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Found 19 papers, 6 papers with code
HiQA: A Hierarchical Contextual Augmentation RAG for Massive Documents QA
As language model agents leveraging external tools rapidly evolve, significant progress has been made in question-answering(QA) methodologies utilizing supplementary documents and the Retrieval-Augmented Generation (RAG) approach.
M$^3$Net: Multilevel, Mixed and Multistage Attention Network for Salient Object Detection
To overcome these, we propose the M$^3$Net, i. e., the Multilevel, Mixed and Multistage attention network for Salient Object Detection (SOD).
Ranked #2 on
RGB Salient Object Detection
on HKU-IS
ProxyFormer: Proxy Alignment Assisted Point Cloud Completion with Missing Part Sensitive Transformer
Specifically, we fuse information into point proxy via feature and position extractor, and generate features for missing point proxies from the features of existing point proxies.
Contextual Conservative Q-Learning for Offline Reinforcement Learning
In this paper, we propose Contextual Conservative Q-Learning(C-CQL) to learn a robustly reliable policy through the contextual information captured via an inverse dynamics model.
Robust Action Gap Increasing with Clipped Advantage Learning
Advantage Learning (AL) seeks to increase the action gap between the optimal action and its competitors, so as to improve the robustness to estimation errors.
Smoothing Advantage Learning
Advantage learning (AL) aims to improve the robustness of value-based reinforcement learning against estimation errors with action-gap-based regularization.
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising
Third, to deploy MAAB in the large-scale advertising system with millions of advertisers, we propose a mean-field approach.
Greedy-Step Off-Policy Reinforcement Learning
Most of the policy evaluation algorithms are based on the theories of Bellman Expectation and Optimality Equation, which derive two popular approaches - Policy Iteration (PI) and Value Iteration (VI).
Stabilizing Q Learning Via Soft Mellowmax Operator
Learning complicated value functions in high dimensional state space by function approximation is a challenging task, partially due to that the max-operator used in temporal difference updates can theoretically cause instability for most linear or non-linear approximation schemes.
Deep Robust Multilevel Semantic Cross-Modal Hashing
Hashing based cross-modal retrieval has recently made significant progress.
SMIX($λ$): Enhancing Centralized Value Functions for Cooperative Multi-Agent Reinforcement Learning
Learning a stable and generalizable centralized value function (CVF) is a crucial but challenging task in multi-agent reinforcement learning (MARL), as it has to deal with the issue that the joint action space increases exponentially with the number of agents in such scenarios.
Truly Proximal Policy Optimization
Proximal policy optimization (PPO) is one of the most successful deep reinforcement-learning methods, achieving state-of-the-art performance across a wide range of challenging tasks.
Robust Reinforcement Learning in POMDPs with Incomplete and Noisy Observations
In real-world scenarios, the observation data for reinforcement learning with continuous control is commonly noisy and part of it may be dynamically missing over time, which violates the assumption of many current methods developed for this.
Trust Region-Guided Proximal Policy Optimization
We formally show that this method not only improves the exploration ability within the trust region but enjoys a better performance bound compared to the original PPO as well.
A Unified Gender-Aware Age Estimation
Although leading to promotion of age estimation performance, such a concatenation not only likely confuses the semantics between the gender and age, but also ignores the aging discrepancy between the male and the female.
Face Alignment In-the-Wild: A Survey
Over the last two decades, face alignment or localizing fiducial facial points has received increasing attention owing to its comprehensive applications in automatic face analysis.
Bayesian Neighbourhood Component Analysis
Learning a good distance metric in feature space potentially improves the performance of the KNN classifier and is useful in many real-world applications.
Tri-Subject Kinship Verification: Understanding the Core of A Family
One major challenge in computer vision is to go beyond the modeling of individual objects and to investigate the bi- (one-versus-one) or tri- (one-versus-two) relationship among multiple visual entities, answering such questions as whether a child in a photo belongs to given parents.
Unsupervised Feature Learning with C-SVDDNet
To address this issue, we propose a SVDD based feature learning algorithm that describes the density and distribution of each cluster from K-means with an SVDD ball for more robust feature representation.
Ranked #23 on
Image Classification
on MNIST
