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- Junliang ChenSuggested from co-authorship
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Works116 from public data
- Learning Latent Forests for Medical Relation Extraction401
This paper presents a novel method that automatically learns a retrieval model alternately with the programmer from weak supervision alternately with the programmer from weak supervision, i.e., the system’s performance with respect to the produced answers.
- Reasoning with Latent Structure Refinement for Document-Level Relation Extraction332
This work proposes a novel model that empowers the relational reasoning across sentences by automatically inducing the latent document-level graph and develops a refinement strategy, which enables the model to incrementally aggregate relevant information for multi-hop reasoning.
- Overview of AI and communication for 6G network: fundamentals, challenges, and future research opportunities261
The quality of AI service is defined, which refers to a framework for measuring AI services within the network, which is anticipated that future 6G networks will innately provide AI functions as services, supporting application scenarios like immersive communication and intelligent industrial robots.
- Interventional Video Grounding with Dual Contrastive Learning175
A novel paradigm from the perspective of the causal inference, i.e., interventional video grounding (IVG) that leverages backdoor adjustment to deconfound the selection bias based on structured causal model (SCM) and do-calculus P(Y |do(X)).
- Physical-Layer Adversarial Robustness for Deep Learning-Based Semantic Communications102
This work presents MobileSC, a novel semantic communication framework that considers the computation and memory efficiency in wireless environments and proposes SemAdv, a physical-layer adversarial perturbation generator that aims to craft semantic adversaries over the air with the abovementioned criteria.
- You Can Ground Earlier than See: An Effective and Efficient Pipeline for Temporal Sentence Grounding in Compressed Videos97
A new setting is posed, compressed-domain TSG, which directly utilizes compressed videos rather than fully-decompressed frames as the visual input, and a novel Three-branch Compressed-domain Spatial-temporal Fusion (TCSF) framework, which extracts and aggregates three kinds of low-level visual features for effective and efficient grounding.
- Uncovering what, why and How: A Comprehensive Benchmark for Causation Understanding of Video Anomaly70
MMEval is introduced, a novel evaluation metric designed to better align with human preferences for CUVA, facilitating the measurement of existing LLMs in comprehending the underlying cause and corresponding effect of video anoma-lies.
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- A survey on Information-centric Networking: Rationales, designs and debates64
The rationales of ICN are revealed from the perspective of the Internet evolution, different design choices are surveyed, and debate on two debatable topics in ICN is discussed, i.e., self-certifying versus hierarchical names, and edge versus pervasive caching.
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- Uncovering Main Causalities for Long-tailed Information Extraction52
The proposed counterfactual IE (CFIE) is a novel framework that aims to uncover the main causalities behind data in the view of causal inference, and introduces a unified structural causal model for various IE tasks, describing the relationships among variables.
- Semantic Entropy Can Simultaneously Benefit Transmission Efficiency and Channel Security of Wireless Semantic Communications49
SemEntropy, a novel method that answers the above question by exploring the semantics of data for both adaptive transmission and physical layer encryption by exploring the expectation of various semantic scores regarding the transmission goal of the DLSC, is proposed.
- SemProtector: A Unified Framework for Semantic Protection in Deep Learning-based Semantic Communication Systems40
SemProtector is presented, a unified framework that aims to secure an online SC system with three hot-pluggable semantic protection modules that are able to encrypt semantics to be transmitted by an encryption method, mitigate privacy risks from wireless channels by a perturbation mechanism, and calibrate distorted semantics at the destination by a semantic signature generation method.
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- Towards Robust Temporal Activity Localization Learning with Noisy Labels18
A novel method called Co-Teaching Regularizer (CTR) is proposed, which utilizes two models sharing the same framework to teach each other for robust learning and is significantly more robust to the noisy training data compared to the existing methods.
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- Filling the Information Gap between Video and Query for Language-Driven Moment Retrieval16
A Teacher-Student Moment Retrieval framework to fill the cross-modal information gap during the moment retrieval guided by a single query and introduces an adaptive knowledge distillation mechanism to train a student model with a single queries input by selectively absorbing the knowledge from the teacher model.
- Malsight: Exploring Malicious Source Code and Benign Pseudocode for Iterative Binary Malware Summarization15
This work proposes Malsight, a novel code summarization framework that can iteratively generate descriptions of binary malware by exploring malicious source code and benign pseudocode and proposes a novel evaluation benchmark, BLEURT-sum, to measure the quality of summaries.
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- Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks12
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- HGMAN: Multi-Hop and Multi-Answer Question Answering Based on Heterogeneous Knowledge Graph (Student Abstract)7
A novel model that converts the ranking of candidate answers into individual predictions for each candidate, named HGMAN, which is capable of capturing more informative representations for relations assisted by the authors' heterogeneous graph, which consists of multiple entity nodes and relation nodes.
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- Integrating Subgraph-Aware Relation and Direction Reasoning for Question Answering3
A novel neural model is proposed, Relation-updated Direction-guided Answer Selector (RDAS), which converts relations in each subgraph to additional nodes to learn structure information and utilizes direction information to enhance the reasoning ability.
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- Auditing unauthorized training data from AI generated content using information isotopes1
This work proposes an information-isotope tracing framework that selectively marks target data elements and detects their propagation in model outputs, providing concrete evidence of data utilization under black-box access.
- Diagnosing Knowledge Conflict in Multimodal Long-Chain Reasoning1
This work formalizes these failures under a unified notion of knowledge conflict, distinguishing input-level objective conflict from process-level effective conflict and provides a mechanism-level view of multimodal reasoning under knowledge conflict and enable principled diagnosis and control of long-CoT failures.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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