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Fusing Pre-trained Language Models with Multimodal Prompts through Reinforcement Learning

Fusing Pre-trained Language Models with Multimodal Prompts t...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Yu, Youngjae Chung, Jiwan Yun, Heeseung Hessel, Jack Park, Jae Sung Lu, Ximing Zellers, Rowan Ammanabrolu, Prithviraj Le Bras, Ronan Kim, Gunhee Choi, Yejin Allen Inst Artificial Intelligence Seattle WA USA OpenAI Seattle WA USA Yonsei Univ Dept Artificial Intelligence Seoul South Korea Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea Univ Washington Paul G Allen Sch Comp Sci Seattle WA 98195 USA

ISBN: (纸本)9798350301298

Language models are capable of commonsense reasoning: while domain-specific models can learn from explicit knowledge (e.g. commonsense graphs [6], ethical norms [25]), and larger models like GPT-3 [7] manifest broad commonsense reasoning capacity. Can their knowledge be extended to multimodal inputs such as images and audio without paired domain data? In this work, we propose ESPER (Extending Sensory PErception with Reinforcement learning) which enables text-only pretrained models to address multimodal tasks such as visual commonsense reasoning. Our key novelty is to use reinforcement learning to align multimodal inputs to language model generations without direct supervision: for example, our reward optimization relies only on cosine similarity derived from CLIP [52] and requires no additional paired (image, text) data. Experiments demonstrate that ESPER outperforms baselines and prior work on a variety of multimodal text generation tasks ranging from captioning to commonsense reasoning;these include a new benchmark we collect and release, the ESP dataset, which tasks models with generating the text of several different domains for each image. Our code and data are publicly released at https://***/JiwanChung/esper.

关键词： and reasoning language vision

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S³C: Semi-Supervised VQA Natural Language Explanation via Self-Critical Learning

S<SUP>3</SUP>C: Semi-Supervised VQA Natural Language Explana...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Suo, Wei Sun, Mengyang Liu, Weisong Gao, Yiqi Wang, Peng Zhang, Yanning Wu, Qi Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Northwestern Polytech Univ Ningbo Inst Xian Peoples R China Northwestern Polytech Univ Sch Cybersecur Xian Peoples R China Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9798350301298

VQA Natural Language Explanation (VQA-NLE) task aims to explain the decision-making process of VQA models in natural language. Unlike traditional attention or gradient analysis, free-text rationales can be easier to understand and gain users' trust. Existing methods mostly use post-hoc or self-rationalization models to obtain a plausible explanation. However, these frameworks are bottlenecked by the following challenges: 1) the reasoning process cannot be faithfully responded to and suffer from the problem of logical inconsistency. 2) Human-annotated explanations are expensive and time-consuming to collect. In this paper, we propose a new Semi-Supervised VQA-NLE via Self-Critical Learning ((SC)-C-3), which evaluates the candidate explanations by answering rewards to improve the logical consistency between answers and rationales. With a semi-supervised learning framework, the (SC)-C-3 can benefit from a tremendous amount of samples without human-annotated explanations. A large number of automatic measures and human evaluations all show the effectiveness of our method. Meanwhile, the framework achieves a new state-of-the-art performance on the two VQA-NLE datasets.

关键词： and reasoning language vision

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PromptKD: Unsupervised Prompt Distillation for vision-Language Models

PromptKD: Unsupervised Prompt Distillation for Vision-Langua...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Li, Zheng Li, Xiang Fu, Xinyi Zhang, Xin Wang, Weiqiang Chen, Shuo Yang, Jian Nankai Univ Coll Comp Sci PCA Lab VCIP Tianjin Peoples R China NKIARI Shenzhen Futian Peoples R China Ant Grp Tiansuan Lab Hangzhou Peoples R China RIKEN Wako Saitama Japan

