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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

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IDGI: A Framework to Eliminate Explanation Noise from Integrated Gradients

IDGI: A Framework to Eliminate Explanation Noise from Integr...

引用

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

作者： Yang, Ruo Wang, Binghui Bilgic, Mustafa IIT Dept Comp Sci Chicago IL 60616 USA

ISBN: (纸本)9798350301298

Integrated Gradients (IG) as well as its variants are well-known techniques for interpreting the decisions of deep neural networks. While IG-based approaches attain state-of-the-art performance, they often integrate noise into their explanation saliency maps, which reduce their interpretability. To minimize the noise, we examine the source of the noise analytically and propose a new approach to reduce the explanation noise based on our analytical findings. We propose the Important Direction Gradient Integration (IDGI) framework, which can be easily incorporated into any IG-based method that uses the Reimann Integration for integrated gradient computation. Extensive experiments with three IG-based methods show that IDGI improves them drastically on numerous interpretability metrics. The source code for IDGI is available at https://***/yangruo1226/IDGI.

关键词： Explainable computer vision

来源：评论

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Behavioral Analysis of vision-and-Language Navigation Agents

Behavioral Analysis of Vision-and-Language Navigation Agents

引用

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

作者： Yang, Zijiao Majumdar, Arjun Lee, Stefan Oregon State Univ Corvallis OR 97331 USA Georgia Inst Technol Atlanta GA USA

ISBN: (纸本)9798350301298

To be successful, vision-and-Language Navigation (VLN) agents must be able to ground instructions to actions based on their surroundings. In this work, we develop a methodology to study agent behavior on a skill-specific basis - examining how well existing agents ground instructions about stopping, turning, and moving towards specified objects or rooms. Our approach is based on generating skill-specific interventions and measuring changes in agent predictions. We present a detailed case study analyzing the behavior of a recent agent and then compare multiple agents in terms of skill-specific competency scores. This analysis suggests that biases from training have lasting effects on agent behavior and that existing models are able to ground simple referring expressions. Our comparisons between models show that skill-specific scores correlate with improvements in overall VLN task performance.

关键词： language reasoning vision

来源：评论

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NTIRE 2024 Quality Assessment of AI-Generated Content Challenge

NTIRE 2024 Quality Assessment of AI-Generated Content Challe...

引用

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

作者： Liu, Xiaohong Min, Xiongkuo Zhai, Guangtao Li, Chunyi Kou, Tengchuan Sun, Wei Wu, Haoning Gao, Yixuan Cao, Yuqin Zhang, Zicheng Wu, Xiele Timofte, Radu Peng, Fei Fu, Huiyuan Ming, Anlong Wang, Chuanming Ma, Huadong He, Shuai Dou, Zifei Chen, Shu Zhang, Huacong Xie, Haiyi Wang, Chengwei Chen, Baoying Zeng, Jishen Yang, Jianquan Wang, Weigang Fang, Xi Lv, Xiaoxin Yan, Jun Zhi, Tianwu Zhang, Yabin Li, Yaohui Li, Yang Xu, Jingwen Liu, Jianzhao Liao, Yiting Li, Junlin Yu, Zihao Guan, Fengbin Lu, Yiting Li, Xin Motamednia, Hossein Hosseini-Benvidi, S. Farhad Mahmoudi-Aznaveh, Ahmad Mansouri, Azadeh Gankhuyag, Ganzorig Yoon, Kihwan Xu, Yifang Fan, Haotian Kong, Fangyuan Zhao, Shiling Dong, Weifeng Yin, Haibing Zhu, Li Wang, Zhiling Huang, Bingchen Saha, Avinab Mishra, Sandeep Gupta, Shashank Sureddi, Rajesh Saha, Oindrila Celona, Luigi Bianco, Simone Napoletano, Paolo Schettini, Raimondo Yang, Junfeng Fu, Jing Zhang, Wei Cao, Wenzhi Liu, Limei Peng, Han Yuan, Weijun Li, Zhan Cheng, Yihang Deng, Yifan Li, Haohui Qu, Bowen Li, Yao Luo, Shuqing Wang, Shunzhou Gao, Wei Lu, Zihao Conde, Marcos, V Wang, Xinrui Chen, Zhibo Liao, Ruling Ye, Yan Wang, Qiulin Li, Bing Zhou, Zhaokun Geng, Miao Chen, Rui Tao, Xin Liang, Xiaoyu Sun, Shangkun Ma, Xingyuan Li, Jiaze Yang, Mengduo Xu, Haoran Zhou, Jie Zhu, Shiding Yu, Bohan Chen, Pengfei Xu, Xinrui Shen, Jiabin Duan, Zhichao Asadi, Erfan Liu, Jiahe Yan, Qi Qu, Youran Zeng, Xiaohui Wang, Lele Liao, Renjie

