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Zhen Yao

7 accepted papers

2026

CHROMOUVQA: BENCHMARKING VISION-LANGUAGE MODELS UNDER CHROMATIC CAMOUFLAGED IMAGES

ICASSP 2026poster

Vision-Language Models (VLMs) have advanced multimodal understanding, yet still struggle when targets are embedded in cluttered backgrounds requiring figure-ground segregation. To address this, we introduce ChromouVQA, a large-scale, multi-task benchmark based on Ishihara-style chromatic camouflaged…

Cited by 0SourcePDFScholar
2024

CrackNex: a Few-shot Low-light Crack Segmentation Model Based on Retinex Theory for UAV Inspections

ICRA 2024poster

Routine visual inspections of concrete structures are imperative for upholding the safety and integrity of critical infrastructure. Such visual inspections sometimes happen under low-light conditions, e.g., checking for bridge health. Crack segmentation under such conditions is challenging due to th…

Cited by 13SourcecodeScholar
2024

Identification and Estimation of the Bi-Directional MR with Some Invalid Instruments

NeurIPS 2024oral

We consider the challenging problem of estimating causal effects from purely observational data in the bi-directional Mendelian randomization (MR), where some invalid instruments, as well as unmeasured confounding, usually exist. To address this problem, most existing methods attempt to find proper…

Cited by 0SourcePDFScholar
2023

Analogical Inference Enhanced Knowledge Graph Embedding

AAAI 2023technical

Knowledge graph embedding (KGE), which maps entities and relations in a knowledge graph into continuous vector spaces, has achieved great success in predicting missing links in knowledge graphs. However, knowledge graphs often contain incomplete triples that are difficult to inductively infer by KGE…

2023

Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding

AAAI 2023technical

We propose an entity-agnostic representation learning method for handling the problem of inefficient parameter storage costs brought by embedding knowledge graphs. Conventional knowledge graph embedding methods map elements in a knowledge graph, including entities and relations, into continuous vect…

2022

Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting

IJCAI 2022poster

We study the knowledge extrapolation problem to embed new components (i.e., entities and relations) that come with emerging knowledge graphs (KGs) in the federated setting. In this problem, a model trained on an existing KG needs to embed an emerging KG with unseen entities and relations. To solve t…