Search Results for author:
Found 29 papers, 9 papers with code
Visual Prompt Tuning for Few-Shot Text Classification
In few-shot text classification, a feasible paradigm for deploying VL-PTMs is to align the input samples and their category names via the text encoders.
Decoupled Training: Return of Frustratingly Easy Multi-Domain Learning
Multi-domain learning (MDL) aims to train a model with minimal average risk across multiple overlapping but non-identical domains.
STEM: Unleashing the Power of Embeddings for Multi-task Recommendation
Surprisingly, negative transfer still occurs in existing MTL methods on samples that receive comparable feedback across tasks.
Temporal Interest Network for Click-Through Rate Prediction
Our proposed TIN outperforms the best-performing baselines by 0. 43\% and 0. 29\% on two datasets, respectively.
Global and Local Semantic Completion Learning for Vision-Language Pre-training
Therefore, in this paper, we propose a novel Global and Local Semantic Completion Learning (GLSCL) task to facilitate global-local alignment and local-local alignment simultaneously.
VLPD: Context-Aware Pedestrian Detection via Vision-Language Semantic Self-Supervision
Firstly, we propose a self-supervised Vision-Language Semantic (VLS) segmentation method, which learns both fully-supervised pedestrian detection and contextual segmentation via self-generated explicit labels of semantic classes by vision-language models.
Ranked #5 on
Pedestrian Detection
on Caltech
ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning
Auxiliary-Task Learning (ATL) aims to improve the performance of the target task by leveraging the knowledge obtained from related tasks.
The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge
It is intended to resolve the above-mentioned problems and promote the development of both intracranial hemorrhage segmentation and anisotropic data processing.
Tencent AVS: A Holistic Ads Video Dataset for Multi-modal Scene Segmentation
Therefore, TAVS is distinguished from previous temporal segmentation datasets due to its multi-modal information, holistic view of categories, and hierarchical granularities.
AdaTask: A Task-aware Adaptive Learning Rate Approach to Multi-task Learning
In this paper, we propose to measure the task dominance degree of a parameter by the total updates of each task on this parameter.
Seeing What You Miss: Vision-Language Pre-training with Semantic Completion Learning
Cross-modal alignment is essential for vision-language pre-training (VLP) models to learn the correct corresponding information across different modalities.
AutoAttention: Automatic Field Pair Selection for Attention in User Behavior Modeling
In this paper, we propose a novel model named AutoAttention, which includes all item/user/context side fields as the query, and assigns a learnable weight for each field pair between behavior fields and query fields.
Towards In-distribution Compatibility in Out-of-distribution Detection
Various training criteria for these auxiliary outliers are proposed based on heuristic intuitions.
DPTNet: A Dual-Path Transformer Architecture for Scene Text Detection
We further propose a parallel design that integrates the convolutional network with a powerful self-attention mechanism to provide complementary clues between the attention path and convolutional path.
Tencent Text-Video Retrieval: Hierarchical Cross-Modal Interactions with Multi-Level Representations
With multi-level representations for video and text, hierarchical contrastive learning is designed to explore fine-grained cross-modal relationships, i. e., frame-word, clip-phrase, and video-sentence, which enables HCMI to achieve a comprehensive semantic comparison between video and text modalities.
Ranked #1 on
Video Retrieval
on MSVD
(using extra training data)
Exploiting Spatial-Temporal Semantic Consistency for Video Scene Parsing
Compared with image scene parsing, video scene parsing introduces temporal information, which can effectively improve the consistency and accuracy of prediction.
Pure Characteristics Demand Models and Distributionally Robust Mathematical Programs with Stochastic Complementarity Constraints
We formulate pure characteristics demand models under uncertainties of probability distributions as distributionally robust mathematical programs with stochastic complementarity constraints (DRMP-SCC).
Optimization and Control 90C15, 90C33, 90C26
New gedanken experiment on higher-dimensional asymptotically AdS Reissner-Nordström black hole
Viewing the negative cosmological constant as a dynamical quantity derived from the matter field, we study the weak cosmic censorship conjecture for the higher-dimensional asymptotically AdS Reissner-Nordstr\"om black hole.
General Relativity and Quantum Cosmology High Energy Physics - Theory
Scene Segmentation with Dual Relation-aware Attention Network
We conduct extensive experiments to validate the effectiveness of our network and achieve new state-of-the-art segmentation performance on four challenging scene segmentation data sets, i. e., Cityscapes, ADE20K, PASCAL Context, and COCO Stuff data sets.
Ranked #8 on
Semantic Segmentation
on COCO-Stuff test
The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design
The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques.
Materials Science Computational Physics
Non-Autoregressive Image Captioning with Counterfactuals-Critical Multi-Agent Learning
In this paper, we propose a Non-Autoregressive Image Captioning (NAIC) model with a novel training paradigm: Counterfactuals-critical Multi-Agent Learning (CMAL).
KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion
Based on the datasets, we propose novel tasks such as multi-hop knowledge abstraction (MKA), multi-hop knowledge concretization (MKC) and then design a comprehensive benchmark.
AHash: A Load-Balanced One Permutation Hash
Unlike the densification to fill the empty bins after they undesirably occur, our design goal is to balance the load so as to reduce the empty bins in advance.
X-Forest: Approximate Random Projection Trees for Similarity Measurement
First, we introduced RP Trees into the tasks of similarity measurement such that accuracy is improved.
Static charged dilaton black hole cannot be overcharged by gedanken experiments
We consider the new version of the gedanken experiments proposed recently by Sorce and Wald to overcharge a static charged dilaton black hole.
High Energy Physics - Theory
Deep Learning Based Energy Disaggregation and On/Off Detection of Household Appliances
On the basis of energy disaggregation, we then investigate the performance of two deep-learning based frameworks for the task of on/off detection which aims at estimating whether an appliance is in operation or not.
DENet: A Universal Network for Counting Crowd with Varying Densities and Scales
Counting people or objects with significantly varying scales and densities has attracted much interest from the research community and yet it remains an open problem.
Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods
Solar-energy plays an important role in solving serious environmental problems and meeting high-energy demand.
Materials Science
Machine Translation for Subtitling: A Large-Scale Evaluation
This article describes a large-scale evaluation of the use of Statistical Machine Translation for professional subtitling.