ISBN: (纸本)9798350365474

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at cvpr 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC.

关键词： Video analysis

来源：评论

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引用

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

作者： Shaharabany, Tal Wolf, Lior Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9798350301298

A phrase grounding model receives an input image and a text phrase and outputs a suitable localization map. We present an effective way to refine a phrase ground model by considering self-similarity maps extracted from the latent representation of the model's image encoder. Our main insights are that these maps resemble localization maps and that by combining such maps, one can obtain useful pseudo-labels for performing self-training. Our results surpass, by a large margin, the state of the art in weakly supervised phrase grounding. A similar gap in performance is obtained for a recently proposed downstream task called WWbL, in which only the image is input, without any text. Our code is available at https://***/talshaharabany/Similarity-Maps-forSelf-Training-Weakly-Supervised- Phrase-Grounding.

关键词： language reasoning vision

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RGB no more: Minimally-decoded JPEG vision Transformers

RGB no more: Minimally-decoded JPEG Vision Transformers

引用

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

作者： Park, Jeongsoo Johnson, Justin Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9798350301298

Most neural networks for computer vision are designed to infer using RGB images. However, these RGB images are commonly encoded in JPEG before saving to disk;decoding them imposes an unavoidable overhead for RGB networks. Instead, our work focuses on training vision Transformers (ViT) directly from the encoded features of JPEG. This way, we can avoid most of the decoding overhead, accelerating data load. Existing works have studied this aspect but they focus on CNNs. Due to how these encoded features are structured, CNNs require heavy modification to their architecture to accept such data. Here, we show that this is not the case for ViTs. In addition, we tackle data augmentation directly on these encoded features, which to our knowledge, has not been explored in-depth for training in this setting. With these two improvements - ViT and data augmentation - we show that our ViT-Ti model achieves up to 39.2% faster training and 17.9% faster inference with no accuracy loss compared to the RGB counterpart. (1)

关键词： and reasoning language vision

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Neural Fourier Filter Bank

Neural Fourier Filter Bank

引用

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

作者： Wu, Zhijie Jin, Yuhe Yi, Kwang Moo Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9798350301298

We present a novel method to provide efficient and highly detailed reconstructions. Inspired by wavelets, we learn a neural field that decompose the signal both spatially and frequency-wise. We follow the recent grid-based paradigm for spatial decomposition, but unlike existing work, encourage specific frequencies to be stored in each grid via Fourier features encodings. We then apply a multi-layer perceptron with sine activations, taking these Fourier encoded features in at appropriate layers so that higher-frequency components are accumulated on top of lower-frequency components sequentially, which we sum up to form the final output. We demonstrate that our method outperforms the state of the art regarding model compactness and convergence speed on multiple tasks: 2D image fitting, 3D shape reconstruction, and neural radiance fields. Our code is available at https://***/ubc-vision/NFFB.

关键词： vision + graphics

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Doubly Right Object recognition: A Why Prompt for Visual Rationales

Doubly Right Object Recognition: A Why Prompt for Visual Rat...

