44 citations · 118 across the 11 of their papers we have counts for
11 papers
Prompt Switch: Efficient CLIP Adaptation for Text-Video Retrieval
Chaorui Deng, Qi Chen, Pengda Qin +2
In text-video retrieval, recent works have benefited from the powerful learning capabilities of pre-trained text-image foundation models (e.g., CLIP) by adapting them to the video…
Image Background Serves as Good Proxy for Out-of-distribution Data
Sen Pei
Out-of-distribution (OOD) detection empowers the model trained on the closed image set to identify unknown data in the open world. Though many prior techniques have yielded conside…
Synthesizing Coherent Story with Auto-Regressive Latent Diffusion Models
Xichen Pan, Pengda Qin, Yuhong Li +2
Conditioned diffusion models have demonstrated state-of-the-art text-to-image synthesis capacity. Recently, most works focus on synthesizing independent images; While for real-worl…
InfoBehavior: Self-supervised Representation Learning for Ultra-long Behavior Sequence via Hierarchical Grouping
Runshi Liu, Pengda Qin, Yuhong Li +4
E-commerce companies have to face abnormal sellers who sell potentially-risky products. Typically, the risk can be identified by jointly considering product content (e.g., title an…
TVDIM: Enhancing Image Self-Supervised Pretraining via Noisy Text Data
Pengda Qin, Yuhong Li, Kefeng Deng +1
Among ubiquitous multimodal data in the real world, text is the modality generated by human, while image reflects the physical world honestly. In a visual understanding application…
Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs
Pengda Qin, Xin Wang, Wenhu Chen +3
Large-scale knowledge graphs (KGs) are shown to become more important in current information systems. To expand the coverage of KGs, previous studies on knowledge graph completion…
Deep Reinforcement Learning with Distributional Semantic Rewards for Abstractive Summarization
Siyao Li, Deren Lei, Pengda Qin +1
Deep reinforcement learning (RL) has been a commonly-used strategy for the abstractive summarization task to address both the exposure bias and non-differentiable task issues. Howe…
Multi-Task Self-Supervised Learning for Disfluency Detection
Shaolei Wang, Wanxiang Che, Qi Liu +3
Most existing approaches to disfluency detection heavily rely on human-annotated data, which is expensive to obtain in practice. To tackle the training data bottleneck, we investig…
Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention
Wenhu Chen, Jianshu Chen, Pengda Qin +2
Semantically controlled neural response generation on limited-domain has achieved great performance. However, moving towards multi-domain large-scale scenarios are shown to be diff…
DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction
Pengda Qin, Weiran Xu, William Yang Wang
Distant supervision can effectively label data for relation extraction, but suffers from the noise labeling problem. Recent works mainly perform soft bag-level noise reduction stra…
Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning
Pengda Qin, Weiran Xu, William Yang Wang
Distant supervision has become the standard method for relation extraction. However, even though it is an efficient method, it does not come at no cost---The resulted distantly-sup…