collaborators

6 papers

cs.RO2026

History-Conditioned Spatio-Temporal Visual Token Pruning for Efficient Vision-Language Navigation

Qitong Wang, Yijun Liang, Ming Li +2

Vision-Language Navigation (VLN) enables robots to follow natural-language instructions in visually grounded environments, serving as a key capability for embodied robotic systems.…

cs.RO2026

Toward Low-Latency Vision-Language Models with Doubly-Correct Predictions in Egocentric Visual Understanding

Qitong Wang, Fan Du, Pranav Maneriker +2

The rapid rise of Vision-Language Models (VLMs) in egocentric visual understanding has made low-latency inference in human-robot collaborative (HRC) tasks increasingly critical. We…

cs.LG2026

IceCache: Memory-efficient KV-cache Management for Long-Sequence LLMs

Yuzhen Mao, Qitong Wang, Martin Ester +1

Key-Value (KV) cache plays a crucial role in accelerating inference in large language models (LLMs) by storing intermediate attention states and avoiding redundant computation duri…

cs.LG2026

When One Modality Rules Them All: Backdoor Modality Collapse in Multimodal Diffusion Models

Qitong Wang, Haoran Dai, Haotian Zhang +2

While diffusion models have revolutionized visual content generation, their rapid adoption has underscored the critical need to investigate vulnerabilities, e.g., to backdoor attac…

cs.CL2026

Decoder-based Sense Knowledge Distillation

Qitong Wang, Mohammed J. Zaki, Georgios Kollias +1

Large language models (LLMs) learn contextual embeddings that capture rich semantic information, yet they often overlook structured lexical knowledge such as word senses and relati…

cs.LG2025

Beyond Accuracy: On the Effects of Fine-tuning Towards Vision-Language Model's Prediction Rationality

Qitong Wang, Tang Li, Kien X. Nguyen +1

Vision-Language Models (VLMs), such as CLIP, have already seen widespread applications. Researchers actively engage in further fine-tuning VLMs in safety-critical domains. In these…