ISBN: (纸本)9798350353006

Prompt learning has emerged as a valuable technique in enhancing vision-language models (VLMs) such as CLIP for downstream tasks in specific domains. Existing work mainly focuses on designing various learning forms of prompts, neglecting the potential of prompts as effective distillers for learning from larger teacher models. In this paper, we introduce an unsupervised domain prompt distillation framework, which aims to transfer the knowledge of a larger teacher model to a lightweight target model through prompt-driven imitation using unlabeled domain images. Specifically, our framework consists of two distinct stages. In the initial stage, we pre-train a large CLIP teacher model using domain (few-shot) labels. After pre-training, we leverage the unique decoupled-modality characteristics of CLIP by pre-computing and storing the text features as class vectors only once through the teacher text encoder. In the subsequent stage, the stored class vectors are shared across teacher and student image encoders for calculating the predicted logits. Further, we align the logits of both the teacher and student models via KL divergence, encouraging the student image encoder to generate similar probability distributions to the teacher through the learnable prompts. The proposed prompt distillation process eliminates the reliance on labeled data, enabling the algorithm to leverage a vast amount of unlabeled images within the domain. Finally, the well-trained student image encoders and pre-stored text features (class vectors) are utilized for inference. To our best knowledge, we are the first to (1) perform unsupervised domain-specific prompt-driven knowledge distillation for CLIP, and (2) establish a practical pre-storing mechanism of text features as shared class vectors between teacher and student. Extensive experiments on 11 datasets demonstrate the effectiveness of our method. Code is publicly available at https://***/zhengli97/PromptKD.

关键词： knowledge distillation prompt learning vision-language models zero-shot learning

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Query-Dependent Video Representation for Moment Retrieval and Highlight Detection

Query-Dependent Video Representation for Moment Retrieval an...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Moon, WonJun Hyun, Sangeek Park, SangUk Park, Dongchan Heo, Jae-Pil Sungkyunkwan Univ Seoul South Korea Pyler Seoul South Korea

ISBN: (纸本)9798350301298

Recently, video moment retrieval and highlight detection (MR/HD) are being spotlighted as the demand for video understanding is drastically increased. The key objective of MR/HD is to localize the moment and estimate clip-wise accordance level, i.e., saliency score, to the given text query. Although the recent transformer-based models brought some advances, we found that these methods do not fully exploit the information of a given query. For example, the relevance between text query and video contents is sometimes neglected when predicting the moment and its saliency. To tackle this issue, we introduce Query-Dependent DETR (QD-DETR), a detection transformer tailored for MR/HD. As we observe the insignificant role of a given query in transformer architectures, our encoding module starts with cross-attention layers to explicitly inject the context of text query into video representation. Then, to enhance the model's capability of exploiting the query information, we manipulate the video-query pairs to produce irrelevant pairs. Such negative (irrelevant) video-query pairs are trained to yield low saliency scores, which in turn, encourages the model to estimate precise accordance between query-video pairs. Lastly, we present an input-adaptive saliency predictor which adaptively defines the criterion of saliency scores for the given video-query pairs. Our extensive studies verify the importance of building the query-dependent representation for MR/HD. Specifically, QD-DETR outperforms state-of-the-art methods on QVHighlights, TVSum, and Charades-STA datasets. Codes are available at ***/wjun0830/QD-DETR.

关键词： detection recognition: Categorization retrieval

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Joint Multimodal Transformer for Emotion recognition in the Wild

Joint Multimodal Transformer for Emotion Recognition in the ...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Waligora, Paul Aslam, Muhammad Haseeb Zeeshan, Muhammad Osama Belharbi, Soufiane Koerich, Alessandro Lameiras Pedersoli, Marco Bacon, Simon Granger, Eric ETS Montreal LIVIA Dept Syst Engn Montreal PQ Canada Concordia Univ Dept Hlth Kinesiol & Appl Physiol Montreal PQ Canada

