177 citations · 826 across the 39 of their papers we have counts for
40 papers
Less is More: Removing Text-regions Improves CLIP Training Efficiency and Robustness
Liangliang Cao, Bowen Zhang, Chen Chen +5
The CLIP (Contrastive Language-Image Pre-training) model and its variants are becoming the de facto backbone in many applications. However, training a CLIP model from hundreds of m…
STAIR: Learning Sparse Text and Image Representation in Grounded Tokens
Chen Chen, Bowen Zhang, Liangliang Cao +7
Image and text retrieval is one of the foundational tasks in the vision and language domain with multiple real-world applications. State-of-the-art approaches, e.g. CLIP, ALIGN, re…
Exploiting Category Names for Few-Shot Classification with Vision-Language Models
Taihong Xiao, Zirui Wang, Liangliang Cao +3
Vision-language foundation models pretrained on large-scale data provide a powerful tool for many visual understanding tasks. Notably, many vision-language models build two encoder…
Input Length Matters: Improving RNN-T and MWER Training for Long-form Telephony Speech Recognition
Zhiyun Lu, Yanwei Pan, Thibault Doutre +5
End-to-end models have achieved state-of-the-art results on several automatic speech recognition tasks. However, they perform poorly when evaluated on long-form data, e.g., minutes…
Improving Confidence Estimation on Out-of-Domain Data for End-to-End Speech Recognition
Qiujia Li, Yu Zhang, David Qiu +3
As end-to-end automatic speech recognition (ASR) models reach promising performance, various downstream tasks rely on good confidence estimators for these systems. Recent research…
BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition
Yu Zhang, Daniel S. Park, Wei Han +23
We summarize the results of a host of efforts using giant automatic speech recognition (ASR) models pre-trained using large, diverse unlabeled datasets containing approximately a m…
Multi-Task Learning for End-to-End ASR Word and Utterance Confidence with Deletion Prediction
David Qiu, Yanzhang He, Qiujia Li +3
Confidence scores are very useful for downstream applications of automatic speech recognition (ASR) systems. Recent works have proposed using neural networks to learn word or utter…
Bridging the gap between streaming and non-streaming ASR systems bydistilling ensembles of CTC and RNN-T models
Thibault Doutre, Wei Han, Chung-Cheng Chiu +3
Streaming end-to-end automatic speech recognition (ASR) systems are widely used in everyday applications that require transcribing speech to text in real-time. Their minimal latenc…
Exploring Targeted Universal Adversarial Perturbations to End-to-end ASR Models
Zhiyun Lu, Wei Han, Yu Zhang +1
Although end-to-end automatic speech recognition (e2e ASR) models are widely deployed in many applications, there have been very few studies to understand models' robustness agains…
Learning Word-Level Confidence For Subword End-to-End ASR
David Qiu, Qiujia Li, Yanzhang He +9
We study the problem of word-level confidence estimation in subword-based end-to-end (E2E) models for automatic speech recognition (ASR). Although prior works have proposed trainin…
Residual Energy-Based Models for End-to-End Speech Recognition
Qiujia Li, Yu Zhang, Bo Li +2
End-to-end models with auto-regressive decoders have shown impressive results for automatic speech recognition (ASR). These models formulate the sequence-level probability as a pro…
Spatial-Temporal Alignment Network for Action Recognition and Detection
Junwei Liang, Liangliang Cao, Xuehan Xiong +2
This paper studies how to introduce viewpoint-invariant feature representations that can help action recognition and detection. Although we have witnessed great progress of action…
Confidence Estimation for Attention-based Sequence-to-sequence Models for Speech Recognition
Qiujia Li, David Qiu, Yu Zhang +5
For various speech-related tasks, confidence scores from a speech recogniser are a useful measure to assess the quality of transcriptions. In traditional hidden Markov model-based…
Zero-shot Entity Linking with Efficient Long Range Sequence Modeling
Zonghai Yao, Liangliang Cao, Huapu Pan
This paper considers the problem of zero-shot entity linking, in which a link in the test time may not present in training. Following the prevailing BERT-based research efforts, we…
Improving Streaming Automatic Speech Recognition With Non-Streaming Model Distillation On Unsupervised Data
Thibault Doutre, Wei Han, Min Ma +7
Streaming end-to-end automatic speech recognition (ASR) models are widely used on smart speakers and on-device applications. Since these models are expected to transcribe speech wi…
RNN-T Models Fail to Generalize to Out-of-Domain Audio: Causes and Solutions
Chung-Cheng Chiu, Arun Narayanan, Wei Han +8
In recent years, all-neural end-to-end approaches have obtained state-of-the-art results on several challenging automatic speech recognition (ASR) tasks. However, most existing wor…
Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions
Matheus Gadelha, Aruni RoyChowdhury, Gopal Sharma +5
The problems of shape classification and part segmentation from 3D point clouds have garnered increasing attention in the last few years. Both of these problems, however, suffer fr…
Progressive Learning Algorithm for Efficient Person Re-Identification
Zhen Li, Hanyang Shao, Nian Xue +2
This paper studies the problem of Person Re-Identification (ReID)for large-scale applications. Recent research efforts have been devoted to building complicated part models, which…
Speech Sentiment Analysis via Pre-trained Features from End-to-end ASR Models
Zhiyun Lu, Liangliang Cao, Yu Zhang +2
In this paper, we propose to use pre-trained features from end-to-end ASR models to solve speech sentiment analysis as a down-stream task. We show that end-to-end ASR features, whi…
Product Image Recognition with Guidance Learning and Noisy Supervision
Qing Li, Xiaojiang Peng, Liangliang Cao +3
This paper considers recognizing products from daily photos, which is an important problem in real-world applications but also challenging due to background clutters, category dive…
Accurate and Robust Pulmonary Nodule Detection by 3D Feature Pyramid Network with Self-supervised Feature Learning
Jingya Liu, Liangliang Cao, Oguz Akin +1
Accurate detection of pulmonary nodules with high sensitivity and specificity is essential for automatic lung cancer diagnosis from CT scans. Although many deep learning-based algo…
3DFPN-HS: 3D Feature Pyramid Network Based High Sensitivity and Specificity Pulmonary Nodule Detection
Jingya Liu, Liangliang Cao, Oguz Akin +1
Accurate detection of pulmonary nodules with high sensitivity and specificity is essential for automatic lung cancer diagnosis from CT scans. Although many deep learning-based algo…
Automatic adaptation of object detectors to new domains using self-training
Aruni RoyChowdhury, Prithvijit Chakrabarty, Ashish Singh +4
This work addresses the unsupervised adaptation of an existing object detector to a new target domain. We assume that a large number of unlabeled videos from this domain are readil…
Learning Deterministic Policy with Target for Power Control in Wireless Networks
Yujiao Lu, Hancheng Lu, Liangliang Cao +2
Inter-Cell Interference Coordination (ICIC) is a promising way to improve energy efficiency in wireless networks, especially where small base stations are densely deployed. However…