SignX: The Foundation Model for Sign Recognition

The complexity of sign language data processing brings many challenges. The current approach to recognition of ASL signs aims to translate RGB sign language videos through pose information into English-based ID glosses, which serve to uniquely identify ASL signs. Note that there is no shared convention for assigning such glosses to ASL signs, so it is essential that the same glossing conventions are used for all of the data in the datasets that are employed. This paper proposes SignX, a foundation model framework for sign recognition. It is a concise yet powerful framework applicable to multiple human activity recognition scenarios. First, we developed a Pose2Gloss component based on an inverse diffusion model, which contains a multi-track pose fusion layer that unifies five of the most powerful pose information sources--SMPLer-X, DWPose, Mediapipe, PrimeDepth, and Sapiens Segmentation--into a single latent pose representation. Second, we trained a Video2Pose module based on ViT that can directly convert raw video into signer pose representation. Through this 2-stage training framework, we enable sign language recognition models to be compatible with existing pose formats, laying the foundation for the common pose estimation necessary for sign recognition. Experimental results show that SignX can recognize signs from sign language video, producing predicted gloss representations with greater accuracy than has been reported in prior work.

Bridging the Gap between Text, Audio, Image, and Any Sequence: A Novel Approach using Gloss-based Annotation

This paper presents an innovative approach called BGTAI to simplify multimodal understanding by utilizing gloss-based annotation as an intermediate step in aligning Text and Audio with Images. While the dynamic temporal factors in textual and audio inputs contain various predicate adjectives that influence the meaning of the entire sentence, images, on the other hand, present static scenes. By representing text and audio as gloss notations that omit complex semantic nuances, a better alignment with images can potentially be achieved. This study explores the feasibility of this idea, specifically, we first propose the first Langue2Gloss model and then integrate it into the multimodal model UniBriVL for joint training. To strengthen the adaptability of gloss with text/audio and overcome the efficiency and instability issues in multimodal training, we propose a DS-Net (Data-Pair Selection Network), an Result Filter module, and a novel SP-Loss function. Our approach outperforms previous multimodal models in the main experiments, demonstrating its efficacy in enhancing multimodal representations and improving compatibility among text, audio, visual, and any sequence modalities.

SignLLM: Sign Languages Production Large Language Models

In this paper, we introduce the first comprehensive multilingual sign language dataset named Prompt2Sign, which builds from public data including American Sign Language (ASL) and seven others. Our dataset transforms a vast array of videos into a streamlined, model-friendly format, optimized for training with translation models like seq2seq and text2text. Building on this new dataset, we propose SignLLM, the first multilingual Sign Language Production (SLP) model, which includes two novel multilingual SLP modes that allow for the generation of sign language gestures from input text or prompt. Both of the modes can use a new loss and a module based on reinforcement learning, which accelerates the training by enhancing the model's capability to autonomously sample high-quality data. We present benchmark results of SignLLM, which demonstrate that our model achieves state-of-the-art performance on SLP tasks across eight sign languages.

Generative AI to Generate Test Data Generators

Generating fake data is an essential dimension of modern software testing, as demonstrated by the number and significance of data faking libraries. Yet, developers of faking libraries cannot keep up with the wide range of data to be generated for different natural languages and domains. In this paper, we assess the ability of generative AI for generating test data in different domains. We design three types of prompts for Large Language Models (LLMs), which perform test data generation tasks at different levels of integrability: 1) raw test data generation, 2) synthesizing programs in a specific language that generate useful test data, and 3) producing programs that use state-of-the-art faker libraries. We evaluate our approach by prompting LLMs to generate test data for 11 domains. The results show that LLMs can successfully generate realistic test data generators in a wide range of domains at all three levels of integrability.

RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair

Automated Program Repair (APR) has evolved significantly with the advent of Large Language Models (LLMs). Fine-tuning LLMs for program repair is a recent avenue of research, with many dimensions which have not been explored. Existing work mostly fine-tunes LLMs with naive code representations and is fundamentally limited in its ability to fine-tune larger LLMs. To address this problem, we propose RepairLLaMA, a novel program repair approach that combines 1) code representations for APR and 2) the state-of-the-art parameter-efficient LLM fine-tuning technique called LoRA. This results in RepairLLaMA producing a highly effective `program repair adapter' for fixing bugs with language models. Our experiments demonstrate the validity of both concepts. First, fine-tuning adapters with program repair specific code representations enables the model to use meaningful repair signals. Second, parameter-efficient fine-tuning helps fine-tuning to converge and contributes to the effectiveness of the repair adapter to fix data-points outside the fine-tuning data distribution. Overall, RepairLLaMA correctly fixes 125 Defects4J v2 and 82 HumanEval-Java bugs, outperforming all baselines.