ISBN: (纸本)9798350365474

Multimodal emotion recognition (MMER) systems typically outperform unimodal systems by leveraging the interand intra-modal relationships between, e.g., visual, textual, physiological, and auditory modalities. This paper proposes an MMER method that relies on a joint multimodal transformer (JMT) for fusion with key-based cross-attention. This framework can exploit the complementary nature of diverse modalities to improve predictive accuracy. Separate backbones capture intra-modal spatiotemporal dependencies within each modality over video sequences. Subsequently, our JMT fusion architecture integrates the individual modality embeddings, allowing the model to effectively capture inter- and intra-modal relationships. Extensive experiments on two challenging expression recognition tasks - (1) dimensional emotion recognition on the Affwild2 dataset (with face and voice) and (2) pain estimation on the Biovid dataset (with face and biosensors) - indicate that our JMT fusion can provide a cost-effective solution for MMER. Empirical results show that MMER systems with our proposed fusion allow us to outperform relevant baseline and state-of-the-art methods. Code is available at: https://***/PoloWlg/Joint-Multimodal-Transformer-6th-ABAW

关键词： Cross Attention Joint Multimodal Transformer Multimodal Emotion recognition Pain Estimation Valence Arousal

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Hierarchical NeuroSymbolic Approach for Comprehensive and Explainable Action Quality Assessment

Hierarchical NeuroSymbolic Approach for Comprehensive and Ex...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Okamoto, Lauren Parmar, Paritosh Princeton Univ Princeton NJ 08544 USA ASTAR IHPC Singapore Singapore

ISBN: (纸本)9798350365474

Action quality assessment (AQA) applies computer vision to quantitatively assess the performance or execution of a human action. Current AQA approaches are end-to-end neural models, which lack transparency and tend to be biased because they are trained on subjective human judgements as ground-truth. To address these issues, we introduce a neuro-symbolic paradigm for AQA, which uses neural networks to abstract interpretable symbols from video data and makes quality assessments by applying rules to those symbols. We take diving as the case study. We found that domain experts prefer our system and find it more informative than purely neural approaches to AQA in diving. Our system also achieves state-of-the-art action recognition and temporal segmentation, and automatically generates a detailed report that breaks the dive down into its elements and provides objective scoring with visual evidence. As verified by a group of domain experts, this report may be used to assist judges in scoring, help train judges, and provide feedback to divers. Annotated training data and code: https://***/laurenok24/NSAQA.

关键词： action quality assessment action recognition AI Coach AI Diving Coach AI Diving Judge AI Olympics Judge explainable AI fairness in AI interpretable action analysis interpretable action quality assessment interpretable fine-grained action quality assessment neuro-symbolic computer vision neurosymbolic action assessment neurosymbolic action scoring neurosymbolic AI neurosymbolic fine-grained action analysis neurosymbolic fine-grained action quality assessment neurosymbolic fine-grained action recogntion neurosymbolic fine-grained action understanding neurosymbolic skills assessment neurosymbolic temporal segmentation neurosymbolic video understanding Olympics Scoring representation learning skills assessment temporal segmentation transparent AI XAI

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Generalizable Implicit Neural Representations via Instance pattern Composers

Generalizable Implicit Neural Representations via Instance P...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Kim, Chiheon Lee, Doyup Kim, Saehoon Cho, Minsu Han, Wook-Shin Kakao Brain Seongnam South Korea POSTECH Pohang South Korea

ISBN: (纸本)9798350301298

Despite recent advances in implicit neural representations (INRs), it remains challenging for a coordinate-based multi-layer perceptron (MLP) of INRs to learn a common representation across data instances and generalize it for unseen instances. In this work, we introduce a simple yet effective framework for generalizable INRs that enables a coordinate-based MLP to represent complex data instances by modulating only a small set of weights in an early MLP layer as an instance pattern composer;the remaining MLP weights learn pattern composition rules for common representations across instances. Our generalizable INR framework is fully compatible with existing meta-learning and hypernetworks in learning to predict the modulated weight for unseen instances. Extensive experiments demonstrate that our method achieves high performance on a wide range of domains such as an audio, image, and 3D object, while the ablation study validates our weight modulation.

