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Chao Yao

14 accepted papers

2026

Adaptive Scaling of Policy Constraints for Offline Reinforcement Learning

ICLR 2026poster

Offline reinforcement learning (RL) enables learning effective policies from fixed datasets without any environment interaction. Existing methods typically employ policy constraints to mitigate the distribution shift encountered during offline RL training. However, because the scale of the constrain…

Cited by 0SourcecodeScholar
2026

QD-PCQA: Quality-Aware Domain Adaptation for Point Cloud Quality Assessment

CVPR 2026

No-Reference Point Cloud Quality Assessment (NR-PCQA) still struggles with generalization, primarily due to the scarcity of annotated point cloud datasets. Since the Human Visual System (HVS) drives perceptual quality assessment independently of media types, prior knowledge on quality learned from i

Cited by 0SourcecodeScholar
2026

Step-Aware Residual-Guided Diffusion for EEG Spatial Super-Resolution

ICLR 2026poster

For real-world BCI applications, lightweight Electroencephalography (EEG) systems offer the best cost–deployment balance. However, such spatial sparsity of EEG limits spatial fidelity, hurting learning and introducing bias. EEG spatial super-resolution methods aim to recover high-density EEG signals…

Cited by 0SourceScholar
2026

Virtual Nodes Guided Dynamic Graph Neural Network for Brain Tumor Segmentation with Missing Modalities

CVPR 2026

Multimodal magnetic resonance imaging (MRI) is crucial for brain tumor segmentation, with many methods leveraging its four key modalities to capture complementary information for effective sub-region analysis. However, the absence of several modalities is very common in practice, leading to severe p

Cited by 0SourceScholar
2025

ALIC: Adaptive Fusion Entropy Model for Learned Image Compression

ICASSP 2025accepted

Recently, learned image compression algorithms have achieved significant performance. The entropy model is crucial for improving the rate-distortion performance by estimating the probability distribution of latent representation. In this paper, we propose an adaptive fusion entropy model for learned…

Cited by 0SourceScholar
2025

DATA-VSR: Dynamic Trajectory Attention and Texture Adaptive Rooter for Video Super-Resolution

ICASSP 2025accepted

Video Super-Resolution (VSR) is essential for reconstructing high-definition sequences from correlated video frames. While Transformer-based VSR methods have improved reconstruction quality, they require substantial computational resources, limiting deployment on resource-constrained devices. To tac…

Cited by 0SourceScholar
2024

An Object-Driven Navigation Strategy Based on Active Perception and Semantic Association

RA-L 2024

Efficiently navigating to a specific kind of objects in an unknown environment is an important and challenging research topic in Embodied AI. Existing methods, such as end-to-end learning ones and modular ones still struggle at this task as they have poor efficiency, interpretability and/or generali

Cited by 5SourceScholar
2024

RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict

COLING 2024main

Fact-checking is the task of verifying the factuality of a given claim by examining the available evidence. High-quality evidence plays a vital role in enhancing fact-checking systems and facilitating the generation of explanations that are understandable to humans. However, the provision of both su…

2024

SeeClear: Semantic Distillation Enhances Pixel Condensation for Video Super-Resolution

NeurIPS 2024poster

Diffusion-based Video Super-Resolution (VSR) is renowned for generating perceptually realistic videos, yet it grapples with maintaining detail consistency across frames due to stochastic fluctuations. The traditional approach of pixel-level alignment is ineffective for diffusion-processed frames bec…

2024

Semantic Lens: Instance-Centric Semantic Alignment for Video Super-resolution

AAAI 2024technical

As a critical clue of video super-resolution (VSR), inter-frame alignment significantly impacts overall performance. However, accurate pixel-level alignment is a challenging task due to the intricate motion interweaving in the video. In response to this issue, we introduce a novel paradigm for VSR n…

2024

Spatial-Related Sensors Matters: 3D Human Motion Reconstruction Assisted with Textual Semantics

AAAI 2024technical

Leveraging wearable devices for motion reconstruction has emerged as an economical and viable technique. Certain methodologies employ sparse Inertial Measurement Units (IMUs) on the human body and harness data-driven strategies to model human poses. However, the reconstruction of motion based solely…

Cited by 4SourcePDFScholar
2024

Who Looks like Me: Semantic Routed Image Harmonization

IJCAI 2024poster

Image harmonization, aiming to seamlessly blend extraneous foreground objects with background images, is a promising and challenging task.Ensuring a synthetic image appears realistic requires maintaining consistency in visual characteristics, such as texture and style, across global and semantic reg…

Cited by 0SourcePDFScholar
2023

SIGVIC: Spatial Importance Guided Variable-Rate Image Compression

ICASSP 2023accepted

Variable-rate mechanism has improved the flexibility and efficiency of learning-based image compression that trains multiple models for different rate-distortion tradeoffs. One of the most common approaches for variable-rate is to channel- wisely or spatial-uniformly scale the internal features. How…

Cited by 0SourceScholar
2020

Feature Representation Matters: End-to-End Learning for Reference-based Image Super-resolution

ECCV 2020poster

In this paper, we are aiming for a general reference-based super-resolution setting: it does not require the low-resolution image and the high-resolution reference image to be well aligned or with a similar texture. Instead, we only intend to transfer the relevant textures from reference images to t…

Cited by 46SourcePDFScholar