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From Images to Textual Prompts: Zero-shot Visual Question Answering with Frozen Large Language Models

From Images to Textual Prompts: Zero-shot Visual Question An...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Guo, Jiaxian Li, Junnan Li, Dongxu Tiong, Anthony Meng Huat Li, Boyang Tao, Dacheng Hoi, Steven Univ Sydney Sydney NSW Australia Salesforce Res San Francisco CA USA Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9798350301298

Large language models (LLMs) have demonstrated excellent zero-shot generalization to new language tasks. However, effective utilization of LLMs for zero-shot visual question-answering (VQA) remains challenging, primarily due to the modality disconnect and task disconnect between the LLM and VQA tasks. End-to-end training on multimodal data may bridge the disconnects, but is inflexible and computationally expensive. To address this issue, we propose Img2LLM, a plug-and-play module that provides LLM prompts to enable LLMs to perform zero-shot VQA tasks without end-to-end training. We develop LLM-agnostic models describe image content as exemplar question-answer pairs, which prove to be effective LLM prompts. Img2LLM offers the following benefits: 1) It achieves comparable or better performance than methods relying on end-to-end training. For example, we outperform Flamingo [3] by 5.6% on VQAv2. On the challenging A-OKVQA dataset, our method outperforms few-shot methods by as much as 20%. 2) It flexibly interfaces with a wide range of LLMs to perform VQA. 3) It eliminates the need to specialize LLMs using end-to-end finetuning and serve highly specialized LLMs to end users, thereby reducing cost. Code is available via the LAVIS [28] framework at https://***/salesforce/LAVIS/ tree/main/projects/img2llm-vqa.

关键词： language reasoning vision

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Structure Aggregation for Cross-Spectral Stereo Image Guided Denoising

Structure Aggregation for Cross-Spectral Stereo Image Guided...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Sheng, Zehua Yu, Zhu Liu, Xiongwei Cao, Si-Yuan Liu, Yuqi Shen, Hui-Liang Zhang, Huaqi Zhejiang Univ Hangzhou Peoples R China Vivo Mobile Commun Co Ltd Dongguan Peoples R China

ISBN: (纸本)9798350301298

To obtain clean images with salient structures from noisy observations, a growing trend in current denoising studies is to seek the help of additional guidance images with high signal-to-noise ratios, which are often acquired in different spectral bands such as near infrared. Although previous guided denoising methods basically require the input images to be well-aligned, a more common way to capture the paired noisy target and guidance images is to exploit a stereo camera system. However, current studies on cross-spectral stereo matching cannot fully guarantee the pixel-level registration accuracy, and rarely consider the case of noise contamination. In this work, for the first time, we propose a guided denoising framework for cross-spectral stereo images. Instead of aligning the input images via conventional stereo matching, we aggregate structures from the guidance image to estimate a clean structure map for the noisy target image, which is then used to regress the final denoising result with a spatially variant linear representation model. Based on this, we design a neural network, called as SANet, to complete the entire guided denoising process. Experimental results show that, our SANet can effectively transfer structures from an unaligned guidance image to the restoration result, and outperforms state-of-the-art denoisers on various stereo image datasets. Besides, our structure aggregation strategy also shows its potential to handle other unaligned guided restoration tasks such as super-resolution and deblurring. The source code is available at https://***/lustrouselixir/SANet.

关键词： Low-level vision

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Task-specific Fine-tuning via Variational Information Bottleneck for Weakly-supervised Pathology Whole Slide Image Classification

Task-specific Fine-tuning via Variational Information Bottle...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Li, Honglin Zhu, Chenglu Zhang, Yunlong Sun, Yuxuan Shui, Zhongyi Kuang, Wenwei Zheng, Sunyi Yang, Lin Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Westlake Univ Res Ctr Ind Future Hangzhou Peoples R China Westlake Univ Sch Engn Hangzhou Peoples R China Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350301298

While Multiple Instance Learning (MIL) has shown promising results in digital Pathology Whole Slide Image (WSI) analysis, such a paradigm still faces performance and generalization problems due to high computational costs and limited supervision of Gigapixel WSIs. To deal with the computation problem, previous methods utilize a frozen model pretrained from ImageNet to obtain representations, however, it may lose key information owing to the large domain gap and hinder the generalization ability without image-level training-time augmentation. Though Self-supervised Learning (SSL) proposes viable representation learning schemes, the downstream task-specific features via partial label tuning are not explored. To alleviate this problem, we propose an efficient WSI fine-tuning framework motivated by the Information Bottleneck theory. The theory enables the framework to find the minimal sufficient statistics of WSI, thus supporting us to fine-tune the backbone into a task-specific representation only depending on WSI-level weak labels. The WSI-MIL problem is further analyzed to theoretically deduce our fine-tuning method. We evaluate the method on five pathological WSI datasets on various WSI heads. The experimental results show significant improvements in both accuracy and generalization compared with previous works. Source code will be available at https://***/invoker-LL/WSI-finetuning.

