of embeddings, while keeping the transformer body frozen.
Improving Language Plasticity via Pretraining with Active Forgetting
Pretrained language models (PLMs) are today the primary model for natural language processing.
TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usage
With recent advancements in natural language processing, Large Language Models (LLMs) have emerged as powerful tools for various real-world applications.
TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems
Large Language Models (LLMs) have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools that require a blend of task planning and the utilization of external tools, such as APIs.
