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Streaming Dense Video Captioning

Streaming Dense Video Captioning

引用

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

作者： Zhou, Xingyi Arnab, Anurag Buch, Shyamal Yan, Shen Myers, Austin Xiong, Xuehan Nagrani, Arsha Schmid, Cordelia Google Mountain View CA 94043 USA

ISBN: (纸本)9798350353006

An ideal model for dense video captioning - predicting captions localized temporally in a video - should be able to handle long input videos, predict rich, detailed textual descriptions, and be able to produce outputs before processing the entire video. Current state-of-the-art models, however, process a fixed number of downsampled frames, and make a single full prediction after seeing the whole video. We propose a streaming dense video captioning model that consists of two novel components: First, we propose a new memory module, based on clustering incoming tokens, which can handle arbitrarily long videos as the memory is of a fixed size. Second, we develop a streaming decoding algorithm that enables our model to make predictions before the entire video has been processed. Our model achieves this streaming ability, and significantly improves the state-of-the-art on three dense video captioning benchmarks: ActivityNet, YouCook2 and ViTT. Our code is released at https://***/google-research/scenic.

关键词： captioning long video streaming video video captioning vision and language

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Emu Edit: Precise Image Editing via recognition and Generation Tasks

Emu Edit: Precise Image Editing via Recognition and Generati...

引用

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

作者： Sheynin, Shelly Polyak, Adam Singer, Uriel Kirstain, Yuval Zohar, Amit Ashual, Oron Parikh, Devi Taigman, Yaniv Meta GenAI Menlo Pk CA 94025 USA

ISBN: (纸本)9798350353013;9798350353006

Instruction-based image editing holds immense potential for a variety of applications, as it enables users to perform any editing operation using a natural language instruction. However, current models in this domain often struggle with accurately executing user instructions. We present Emu Edit, a multi-task image editing model which sets state-of-the-art results in instruction-based image editing. To develop Emu Edit we train it to multi-task across an unprecedented range of tasks, such as region-based editing, free-form editing, and computer vision tasks, all of which are formulated as generative tasks. Additionally, to enhance Emu Edit's multi-task learning abilities, we provide it with learned task embeddings which guide the generation process towards the correct edit type. Both these elements are essential for Emu Edit's outstanding performance. Furthermore, we show that Emu Edit can generalize to new tasks, such as image inpainting, super-resolution, and compositions of editing tasks, with just a few labeled examples. This capability offers a significant advantage in scenarios where high-quality samples are scarce. Lastly, to facilitate a more rigorous and informed assessment of instructable image editing models, we release a new challenging and versatile benchmark that includes seven different image editing tasks. (1)

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Learning by Correction: Efficient Tuning Task for Zero-Shot Generative vision-Language Reasoning

Learning by Correction: Efficient Tuning Task for Zero-Shot ...

引用

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

作者： Li, Rongjie Wu, Yu He, Xuming ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China

ISBN: (纸本)9798350353006

Generative vision-language models (VLMs) have shown impressive performance in zero-shot vision-language tasks like image captioning and visual question answering. However, improving their zero-shot reasoning typically requires second-stage instruction tuning, which relies heavily on human-labeled or large language model-generated annotation, incurring high labeling costs. To tackle this challenge, we introduce Image-Conditioned Caption Correction (ICCC), a novel pre-training task designed to enhance VLMs' zero-shot performance without the need for labeled task-aware data. The ICCC task compels VLMs to rectify mismatches between visual and language concepts, thereby enhancing instruction following and text generation conditioned on visual inputs. Leveraging language structure and a lightweight dependency parser, we construct data samples of ICCC task from image-text datasets with low labeling and computation costs. Experimental results on BLIP2 and InstructBLIP demonstrate significant improvements in zero-shot image-text generation-based VL tasks through ICCC instruction tuning.

关键词： Multimodal Reasoning vision-Language

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InstructDiffusion: A Generalist Modeling Interface for vision Tasks

InstructDiffusion: A Generalist Modeling Interface for Visio...

