19 citations · 52 across the 18 of their papers we have counts for
19 papers
Before Thinking, Learn to Decide: Proactive Routing for Efficient Visual Reasoning
Yinan Zhou, Haokun Lin, Yichen Wu +7
Large multimodal models have achieved strong reasoning on complex visual tasks, but their inference efficiency is often restricted by long chains of thought. A promising solution i…
TokLIP: Marry Visual Tokens to CLIP for Multimodal Comprehension and Generation
Haokun Lin, Teng Wang, Yixiao Ge +6
Pioneering token-based works such as Chameleon and Emu3 have established a foundation for multimodal unification but face challenges of high training computational overhead and lim…
Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models
Baoshuo Kan, Teng Wang, Wenpeng Lu +3
Pre-trained vision-language models, e.g., CLIP, working with manually designed prompts have demonstrated great capacity of transfer learning. Recently, learnable prompts achieve st…
Transferable Decoding with Visual Entities for Zero-Shot Image Captioning
Junjie Fei, Teng Wang, Jinrui Zhang +3
Image-to-text generation aims to describe images using natural language. Recently, zero-shot image captioning based on pre-trained vision-language models (VLMs) and large language…
Set-level Guidance Attack: Boosting Adversarial Transferability of Vision-Language Pre-training Models
Dong Lu, Zhiqiang Wang, Teng Wang +3
Vision-language pre-training (VLP) models have shown vulnerability to adversarial examples in multimodal tasks. Furthermore, malicious adversaries can be deliberately transferred t…
PTVD: A Large-Scale Plot-Oriented Multimodal Dataset Based on Television Dramas
Chen Li, Xutan Peng, Teng Wang +5
Art forms such as movies and television (TV) dramas are reflections of the real world, which have attracted much attention from the multimodal learning community recently. However,…
LLMVA-GEBC: Large Language Model with Video Adapter for Generic Event Boundary Captioning
Yolo Yunlong Tang, Jinrui Zhang, Xiangchen Wang +2
Our winning entry for the CVPR 2023 Generic Event Boundary Captioning (GEBC) competition is detailed in this paper. Unlike conventional video captioning tasks, GEBC demands that th…
-Tuning: Transferring Multimodal Foundation Models with Optimal Multi-task Interpolation
Chengyue Wu, Teng Wang, Yixiao Ge +4
Foundation models have achieved great advances in multi-task learning with a unified interface of unimodal and multimodal tasks. However, the potential of such multi-task learners…
Caption Anything: Interactive Image Description with Diverse Multimodal Controls
Teng Wang, Jinrui Zhang, Junjie Fei +5
Controllable image captioning is an emerging multimodal topic that aims to describe the image with natural language following human purpose, , looking at the specifi…
Accelerating Vision-Language Pretraining with Free Language Modeling
Teng Wang, Yixiao Ge, Feng Zheng +4
The state of the arts in vision-language pretraining (VLP) achieves exemplary performance but suffers from high training costs resulting from slow convergence and long training tim…
Dense-Localizing Audio-Visual Events in Untrimmed Videos: A Large-Scale Benchmark and Baseline
Tiantian Geng, Teng Wang, Jinming Duan +2
Existing audio-visual event localization (AVE) handles manually trimmed videos with only a single instance in each of them. However, this setting is unrealistic as natural videos o…
Learning Grounded Vision-Language Representation for Versatile Understanding in Untrimmed Videos
Teng Wang, Jinrui Zhang, Feng Zheng +3
Joint video-language learning has received increasing attention in recent years. However, existing works mainly focus on single or multiple trimmed video clips (events), which make…
Multi-modal Segment Assemblage Network for Ad Video Editing with Importance-Coherence Reward
Yolo Yunlong Tang, Siting Xu, Teng Wang +3
Advertisement video editing aims to automatically edit advertising videos into shorter videos while retaining coherent content and crucial information conveyed by advertisers. It m…
Exploiting Context Information for Generic Event Boundary Captioning
Jinrui Zhang, Teng Wang, Feng Zheng +2
Generic Event Boundary Captioning (GEBC) aims to generate three sentences describing the status change for a given time boundary. Previous methods only process the information of a…
VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix
Teng Wang, Wenhao Jiang, Zhichao Lu +4
Existing vision-language pre-training (VLP) methods primarily rely on paired image-text datasets, which are either annotated by enormous human labors, or crawled from the internet…
Semantic-Aware Pretraining for Dense Video Captioning
Teng Wang, Zhu Liu, Feng Zheng +3
This report describes the details of our approach for the event dense-captioning task in ActivityNet Challenge 2021. We present a semantic-aware pretraining method for dense video…
Auto-ABSA: Cross-Domain Aspect Detection and Sentiment Analysis Using Auxiliary Sentences
Teng Wang, Bolun Sun, Yijie Tong
After transformer is proposed, lots of pre-trained language models have been come up with and sentiment analysis (SA) task has been improved. In this paper, we proposed a method th…
End-to-End Dense Video Captioning with Parallel Decoding
Teng Wang, Ruimao Zhang, Zhichao Lu +3
Dense video captioning aims to generate multiple associated captions with their temporal locations from the video. Previous methods follow a sophisticated "localize-then-describe"…
Dense-Captioning Events in Videos: SYSU Submission to ActivityNet Challenge 2020
Teng Wang, Huicheng Zheng, Mingjing Yu
This technical report presents a brief description of our submission to the dense video captioning task of ActivityNet Challenge 2020. Our approach follows a two-stage pipeline: fi…