29 citations · 163 across the 56 of their papers we have counts for
11 papers · 1 filter
Consensus Graph Representation Learning for Better Grounded Image Captioning
Wenqiao Zhang, Haochen Shi, Siliang Tang +3
The contemporary visual captioning models frequently hallucinate objects that are not actually in a scene, due to the visual misclassification or over-reliance on priors that resul…
Self-Supervised Class Incremental Learning
Zixuan Ni, Siliang Tang, Yueting Zhuang
Existing Class Incremental Learning (CIL) methods are based on a supervised classification framework sensitive to data labels. When updating them based on the new class data, they…
Towards Communication-Efficient and Privacy-Preserving Federated Representation Learning
Haizhou Shi, Youcai Zhang, Zijin Shen +4
This paper investigates the feasibility of federated representation learning under the constraints of communication cost and privacy protection. Existing works either conduct annot…
Adaptive Hierarchical Graph Reasoning with Semantic Coherence for Video-and-Language Inference
Juncheng Li, Siliang Tang, Linchao Zhu +5
Video-and-Language Inference is a recently proposed task for joint video-and-language understanding. This new task requires a model to draw inference on whether a natural language…
On the Efficacy of Small Self-Supervised Contrastive Models without Distillation Signals
Haizhou Shi, Youcai Zhang, Siliang Tang +4
It is a consensus that small models perform quite poorly under the paradigm of self-supervised contrastive learning. Existing methods usually adopt a large off-the-shelf model to t…
Revisiting Catastrophic Forgetting in Class Incremental Learning
Zixuan Ni, Haizhou Shi, Siliang Tang +3
Although the concept of catastrophic forgetting is straightforward, there is a lack of study on its causes. In this paper, we systematically explore and reveal three causes for cat…
CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction
Tao Chen, Haizhou Shi, Siliang Tang +3
The journey of reducing noise from distant supervision (DS) generated training data has been started since the DS was first introduced into the relation extraction (RE) task. For t…
Empower Distantly Supervised Relation Extraction with Collaborative Adversarial Training
Tao Chen, Haochen Shi, Liyuan Liu +4
With recent advances in distantly supervised (DS) relation extraction (RE), considerable attention is attracted to leverage multi-instance learning (MIL) to distill high-quality su…
To be a fast adaptive learner: using game history to defeat opponents
Guangzhao Cheng, Siliang Tang
In many real-world games, such as traders repeatedly bargaining with customers, it is very hard for a single AI trader to make good deals with various customers in a few turns, sin…
Disentangled Motif-aware Graph Learning for Phrase Grounding
Zongshen Mu, Siliang Tang, Jie Tan +2
In this paper, we propose a novel graph learning framework for phrase grounding in the image. Developing from the sequential to the dense graph model, existing works capture coarse…
Improving Weakly-supervised Object Localization via Causal Intervention
Feifei Shao, Yawei Luo, Li Zhang +4
The recent emerged weakly supervised object localization (WSOL) methods can learn to localize an object in the image only using image-level labels. Previous works endeavor to perce…