引用

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

作者： Geng, Zigang Yang, Binxin Hang, Tiankai Li, Chen Gu, Shuyang Zhang, Ting Bao, Jianmin Zhang, Zheng Li, Houqiang Hu, Han Chen, Dong Guo, Baining Univ Sci & Technol China Hefei Anhui Peoples R China Southeast Univ Nanjing Jiangsu Peoples R China Xi An Jiao Tong Univ Xian Shaanxi Peoples R China Beijing Normal Univ Beijing Peoples R China Microsoft Res Asia Redmond WA 98052 USA

ISBN: (纸本)9798350353006

We present InstructDiffusion, a unified and generic framework for aligning computer vision tasks with human instructions. Unlike existing approaches that integrate prior knowledge and pre-define the output space (e.g., categories and coordinates) for each vision task, we cast diverse vision tasks into a human-intuitive image-manipulating process whose output space is a flexible and interactive pixel space. Concretely, the model is built upon the diffusion process and is trained to predict pixels according to user instructions, such as encircling the man's left shoulder in red or applying a blue mask to the left car. InstructDiffusion could handle a variety of vision tasks, including understanding tasks (such as segmentation and keypoint detection) and generative tasks (such as editing and enhancement) and outperforms prior methods on novel datasets. This represents a solid step towards a generalist modeling interface for vision tasks, advancing artificial general intelligence in the field of computer vision.

关键词： computer vision

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GSNeRF: Generalizable Semantic Neural Radiance Fields with Enhanced 3D Scene Understanding

GSNeRF: Generalizable Semantic Neural Radiance Fields with E...

引用

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

作者： Chou, Zi-Ting Huang, Sheng-Yu Liu, I-Jieh Wang, Yu-Chiang Frank Natl Taiwan Univ Grad Inst Commun Engn Taipei Taiwan NVIDIA Taipei Taiwan

ISBN: (纸本)9798350353006

Utilizing multi-view inputs to synthesize novel-view images, Neural Radiance Fields (NeRF) have emerged as a popular research topic in 3D vision. In this work, we introduce a Generalizable Semantic Neural Radiance Fields ( GSNeRF), which uniquely takes image semantics into the synthesis process so that both novel view image and the associated semantic maps can be produced for unseen scenes. Our GSNeRF is composed of two stages: Semantic GeoReasoning and Depth-Guided Visual rendering. The former is able to observe multi- view image inputs to extract semantic and geometry features from a scene. Guided by the resulting image geometry information, the latter performs both image and semantic rendering with improved performances. Our experiments not only confirm that GSNeRF performs favorably against prior works on both novel-view image and semantic segmentation synthesis but the effectiveness of our sampling strategy for visual rendering is further verified.

关键词： 3D computer vision generalizable nerf NeRF segmentation

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Optimal Transport Aggregation for Visual Place recognition

Optimal Transport Aggregation for Visual Place Recognition

引用

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

作者： Izquierdo, Sergio Civera, Javier Univ Zaragoza I3A Zaragoza Spain

ISBN: (纸本)9798350353006

The task of Visual Place recognition (VPR) aims to match a query image against references from an extensive database of images from different places, relying solely on visual cues. State-of-the-art pipelines focus on the aggregation of features extracted from a deep backbone, in order to form a global descriptor for each image. In this context, we introduce SALAD (Sinkhorn Algorithm for Locally Aggregated Descriptors), which reformulates NetVLAD's soft-assignment of local features to clusters as an optimal transport problem. In SALAD, we consider both feature-to-cluster and cluster-to-feature relations and we also introduce a 'dustbin' cluster, designed to selectively discard features deemed non-informative, enhancing the overall descriptor quality. Additionally, we leverage and fine-tune DINOv2 as a backbone, which provides enhanced description power for the local features, and dramatically reduces the required training time. As a result, our single-stage method not only surpasses single-stage baselines in public VPR datasets, but also surpasses two-stage methods that add a re-ranking with significantly higher cost. Code and models are available at https://***/serizba/salad.

关键词： Image Retrieval Metric Learning Visual Place recognition

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Consistency and Uncertainty: Identifying Unreliable Responses From Black-Box vision-Language Models for Selective Visual Question Answering

Consistency and Uncertainty: Identifying Unreliable Response...

