activity
20162025
most citedVisual Relationship Detection with Language Priors

111 citations · 216 across the 29 of their papers we have counts for

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Showing 2024Show all

10 papers · 1 filter

cs.CV2024

Perception Tokens Enhance Visual Reasoning in Multimodal Language Models

Mahtab Bigverdi, Zelun Luo, Cheng-Yu Hsieh +4

Multimodal language models (MLMs) still face challenges in fundamental visual perception tasks where specialized models excel. Tasks requiring reasoning about 3D structures benefit…

cs.CV2024

Negative Token Merging: Image-based Adversarial Feature Guidance

Jaskirat Singh, Lindsey Li, Weijia Shi +7

Text-based adversarial guidance using a negative prompt has emerged as a widely adopted approach to steer diffusion models away from producing undesired concepts. While useful, per…

cs.RO20242 cited

I Can Tell What I am Doing: Toward Real-World Natural Language Grounding of Robot Experiences

Zihan Wang, Brian Liang, Varad Dhat +4

Understanding robot behaviors and experiences through natural language is crucial for developing intelligent and transparent robotic systems. Recent advancement in large language m…

cs.CV2024

ActionAtlas: A VideoQA Benchmark for Domain-specialized Action Recognition

Mohammadreza Salehi, Jae Sung Park, Tanush Yadav +5

Our world is full of varied actions and moves across specialized domains that we, as humans, strive to identify and understand. Within any single domain, actions can often appear q…

cs.CL2024

Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization

Cheng-Yu Hsieh, Yung-Sung Chuang, Chun-Liang Li +8

Large language models (LLMs), even when specifically trained to process long input contexts, struggle to capture relevant information located in the middle of their input. This phe…

cs.CL20243 cited

Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps

Yung-Sung Chuang, Linlu Qiu, Cheng-Yu Hsieh +3

When asked to summarize articles or answer questions given a passage, large language models (LLMs) can hallucinate details and respond with unsubstantiated answers that are inaccur…