Supersonic: Learning to Generate Source Code Optimizations in C/C++

Software optimization refines programs for resource efficiency while preserving functionality. Traditionally, it is a process done by developers and compilers. This paper introduces a third option, automated optimization at the source code level. We present Supersonic, a neural approach targeting minor source code modifications for optimization. Using a seq2seq model, Supersonic is trained on C/C++ program pairs ($x_{t}$, $x_{t+1}$), where $x_{t+1}$ is an optimized version of $x_{t}$, and outputs a diff. Supersonic's performance is benchmarked against OpenAI's GPT-3.5-Turbo and GPT-4 on competitive programming tasks. The experiments show that Supersonic not only outperforms both models on the code optimization task but also minimizes the extent of the change with a model more than 600x smaller than GPT-3.5-Turbo and 3700x smaller than GPT-4.

Echotune: A Modular Extractor Leveraging the Variable-Length Nature of Speech in ASR Tasks

The Transformer architecture has proven to be highly effective for Automatic Speech Recognition (ASR) tasks, becoming a foundational component for a plethora of research in the domain. Historically, many approaches have leaned on fixed-length attention windows, which becomes problematic for varied speech samples in duration and complexity, leading to data over-smoothing and neglect of essential long-term connectivity. Addressing this limitation, we introduce Echo-MSA, a nimble module equipped with a variable-length attention mechanism that accommodates a range of speech sample complexities and durations. This module offers the flexibility to extract speech features across various granularities, spanning from frames and phonemes to words and discourse. The proposed design captures the variable length feature of speech and addresses the limitations of fixed-length attention. Our evaluation leverages a parallel attention architecture complemented by a dynamic gating mechanism that amalgamates traditional attention with the Echo-MSA module output. Empirical evidence from our study reveals that integrating Echo-MSA into the primary model's training regime significantly enhances the word error rate (WER) performance, all while preserving the intrinsic stability of the original model.

SignDiff: Learning Diffusion Models for American Sign Language Production

The field of Sign Language Production (SLP) lacked a large-scale, pre-trained model based on deep learning for continuous American Sign Language (ASL) production in the past decade. This limitation hampers communication for all individuals with disabilities relying on ASL. To address this issue, we undertook the secondary development and utilization of How2Sign, one of the largest publicly available ASL datasets. Despite its significance, prior researchers in the field of sign language have not effectively employed this corpus due to the intricacies involved in American Sign Language Production (ASLP). To conduct large-scale ASLP, we propose SignDiff based on the latest work in related fields, which is a dual-condition diffusion pre-training model that can generate human sign language speakers from a skeleton pose. SignDiff has a novel Frame Reinforcement Network called FR-Net, similar to dense human pose estimation work, which enhances the correspondence between text lexical symbols and sign language dense pose frames reduce the occurrence of multiple fingers in the diffusion model. In addition, our ASLP method proposes two new improved modules and a new loss function to improve the accuracy and quality of sign language skeletal posture and enhance the ability of the model to train on large-scale data. We propose the first baseline for ASL production and report the scores of 17.19 and 12.85 on BLEU-4 on the How2Sign dev/test sets. We also evaluated our model on the previous mainstream dataset called PHOENIX14T, and the main experiments achieved the results of SOTA. In addition, our image quality far exceeds all previous results by 10 percentage points on the SSIM indicator. Finally, we conducted ablation studies and qualitative evaluations for discussion.

UniBriVL: Robust Universal Representation and Generation of Audio Driven Diffusion Models

Multimodal large models have been recognized for their advantages in various performance and downstream tasks. The development of these models is crucial towards achieving general artificial intelligence in the future. In this paper, we propose a novel universal language representation learning method called UniBriVL, which is based on Bridging-Vision-and-Language (BriVL). Universal BriVL embeds audio, image, and text into a shared space, enabling the realization of various multimodal applications. Our approach addresses major challenges in robust language (both text and audio) representation learning and effectively captures the correlation between audio and image. Additionally, we demonstrate the qualitative evaluation of the generated images from UniBriVL, which serves to highlight the potential of our approach in creating images from audio. Overall, our experimental results demonstrate the efficacy of UniBriVL in downstream tasks and its ability to choose appropriate images from audio. The proposed approach has the potential for various applications such as speech recognition, music signal processing, and captioning systems.

New Audio Representations Image Gan Generation from BriVL

Recently, researchers have gradually realized that in some cases, the self-supervised pre-training on large-scale Internet data is better than that of high-quality/manually labeled data sets, and multimodal/large models are better than single or bimodal/small models. In this paper, we propose a robust audio representation learning method WavBriVL based on Bridging-Vision-and-Language (BriVL). WavBriVL projects audio, image and text into a shared embedded space, so that multi-modal applications can be realized. We demonstrate the qualitative evaluation of the image generated from WavBriVL as a shared embedded space, with the main purposes of this paper: (1) Learning the correlation between audio and image; (2) Explore a new way of image generation, that is, use audio to generate pictures. Experimental results show that this method can effectively generate appropriate images from audio.