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Zhenzhen Hu

7 accepted papers

2026

Agent Journey Beyond RGB: Hierarchical Semantic-Spatial Representation Enrichment for Vision-and-Language Navigation

AAAI 2026technical

Navigating unseen environments based on natural language instructions remains difficult for egocentric agents in Vision-and-Language Navigation (VLN). Intuitively, humans inherently ground concrete semantic knowledge within spatial layouts during indoor navigation. Although previous studies have i

Cited by 0SourcePDFScholar
2025

Meta-Reinforcement Learning With Evolving Gradient Regularization

RA-L 2025

Deep reinforcement learning (DRL) typically requires reinitializing training for new tasks, limiting its generalization due to isolated knowledge transfer. Meta-reinforcement learning (Meta-RL) addresses this by enabling rapid adaptation through prior task experiences, yet existing gradient-based me

Cited by 1SourceScholar
2025

Rebalancing Contrastive Alignment with Bottlenecked Semantic Increments in Text-Video Retrieval

NeurIPS 2025poster

Recent progress in text–video retrieval has been largely driven by contrastive learning. However, existing methods often overlook the effect of the modality gap, which causes anchor representations to undergo in-place optimization (i.e., optimization tension) that limits their alignment capacity. Mo…

Cited by 0SourceScholar
2025

Text Proxy: Decomposing Retrieval from a 1-to-N Relationship into N 1-to-1 Relationships for Text-Video Retrieval

AAAI 2025technical

Text-video retrieval (TVR) has seen substantial advancements in recent years, fueled by the utilization of pre-trained models and large language models (LLMs). Despite these advancements, achieving accurate matching in TVR remains challenging due to inherent disparities between video and textual mod…

2024

Offline Meta-Reinforcement Learning with Evolving Gradient Agreement

IROS 2024poster

Meta-Reinforcement Learning (Meta-RL) is a machine learning paradigm aimed at learning reinforcement learning policies that can quickly adapt to unseen tasks with few-shot data. Nevertheless, applying Meta-RL to real-world applications faces challenges due to the cost of data acquisition. To address…

Cited by 0SourceScholar
2020

More Grounded Image Captioning by Distilling Image-Text Matching Model

CVPR 2020poster

Visual attention not only improves the performance of image captioners, but also serves as a visual interpretation to qualitatively measure the caption rationality and model transparency. Specifically, we expect that a captioner can fix its attentive gaze on the correct objects while generating the…

Cited by 179PDFcodeScholar