166 citations · 644 across the 58 of their papers we have counts for
14 papers · 1 filter
Relational Graph Learning for Grounded Video Description Generation
Wenqiao Zhang, Xin Eric Wang, Siliang Tang +5
Grounded video description (GVD) encourages captioning models to attend to appropriate video regions (e.g., objects) dynamically and generate a description. Such a setting can help…
MAGIC: Multimodal relAtional Graph adversarIal inferenCe for Diverse and Unpaired Text-based Image Captioning
Wenqiao Zhang, Haochen Shi, Jiannan Guo +5
Text-based image captioning (TextCap) requires simultaneous comprehension of visual content and reading the text of images to generate a natural language description. Although a ta…
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…
Natural Language Video Localization with Learnable Moment Proposals
Shaoning Xiao, Long Chen, Jian Shao +2
Given an untrimmed video and a natural language query, Natural Language Video Localization (NLVL) aims to identify the video moment described by the query. To address this task, ex…
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…
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…
A Sequence-to-Set Network for Nested Named Entity Recognition
Zeqi Tan, Yongliang Shen, Shuai Zhang +2
Named entity recognition (NER) is a widely studied task in natural language processing. Recently, a growing number of studies have focused on the nested NER. The span-based methods…
Deep Learning for Weakly-Supervised Object Detection and Object Localization: A Survey
Feifei Shao, Long Chen, Jian Shao +5
Weakly-Supervised Object Detection (WSOD) and Localization (WSOL), i.e., detecting multiple and single instances with bounding boxes in an image using image-level labels, are long-…
VL-NMS: Breaking Proposal Bottlenecks in Two-Stage Visual-Language Matching
Chenchi Zhang, Wenbo Ma, Jun Xiao +4
The prevailing framework for matching multimodal inputs is based on a two-stage process: 1) detecting proposals with an object detector and 2) matching text queries with proposals.…
Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation
Weijie Chen, Luojun Lin, Shicai Yang +4
It is a strong prerequisite to access source data freely in many existing unsupervised domain adaptation approaches. However, source data is agnostic in many practical scenarios du…
Box Re-Ranking: Unsupervised False Positive Suppression for Domain Adaptive Pedestrian Detection
Weijie Chen, Yilu Guo, Shicai Yang +7
False positive is one of the most serious problems brought by agnostic domain shift in domain adaptive pedestrian detection. However, it is impossible to label each box in countles…