100 citations · 173 across the 11 of their papers we have counts for
10 papers · 1 filter
Exploring Hallucination of Large Multimodal Models in Video Understanding: Benchmark, Analysis and Mitigation
Hongcheng Gao, Jiashu Qu, Jingyi Tang +6
The hallucination of large multimodal models (LMMs), providing responses that appear correct but are actually incorrect, limits their reliability and applicability. This paper aims…
Query-centric Audio-Visual Cognition Network for Moment Retrieval, Segmentation and Step-Captioning
Yunbin Tu, Liang Li, Li Su +1
Video has emerged as a favored multimedia format on the internet. To better gain video contents, a new topic HIREST is presented, including video retrieval, moment retrieval, momen…
Distractors-Immune Representation Learning with Cross-modal Contrastive Regularization for Change Captioning
Yunbin Tu, Liang Li, Li Su +2
Change captioning aims to succinctly describe the semantic change between a pair of similar images, while being immune to distractors (illumination and viewpoint changes). Under th…
Context-aware Difference Distilling for Multi-change Captioning
Yunbin Tu, Liang Li, Li Su +3
Multi-change captioning aims to describe complex and coupled changes within an image pair in natural language. Compared with single-change captioning, this task requires the model…
Self-supervised Cross-view Representation Reconstruction for Change Captioning
Yunbin Tu, Liang Li, Li Su +3
Change captioning aims to describe the difference between a pair of similar images. Its key challenge is how to learn a stable difference representation under pseudo changes caused…
Neighborhood Contrastive Transformer for Change Captioning
Yunbin Tu, Liang Li, Li Su +2
Change captioning is to describe the semantic change between a pair of similar images in natural language. It is more challenging than general image captioning, because it requires…
Modeling Temporal Concept Receptive Field Dynamically for Untrimmed Video Analysis
Zhaobo Qi, Shuhui Wang, Chi Su +3
Event analysis in untrimmed videos has attracted increasing attention due to the application of cutting-edge techniques such as CNN. As a well studied property for CNN-based models…
Self-Regulated Learning for Egocentric Video Activity Anticipation
Zhaobo Qi, Shuhui Wang, Chi Su +3
Future activity anticipation is a challenging problem in egocentric vision. As a standard future activity anticipation paradigm, recursive sequence prediction suffers from the accu…
Knowledge-guided Pairwise Reconstruction Network for Weakly Supervised Referring Expression Grounding
Xuejing Liu, Liang Li, Shuhui Wang +3
Weakly supervised referring expression grounding (REG) aims at localizing the referential entity in an image according to linguistic query, where the mapping between the image regi…
Cascaded Partial Decoder for Fast and Accurate Salient Object Detection
Zhe Wu, Li Su, Qingming Huang
Existing state-of-the-art salient object detection networks rely on aggregating multi-level features of pre-trained convolutional neural networks (CNNs). Compared to high-level fea…