Jinfeng Bai

Semi-supervised learning algorithms that use pseudo-labeling have become increasingly popular for improving model performance by utilizing both labeled and unlabeled data. In this paper, we offer a fresh perspective on the selection of pseudo-labels, inspired by theoretical insights. We suggest that pseudo-labels with a high degree of local variance are more prone to inaccuracies. Based on this premise, we introduce the Local Variance Match (LVM) method, which aims to optimize the selection of pseudo-labels in semi-supervised learning (SSL) tasks. Our methodology is validated through a series of experiments on widely-used image classification datasets, such as CIFAR-10, CIFAR-100, and SVHN, spanning various labeled data quantity scenarios. The empirical findings show that the LVM method substantially outpaces current SSL techniques, achieving state-of-the-art results in many of these scenarios. For instance, we observed an error rate of 5.41% on CIFAR-10 with a single label for each class, 35.87% on CIFAR-100 when using four labels per class, and 1.94% on SVHN with four labels for each class. Notably, the standout error rate of 5.41% is less than 1% shy of the performance in a fully-supervised learning environment. In experiments on ImageNet with 100k labeled data, the LVM also reached state-of-the-art outcomes. Additionally, the efficacy of the LVM method is further validated by its stellar performance in speech recognition experiments.

New NLP benchmarks are urgently needed to align with the rapid development of large language models (LLMs). We present a meticulously designed evaluation benchmark that leverages the knowledge graph. This evaluation comprises 584 level-1 knowledge points and 1,989 level-2 knowledge points, thereby encompassing a comprehensive spectrum of the K12 education domain knowledge. The primary objective is to comprehensively assess the high-level comprehension aptitude and reasoning capabilities of LLMs operating within the Chinese context. Our evaluation incorporates five distinct question types with 39,452 questions. We test the current mainstream LLMs by three distinct modes. Firstly, four prompt evaluation modes were employed to assess the fundamental capacity. Additionally, for choice questions, a result-oriented evaluation approach was designed through data augmentation to assess the model's proficiency in advanced knowledge and reasoning. Moreover, a subset with reasoning process is derived, and the process-oriented testing method is used to test the model's interpretability and higher-order reasoning capacity. We further show models' capability in our knowledge points, and anticipate the evaluation can assist in the assessment of the strengths and deficiencies of LLMs on knowledge points, thus fostering their development within the Chinese context. Our Dataset will be publicly available in https://github.com/tal-tech/chinese-k12-evaluation.

Language identification (LID) involves automatically determining the language being spoken in a given segment. In code-switching speech, there are rapid switches between two or more languages within a single conversation. Despite LID attaining high accuracy on medium or long utterances, the performance on short utterances of code-switching speech is currently unsatisfactory. We propose a bidirectional encoder representation from transformers (BERT)-based LID system (BERT-LID) to enhance LID performance, especially for short-duration code-switching speech segments. The original BERT model is expanded by incorporating phonetic posterior grams (PPGs) extracted from a front-end phone recognizer as input. Then it is followed by the deployment of an optimal deep classifier for LID. Our BERT-LID model demonstrates a significant improvement of approximately 6.5% in accuracy for long-segment identification and 19.9% for short-segment identification, thereby demonstrating its effectiveness in code-switching speech LID tasks.

Nowadays, neural vocoders are preferred for their ability to synthesize high-fidelity audio. However, training a neural vocoder requires a massive corpus of high-quality real audio, and the audio recording process is often labor-intensive. In this work, we propose a synthetic corpus generation method for neural vocoder training, which can easily generate synthetic audio with an unlimited number at nearly no cost. We explicitly model the prior characteristics of audio from multiple target domains simultaneously (e.g., speeches, singing voices, and instrumental pieces) to equip the generated audio data with these characteristics. And we show that our synthetic corpus allows the neural vocoder to achieve competitive results without any real audio in the training process. To validate the effectiveness of our proposed method, we performed empirical experiments on both speech and music utterances in subjective and objective metrics. The experimental results show that the neural vocoder trained with the synthetic corpus produced by our method can generalize to multiple target scenarios and has excellent singing voice (MOS: 4.20) and instrumental piece (MOS: 4.00) synthesis results.

The task of referring video object segmentation aims to segment the object in the frames of a given video to which the referring expressions refer. Previous methods adopt multi-stage approach and design complex pipelines to obtain promising results. Recently, the end-to-end method based on Transformer has proved its superiority. In this work, we draw on the advantages of the above methods to provide a simple and effective pipeline for RVOS. Firstly, We improve the state-of-the-art one-stage method ReferFormer to obtain mask sequences that are strongly correlated with language descriptions. Secondly, based on a reliable and high-quality keyframe, we leverage the superior performance of video object segmentation model to further enhance the quality and temporal consistency of the mask results. Our single model reaches 70.3 J &F on the Referring Youtube-VOS validation set and 63.0 on the test set. After ensemble, we achieve 64.1 on the final leaderboard, ranking 1st place on CVPR2022 Referring Youtube-VOS challenge. Code will be available at https://github.com/Zhiweihhh/cvpr2022-rvos-challenge.git.

Referring image segmentation aims to segment the target object described by a given natural language expression. Typically, referring expressions contain complex relationships between the target and its surrounding objects. The main challenge of this task is to understand the visual and linguistic content simultaneously and to find the referred object accurately among all instances in the image. Currently, the most effective way to solve the above problem is to obtain aligned multi-modal features by computing the correlation between visual and linguistic feature modalities under the supervision of the ground-truth mask. However, existing paradigms have difficulty in thoroughly understanding visual and linguistic content due to the inability to perceive information directly about surrounding objects that refer to the target. This prevents them from learning aligned multi-modal features, which leads to inaccurate segmentation. To address this issue, we present a position-aware contrastive alignment network (PCAN) to enhance the alignment of multi-modal features by guiding the interaction between vision and language through prior position information. Our PCAN consists of two modules: 1) Position Aware Module (PAM), which provides position information of all objects related to natural language descriptions, and 2) Contrastive Language Understanding Module (CLUM), which enhances multi-modal alignment by comparing the features of the referred object with those of related objects. Extensive experiments on three benchmarks demonstrate our PCAN performs favorably against the state-of-the-art methods. Our code will be made publicly available.

Prompt tuning has been employed as an efficient way to adapt large vision-language pre-trained models (e.g. CLIP) to various downstream tasks in data-limited or label-limited settings. Nonetheless, visual data (e.g., images) is by default prerequisite for learning prompts in existing methods. In this work, we advocate that the effectiveness of image-text contrastive learning in aligning the two modalities (for training CLIP) further makes it feasible to treat texts as images for prompt tuning and introduce TaI prompting. In contrast to the visual data, text descriptions are easy to collect, and their class labels can be directly derived. Particularly, we apply TaI prompting to multi-label image recognition, where sentences in the wild serve as alternatives to images for prompt tuning. Moreover, with TaI, double-grained prompt tuning (TaI-DPT) is further presented to extract both coarse-grained and fine-grained embeddings for enhancing the multi-label recognition performance. Experimental results show that our proposed TaI-DPT outperforms zero-shot CLIP by a large margin on multiple benchmarks, e.g., MS-COCO, VOC2007, and NUS-WIDE, while it can be combined with existing methods of prompting from images to improve recognition performance further. Code is released at https://github.com/guozix/TaI-DPT.