5 citations · 6 across the 12 of their papers we have counts for
13 papers · 1 filter
Companion-style QA Assistance in Ego-Vision
Hangyu Qin, Junbin Xiao, Shenglang Zhang +1
AI companions are envisioned as always-on assistants that support users in daily life. With this regard, we introduce BuddyVQA, a benchmark for companion-style question answering (…
Keep It in Mind: User Centric Continual Spatial Intelligence Reasoning in Egocentric Video Streams
Yun Wang, Junbin Xiao, Han Lyu +6
We introduce UCS-Bench, a dataset spanning 170+ hours of egocentric visual observations with 8.1K+ timestamped questions for diagnosing User-Centric Continual Spatial intelligence…
MuKV: Multi-Grained KV Cache Compression for Long Streaming Video Question-Answering
Junbin Xiao, Jiajun Chen, Tianxiang Sun +2
Long streaming video QA remains challenging due to growing visual tokens and limited reasoning length of large language models (LLMs). KV-caching stores the Key-Value (KV) of the h…
Ego-Grounding for Personalized Question-Answering in Egocentric Videos
Junbin Xiao, Shenglang Zhang, Pengxiang Zhu +1
We present the first systematic analysis of multimodal large language models (MLLMs) in personalized question-answering requiring ego-grounding - the ability to understand the came…
EgoExo-Con: Exploring View-Invariant Video Temporal Understanding
Minjoon Jung, Junbin Xiao, Junghyun Kim +2
Do Video-LLMs have consistent temporal understanding when videos capture the same event from different viewpoints? To study this question, we introduce EgoExo-Con(sistency), a benc…
Visual Intention Grounding for Egocentric Assistants
Pengzhan Sun, Junbin Xiao, Tze Ho Elden Tse +3
Visual grounding associates textual descriptions with objects in an image. Conventional methods target third-person image inputs and named object queries. In applications such as A…
EgoTextVQA: Towards Egocentric Scene-Text Aware Video Question Answering
Sheng Zhou, Junbin Xiao, Qingyun Li +6
We introduce EgoTextVQA, a novel and rigorously constructed benchmark for egocentric QA assistance involving scene text. EgoTextVQA contains 1.5K ego-view videos and 7K scene-text…
EgoBlind: Towards Egocentric Visual Assistance for the Blind
Junbin Xiao, Nanxin Huang, Hao Qiu +5
We present EgoBlind, the first egocentric VideoQA dataset collected from blind individuals to evaluate the assistive capabilities of contemporary multimodal large language models (…
On the Consistency of Video Large Language Models in Temporal Comprehension
Minjoon Jung, Junbin Xiao, Byoung-Tak Zhang +1
Video large language models (Video-LLMs) can temporally ground language queries and retrieve video moments. Yet, such temporal comprehension capabilities are neither well-studied n…
Scene-Text Grounding for Text-Based Video Question Answering
Sheng Zhou, Junbin Xiao, Xun Yang +5
Existing efforts in text-based video question answering (TextVideoQA) are criticized for their opaque decisionmaking and heavy reliance on scene-text recognition. In this paper, we…
Question-Answering Dense Video Events
Hangyu Qin, Junbin Xiao, Angela Yao
This paper presents question-answering on dense video events, a novel task that answers and grounds dense-event questions in long videos, thus challenging MLLMs to faithfully compr…
VideoQA in the Era of LLMs: An Empirical Study
Junbin Xiao, Nanxin Huang, Hangyu Qin +8
Video Large Language Models (Video-LLMs) are flourishing and has advanced many video-language tasks. As a golden testbed, Video Question Answering (VideoQA) plays pivotal role in V…
Can I Trust Your Answer? Visually Grounded Video Question Answering
Junbin Xiao, Angela Yao, Yicong Li +1
We study visually grounded VideoQA in response to the emerging trends of utilizing pretraining techniques for video-language understanding. Specifically, by forcing vision-language…