关键词： cell microscopy Medical and biological vision

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ReCo: Region-Controlled Text-to-Image Generation

ReCo: Region-Controlled Text-to-Image Generation

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Yang, Zhengyuan Wang, Jianfeng Gan, Zhe Li, Linjie Lin, Kevin Wu, Chenfei Duan, Nan Liu, Zicheng Liu, Ce Zeng, Michael Wang, Lijuan Microsoft Albuquerque NM 87108 USA

ISBN: (纸本)9798350301298

Recently, large-scale text-to-image (T2I) models have shown impressive performance in generating high-fidelity images, but with limited controllability, e.g., precisely specifying the content in a specific region with a free-form text description. In this paper, we propose an effective technique for such regional control in T2I generation. We augment T2I models' inputs with an extra set of position tokens, which represent the quantized spatial coordinates. Each region is specified by four position tokens to represent the top-left and bottom-right corners, followed by an open-ended natural language regional description. Then, we fine-tune a pre-trained T2I model with such new input interface. Our model, dubbed as ReCo (Region-Controlled T2I), enables the region control for arbitrary objects described by open-ended regional texts rather than by object labels from a constrained category set. Empirically, ReCo achieves better image quality than the T2I model strengthened by positional words (FID: 8.82 -> 7.36, SceneFID: 15.54 -> 6.51 on COCO), together with objects being more accurately placed, amounting to a 20.40% region classification accuracy improvement on COCO. Furthermore, we demonstrate that ReCo can better control the object count, spatial relationship, and region attributes such as color/size, with the free-form regional description. Human evaluation on PaintSkill shows that ReCo is +19.28% and +17.21% more accurate in generating images with correct object count and spatial relationship than the T2I model. Code is available at https://***/microsoft/ReCo.

关键词： language reasoning vision

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Adaptive Data-Free Quantization

Adaptive Data-Free Quantization

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Qian, Biao Wang, Yang Hong, Richang Wang, Meng Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ Sch Comp Sci & Informat Engn Hefei Peoples R China

ISBN: (纸本)9798350301298

Data-free quantization (DFQ) recovers the performance of quantized network (Q) without the original data, but generates the fake sample via a generator (G) by learning from full-precision network (P), which, however, is totally independent of Q, overlooking the adaptability of the knowledge from generated samples, i.e., informative or not to the learning process of Q, resulting into the overflow of generalization error. Building on this, several critical questions how to measure the sample adaptability to Q under varied bit-width scenarios? whether the largest adaptability is the best? how to generate the samples with adaptive adaptability to improve Q's generalization? To answer the above questions, in this paper, we propose an Adaptive Data-Free Quantization (AdaDFQ) method, which revisits DFQ from a zero-sum game perspective upon the sample adaptability between two players - a generator and a quantized network. Following this viewpoint, we further define the disagreement and agreement samples to form two boundaries, where the margin between two boundaries is optimized to adaptively regulate the adaptability of generated samples to Q, so as to address the over-and-under fitting issues. Our AdaDFQ reveals: 1) the largest adaptability is NOT the best for sample generation to benefit Q's generalization;2) the knowledge of the generated sample should not be informative to Q only, but also related to the category and distribution information of the training data for P. The theoretical and empirical analysis validate the advantages of AdaDFQ over the state-of-the-arts. Our code is available at https://***/hfutqian/AdaDFQ.

关键词： Efficient and scalable vision

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You Can Ground Earlier than See: An Effective and Efficient Pipeline for Temporal Sentence Grounding in Compressed Videos

You Can Ground Earlier than See: An Effective and Efficient ...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Fang, Xiang Liu, Daizong Zhou, Pan Nan, Guoshun Huazhong Univ Sci & Technol Hubei Key Lab Distributed Syst Secur Hubei Engn Res Ctr Big Data Secur Sch Cyber Sci & Engn Wuhan Peoples R China Peking Univ Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9798350301298

Given an untrimmed video, temporal sentence grounding (TSG) aims to locate a target moment semantically according to a sentence query. Although previous respectable works have made decent success, they only focus on high-level visual features extracted from the consecutive decoded frames and fail to handle the compressed videos for query modelling, suffering from insufficient representation capability and significant computational complexity during training and testing. In this paper, we pose a new setting, compressed-domain TSG, which directly utilizes compressed videos rather than fully-decompressed frames as the visual input. To handle the raw video bit-stream input, we propose a novel Three-branch Compressed-domain Spatial-temporal Fusion (TCSF) framework, which extracts and aggregates three kinds of low-level visual features (I-frame, motion vector and residual features) for effective and efficient grounding. Particularly, instead of encoding the whole decoded frames like previous works, we capture the appearance representation by only learning the I-frame feature to reduce delay or latency. Besides, we explore the motion information not only by learning the motion vector feature, but also by exploring the relations of neighboring frames via the residual feature. In this way, a three-branch spatial-temporal attention layer with an adaptive motion-appearance fusion module is further designed to extract and aggregate both appearance and motion information for the final grounding. Experiments on three challenging datasets shows that our TCSF achieves better performance than other state-of-the-art methods with lower complexity.

