26 citations · 76 across the 13 of their papers we have counts for
13 papers
Incremental Human-Object Interaction Detection with Invariant Relation Representation Learning
Yana Wei, Zeen Chi, Chongyu Wang +4
In open-world environments, human-object interactions (HOIs) evolve continuously, challenging conventional closed-world HOI detection models. Inspired by humans' ability to progres…
Just Say What You Want: Only-prompting Self-rewarding Online Preference Optimization
Ruijie Xu, Zhihan Liu, Yongfei Liu +4
We address the challenge of online Reinforcement Learning from Human Feedback (RLHF) with a focus on self-rewarding alignment methods. In online RLHF, obtaining feedback requires i…
Grounded Image Text Matching with Mismatched Relation Reasoning
Yu Wu, Yana Wei, Haozhe Wang +3
This paper introduces Grounded Image Text Matching with Mismatched Relation (GITM-MR), a novel visual-linguistic joint task that evaluates the relation understanding capabilities o…
Weakly-supervised HOI Detection via Prior-guided Bi-level Representation Learning
Bo Wan, Yongfei Liu, Desen Zhou +2
Human object interaction (HOI) detection plays a crucial role in human-centric scene understanding and serves as a fundamental building-block for many vision tasks. One generalizab…
HOICLIP: Efficient Knowledge Transfer for HOI Detection with Vision-Language Models
Shan Ning, Longtian Qiu, Yongfei Liu +1
Human-Object Interaction (HOI) detection aims to localize human-object pairs and recognize their interactions. Recently, Contrastive Language-Image Pre-training (CLIP) has shown gr…
VL-InterpreT: An Interactive Visualization Tool for Interpreting Vision-Language Transformers
Estelle Aflalo, Meng Du, Shao-Yen Tseng +4
Breakthroughs in transformer-based models have revolutionized not only the NLP field, but also vision and multimodal systems. However, although visualization and interpretability t…
Cascaded Sparse Feature Propagation Network for Interactive Segmentation
Chuyu Zhang, Chuanyang Hu, Hui Ren +2
We aim to tackle the problem of point-based interactive segmentation, in which the key challenge is to propagate the user-provided annotations to unlabeled regions efficiently. Exi…
KD-VLP: Improving End-to-End Vision-and-Language Pretraining with Object Knowledge Distillation
Yongfei Liu, Chenfei Wu, Shao-yen Tseng +3
Self-supervised vision-and-language pretraining (VLP) aims to learn transferable multi-modal representations from large-scale image-text data and to achieve strong performances on…
GEM: A General Evaluation Benchmark for Multimodal Tasks
Lin Su, Nan Duan, Edward Cui +7
In this paper, we present GEM as a General Evaluation benchmark for Multimodal tasks. Different from existing datasets such as GLUE, SuperGLUE, XGLUE and XTREME that mainly focus o…
Relation-aware Instance Refinement for Weakly Supervised Visual Grounding
Yongfei Liu, Bo Wan, Lin Ma +1
Visual grounding, which aims to build a correspondence between visual objects and their language entities, plays a key role in cross-modal scene understanding. One promising and sc…
Part-aware Prototype Network for Few-shot Semantic Segmentation
Yongfei Liu, Xiangyi Zhang, Songyang Zhang +1
Few-shot semantic segmentation aims to learn to segment new object classes with only a few annotated examples, which has a wide range of real-world applications. Most existing meth…
Learning Cross-modal Context Graph for Visual Grounding
Yongfei Liu, Bo Wan, Xiaodan Zhu +1
Visual grounding is a ubiquitous building block in many vision-language tasks and yet remains challenging due to large variations in visual and linguistic features of grounding ent…
Pose-aware Multi-level Feature Network for Human Object Interaction Detection
Bo Wan, Desen Zhou, Yongfei Liu +2
Reasoning human object interactions is a core problem in human-centric scene understanding and detecting such relations poses a unique challenge to vision systems due to large vari…