引用

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

作者： Khan, Zaid Fu, Yun Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9798350353006

The goal of selective prediction is to allow an a model to abstain when it may not be able to deliver a reliable prediction, which is important in safety-critical contexts. Existing approaches to selective prediction typically require access to the internals of a model, require retraining a model or study only unimodal models. However, the most powerful models (e.g. GPT-4) are typically only available as black boxes with inaccessible internals, are not retrainable by end-users, and are frequently used for multimodal tasks. We study the possibility of selective prediction for vision-language models in a realistic, black-box setting. We propose using the principle of neighborhood consistency to identify unreliable responses from a black-box vision-language model in question answering tasks. We hypothesize that given only a visual question and model response, the consistency of the model's responses over the neighborhood of a visual question will indicate reliability. It is impossible to directly sample neighbors in feature space in a black-box setting. Instead, we show that it is possible to use a smaller proxy model to approximately sample from the neighborhood. We find that neighborhood consistency can be used to identify model responses to visual questions that are likely unreliable, even in adversarial settings or settings that are out-of-distribution to the proxy model.

关键词： predictive uncertainty selective prediction trustworthy ml vision-language visual question answering

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IrrNet: Spatio-Temporal Segmentation guided Classification for Irrigation Mapping

IrrNet: Spatio-Temporal Segmentation guided Classification f...

引用

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

作者： Hoque, Oishee Bintey Univ Virginia Dept Comp Sci Charlottesville VA 22903 USA

ISBN: (纸本)9798350365474

Irrigation systems can vary widely in scale, from smallscale subsistence farming to large commercial agriculture (see Fig. 1 ). The heterogeneity in irrigation practices and systems across different regions adds to the complexity of mapping (see Fig. 1 ). Distinguishing between irrigated and non-irrigated areas is challenging due to the spectral characteristics of various irrigation systems and practices across different regions, further complicating the task of mapping different types of irrigation. For example, rainfed agriculture is prevalent in the Midwest, Southeast, and parts of the Northeast U.S., while irrigation is common in arid Western and Southwestern states. Rainfed farming can result in highly variable patterns of cultivation. Farmers may practice rainfed agriculture in some fields while irrigating others, leading to a complex mosaic of irrigated and non-irrigated areas within the same region. © 2024 ieee.

关键词： Deep Learning Irrigation Mapping MTL Remote Sensing Segmentation Transfer Learning vision in Agriculture

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Classes Are Not Equal: An Empirical Study on Image recognition Fairness

Classes Are Not Equal: An Empirical Study on Image Recogniti...

引用

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

作者： Cui, Jiequan Zhu, Beier Wen, Xin Qi, Xiaojuan Yu, Bei Zhang, Hanwang Nanyang Technol Univ Singapore Singapore Univ Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350353006

In this paper, we present an empirical study on image unfairness, i.e., extreme class accuracy disparity on balanced data like ImageNet. We demonstrate that are not equal and unfairness is prevalent for image classification models across various datasets, network and model capacities. Moreover, several intriguing properties of fairness are identified. First, the unfairness lies in problematic representation rather than classifier bias distinguished from long-tailed recognition. Second, with the proposed concept of Model Prediction Bias, investigate the origins of problematic representation training optimization. Our findings reveal that models tend to exhibit greater prediction biases for classes that more challenging to recognize. It means that more other will be confused with harder classes. Then the False (FPs) will dominate the learning in optimization, leading to their poor accuracy. Further, we conclude data augmentation and representation learning algorithms improve overall performance by promoting fairness some degree in image classification.

关键词： Fairness Long-tailed recognition Representation Learning vision-Language Models

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Edit One for All: Interactive Batch Image Editing

Edit One for All: Interactive Batch Image Editing

引用

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

作者： Thao Nguyen Ojha, Utkarsh Li, Yuheng Liu, Haotian Lee, Yong Jae Univ Wisconsin Madison Madison WI 53707 USA

ISBN: (纸本)9798350353013;9798350353006

In recent years, image editing has advanced remarkably. With increased human control, it is now possible to edit an image in a plethora of ways;from specifying in text what we want to change, to straight up dragging the contents of the image in an interactive point-based manner. However, most of the focus has remained on editing single images at a time. Whether and how we can simultaneously edit large batches of images has remained understudied. With the goal of minimizing human supervision in the editing process, this paper presents a novel method for interactive batch image editing using StyleGAN as the medium. Given an edit specified by users in an example image (e.g., make the face frontal), our method can automatically transfer that edit to other test images, so that regardless of their initial state (pose), they all arrive at the same final state (e.g., all facing front). Extensive experiments demonstrate that edits performed using our method have similar visual quality to existing single-image-editing methods, while having more visual consistency and saving significant time and human effort.

关键词： batch image editing computer vision image editing image generation image manipulation

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