关键词： language reasoning vision

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Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object Detection

Discriminative Co-Saliency and Background Mining Transformer...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Li, Long Han, Junwei Zhang, Ni Liu, Nian Khan, Salman Cholakkal, Hisham Anwer, Rao Muhammad Khan, Fahad Shahbaz Northwestern Polytech Univ Grande Prairie AB Canada Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Australian Natl Univ Canberra Australia Linkoping Univ CVL Linkoping Sweden

ISBN: (纸本)9798350301298

Most previous co-salient object detection works mainly focus on extracting co-salient cues via mining the consistency relations across images while ignore explicit exploration of background regions. In this paper, we propose a Discriminative co-saliency and background Mining Transformer framework (DMT) based on several economical multi-grained correlation modules to explicitly mine both co-saliency and background information and effectively model their discrimination. Specifically, we first propose a region-to-region correlation module for introducing inter-image relations to pixel-wise segmentation features while maintaining computational efficiency. Then, we use two types of pre-defined tokens to mine co-saliency and background information via our proposed contrast-induced pixel-to-token correlation and co-saliency token-to-token correlation modules. We also design a token-guided feature refinement module to enhance the discriminability of the segmentation features under the guidance of the learned tokens. We perform iterative mutual promotion for the segmentation feature extraction and token construction. Experimental results on three benchmark datasets demonstrate the effectiveness of our proposed method. The source code is available at: https://***/dragonlee258079/DMT.

关键词： Low-level vision

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Geometric Visual Similarity Learning in 3D Medical Image Self-supervised Pre-training

Geometric Visual Similarity Learning in 3D Medical Image Sel...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： He, Yuting Yang, Guanyu Ge, Rongjun Chen, Yang Coatrieux, Jean-Louis Wang, Boyu Li, Shuo Southeast Univ Dhaka Bangladesh Nanjing Univ Aeronaut & Astronaut Nanjing Peoples R China Univ Rennes 1 Rennes France Western Univ London England Case Western Reserve Univ Cleveland OH 44106 USA

ISBN: (纸本)9798350301298

Learning inter-image similarity is crucial for 3D medical images self-supervised pre-training, due to their sharing of numerous same semantic regions. However, the lack of the semantic prior in metrics and the semantic-independent variation in 3D medical images make it challenging to get a reliable measurement for the inter-image similarity, hindering the learning of consistent representation for same semantics. We investigate the challenging problem of this task, i.e., learning a consistent representation between images for a clustering effect of same semantic features. We propose a novel visual similarity learning paradigm, Geometric Visual Similarity Learning, which embeds the prior of topological invariance into the measurement of the inter-image similarity for consistent representation of semantic regions. To drive this paradigm, we further construct a novel geometric matching head, the Z-matching head, to collaboratively learn the global and local similarity of semantic regions, guiding the efficient representation learning for different scale-level inter-image semantic features. Our experiments demonstrate that the pre-training with our learning of inter-image similarity yields more powerful inner-scene, inter-scene, and global-local transferring ability on four challenging 3D medical image tasks. Our codes and pre-trained models will be publicly available.

关键词： cell microscopy Medical and biological vision

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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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Generating Aligned Pseudo-Supervision from Non-Aligned Data for Image Restoration in Under-Display Camera

Generating Aligned Pseudo-Supervision from Non-Aligned Data ...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Feng, Ruicheng Li, Chongyi Chen, Huaijin Li, Shuai Gu, Jinwei Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore SenseBrain Technol San Jose CA USA Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Due to the difficulty in collecting large-scale and perfectly aligned paired training data for Under-Display Camera (UDC) image restoration, previous methods resort to monitor-based image systems or simulation-based methods, sacrificing the realness of the data and introducing domain gaps. In this work, we revisit the classic stereo setup for training data collection - capturing two images of the same scene with one UDC and one standard camera. The key idea is to "copy" details from a high-quality reference image and "paste" them on the UDC image. While being able to generate real training pairs, this setting is susceptible to spatial misalignment due to perspective and depth of field changes. The problem is further compounded by the large domain discrepancy between the UDC and normal images, which is unique to UDC restoration. In this paper, we mitigate the non-trivial domain discrepancy and spatial misalignment through a novel Transformer-based framework that generates well-aligned yet high-quality target data for the corresponding UDC input. This is made possible through two carefully designed components, namely, the Domain Alignment Module (DAM) and Geometric Alignment Module (GAM), which encourage robust and accurate discovery of correspondence between the UDC and normal views. Extensive experiments show that high-quality and well-aligned pseudo UDC training pairs are beneficial for training a robust restoration network. Code and the dataset are available at https://github. com/ jnjaby/AlignFormer.

关键词： Low-level vision

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