3 citations · 4 across the 10 of their papers we have counts for
10 papers
HeadHunt-VAD: Hunting Robust Anomaly-Sensitive Heads in MLLM for Tuning-Free Video Anomaly Detection
Zhaolin Cai, Fan Li, Ziwei Zheng +2
Video Anomaly Detection (VAD) aims to locate events that deviate from normal patterns in videos. Traditional approaches often rely on extensive labeled data and incur high computat…
HiProbe-VAD: Video Anomaly Detection via Hidden States Probing in Tuning-Free Multimodal LLMs
Zhaolin Cai, Fan Li, Ziwei Zheng +1
Video Anomaly Detection (VAD) aims to identify and locate deviations from normal patterns in video sequences. Traditional methods often struggle with substantial computational dema…
Seeing It or Not? Interpretable Vision-aware Latent Steering to Mitigate Object Hallucinations
Boxu Chen, Ziwei Zheng, Le Yang +4
Large Vision-Language Models (LVLMs) have achieved remarkable success but continue to struggle with object hallucination (OH), generating outputs inconsistent with visual inputs. W…
DeepEyes: Incentivizing "Thinking with Images" via Reinforcement Learning
Ziwei Zheng, Michael Yang, Jack Hong +5
Large Vision-Language Models excel at multimodal understanding but struggle to deeply integrate visual information into their predominantly text-based reasoning processes, a key ch…
Spot Risks Before Speaking! Unraveling Safety Attention Heads in Large Vision-Language Models
Ziwei Zheng, Junyao Zhao, Le Yang +2
With the integration of an additional modality, large vision-language models (LVLMs) exhibit greater vulnerability to safety risks (e.g., jailbreaking) compared to their language-o…
Nullu: Mitigating Object Hallucinations in Large Vision-Language Models via HalluSpace Projection
Le Yang, Ziwei Zheng, Boxu Chen +3
Recent studies have shown that large vision-language models (LVLMs) often suffer from the issue of object hallucinations (OH). To mitigate this issue, we introduce an efficient met…
OStr-DARTS: Differentiable Neural Architecture Search based on Operation Strength
Le Yang, Ziwei Zheng, Yizeng Han +3
Differentiable architecture search (DARTS) has emerged as a promising technique for effective neural architecture search, and it mainly contains two steps to find the high-performa…
Rethinking the Architecture Design for Efficient Generic Event Boundary Detection
Ziwei Zheng, Zechuan Zhang, Yulin Wang +3
Generic event boundary detection (GEBD), inspired by human visual cognitive behaviors of consistently segmenting videos into meaningful temporal chunks, finds utility in various ap…
Fine-grained Dynamic Network for Generic Event Boundary Detection
Ziwei Zheng, Lijun He, Le Yang +1
Generic event boundary detection (GEBD) aims at pinpointing event boundaries naturally perceived by humans, playing a crucial role in understanding long-form videos. Given the dive…
DyFADet: Dynamic Feature Aggregation for Temporal Action Detection
Le Yang, Ziwei Zheng, Yizeng Han +4
Recent proposed neural network-based Temporal Action Detection (TAD) models are inherently limited to extracting the discriminative representations and modeling action instances wi…