关键词： Self-supervised or unsupervised representation learning

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Neural Video Compression with Diverse Contexts

Neural Video Compression with Diverse Contexts

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Li, Jiahao Li, Bin Lu, Yan Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

For any video codecs, the coding efficiency highly relies on whether the current signal to be encoded can find the relevant contexts from the previous reconstructed signals. Traditional codec has verified more contexts bring substantial coding gain, but in a time-consuming manner. However, for the emerging neural video codec (NVC), its contexts are still limited, leading to low compression ratio. To boost NVC, this paper proposes increasing the context diversity in both temporal and spatial dimensions. First, we guide the model to learn hierarchical quality patterns across frames, which enriches long-term and yet high-quality temporal contexts. Furthermore, to tap the potential of optical flow-based coding framework, we introduce a group-based offset diversity where the cross-group interaction is proposed for better context mining. In addition, this paper also adopts a quadtree-based partition to increase spatial context diversity when encoding the latent representation in parallel. Experiments show that our codec obtains 23.5% bitrate saving over previous SOTA NVC. Better yet, our codec has surpassed the under-developing next generation traditional codec/ECM in both RGB and YUV420 colorspaces, in terms of PSNR. The codes are at https://***/microsoft/DCVC.

关键词： Low-level vision

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VQACL: A Novel Visual Question Answering Continual Learning Setting

VQACL: A Novel Visual Question Answering Continual Learning ...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Xi Zhang, Feifei Xu, Changsheng Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Tianjin Univ Technol Sch Comp Sci & Engn Tianjin Peoples R China

ISBN: (纸本)9798350301298

Research on continual learning has recently led to a variety of work in unimodal community, however little attention has been paid to multimodal tasks like visual question answering (VQA). In this paper, we establish a novel VQA Continual Learning setting named VQACL, which contains two key components: a dual-level task sequence where visual and linguistic data are nested, and a novel composition testing containing new skill-concept combinations. The former devotes to simulating the ever-changing multimodal datastream in real world and the latter aims at measuring models' generalizability for cognitive reasoning. Based on our VQACL, we perform in-depth evaluations of five well-established continual learning methods, and observe that they suffer from catastrophic forgetting and have weak generalizability. To address above issues, we propose a novel representation learning method, which leverages a sample-specific and a sample-invariant feature to learn representations that are both discriminative and generalizable for VQA. Furthermore, by respectively extracting such representation for visual and textual input, our method can explicitly disentangle the skill and concept. Extensive experimental results illustrate that our method significantly outperforms existing models, demonstrating the effectiveness and compositionality of the proposed approach. The code is available at https://***/zhangxi1997/VQACL.

关键词： language reasoning vision

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Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation

Dynamic Graph Enhanced Contrastive Learning for Chest X-ray ...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Li, Mingjie Lin, Bingqian Chen, Zicong Lin, Haokun Liang, Xiaodan Chang, Xiaojun Univ Technol Sydney ReLER AAII Sydney Australia Sun Yat Sen Univ Sch ISE Guangzhou Peoples R China Mohamed bin Zayed Univ Artificial Intelligence Dept Comp Vis Abu Dhabi U Arab Emirates Peng Cheng Natl Lab Shenzhen Peoples R China Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Automatic radiology reporting has great clinical potential to relieve radiologists from heavy workloads and improve diagnosis interpretation. Recently, researchers have enhanced data-driven neural networks with medical knowledge graphs to eliminate the severe visual and textual bias in this task. The structures of such graphs are exploited by using the clinical dependencies formed by the disease topic tags via general knowledge and usually do not update during the training process. Consequently, the fixed graphs can not guarantee the most appropriate scope of knowledge and limit the effectiveness. To address the limitation, we propose a knowledge graph with Dynamic structure and nodes to facilitate chest X-ray report generation with Contrastive Learning, named DCL. In detail, the fundamental structure of our graph is pre-constructed from general knowledge. Then we explore specific knowledge extracted from the retrieved reports to add additional nodes or redefine their relations in a bottom-up manner. Each image feature is integrated with its very own updated graph before being fed into the decoder module for report generation. Finally, this paper introduces Image-Report Contrastive and Image-Report Matching losses to better represent visual features and textual information. Evaluated on IU-Xray and MIMIC-CXR datasets, our DCL outperforms previous state-of-the-art models on these two benchmarks.

关键词： cell microscopy Medical and biological vision

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