引用

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

作者： Mao, Chengzhi Teotial, Revant Sundari, Amrutha Menon, Sachit Yang, Junfeng Wang, Xin Vondrick, Carl Columbia Univ New York NY 10027 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9798350301298

Many visual recognition models are evaluated only on their classification accuracy, a metric for which they obtain strong performance. In this paper, we investigate whether computer vision models can also provide correct rationales for their predictions. We propose a "doubly right" object recognition benchmark, where the metric requires the model to simultaneously produce both the right labels as well as the right rationales. We find that state-of-the-art visual models, such as CLIP, often provide incorrect rationales for their categorical predictions. However, by transferring the rationales from language models into visual representations through a tailored dataset, we show that we can learn a "why prompt," which adapts large visual representations to produce correct rationales. Visualizations and empirical experiments show that our prompts significantly improve performance on doubly right object recognition, in addition to zero-shot transfer to unseen tasks and datasets.

关键词： Explainable computer vision

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Improving Image recognition by Retrieving from Web-Scale Image-Text Data

Improving Image Recognition by Retrieving from Web-Scale Ima...

引用

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

作者： Iscen, Ahmet Fathi, Alireza Schmid, Cordelia Google Res Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

Retrieval augmented models are becoming increasingly popular for computer vision tasks after their recent success in NLP problems. The goal is to enhance the recognition capabilities of the model by retrieving similar examples for the visual input from an external memory set. In this work, we introduce an attention-based memory module, which learns the importance of each retrieved example from the memory. Compared to existing approaches, our method removes the influence of the irrelevant retrieved examples, and retains those that are beneficial to the input query. We also thoroughly study various ways of constructing the memory dataset. Our experiments show the benefit of using a massive-scale memory dataset of 1B image-text pairs, and demonstrate the performance of different memory representations. We evaluate our method in three different classification tasks, namely long-tailed recognition, learning with noisy labels, and fine-grained classification, and show that it achieves state-of-the-art accuracies in ImageNet-LT, Places-LT and Webvision datasets.

关键词： detection recognition: Categorization retrieval

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Decentralized Learning with Multi-Headed Distillation

Decentralized Learning with Multi-Headed Distillation

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

作者： Zhmogiov, Andrey Sandler, Mark Miller, Nolan Kristiansen, Gus Vladymyrov, Max Google AI 1600 Amphitheatre Pkwy Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

Decentralized learning with private data is a central problem in machine learning. We propose a novel distillation-based decentralized learning technique that allows multiple agents with private non-iid data to learn from each other, without having to share their data, weights or weight updates. Our approach is communication efficient, utilizes an unlabeled public dataset and uses multiple auxiliary heads for each client, greatly improving training efficiency in the case of heterogeneous data. This approach allows individual models to preserve and enhance performance on their private tasks while also dramatically improving their performance on the global aggregated data distribution. We study the effects of data and model architecture heterogeneity and the impact of the underlying communication graph topology on learning efficiency and show that our agents can significantly improve their performance compared to learning in isolation.

关键词： Efficient and scalable vision

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LayoutDM: Discrete Diffusion Model for Controllable Layout Generation

LayoutDM: Discrete Diffusion Model for Controllable Layout G...

引用

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

作者： Inoue, Naoto Kikuchi, Kotaro Simo-Serra, Edgar Otani, Mayu Yamaguchi, Kota CyberAgent Tokyo Japan Waseda Univ Tokyo Japan

ISBN: (纸本)9798350301298

Controllable layout generation aims at synthesizing plausible arrangement of element bounding boxes with optional constraints, such as type or position of a specific element. In this work, we try to solve a broad range of layout generation tasks in a single model that is based on discrete state-space diffusion models. Our model, named LayoutDM, naturally handles the structured layout data in the discrete representation and learns to progressively infer a noiseless layout from the initial input, where we model the layout corruption process by modality-wise discrete diffusion. For conditional generation, we propose to inject layout constraints in the form of masking or logit adjustment during inference. We show in the experiments that our LayoutDM successfully generates high-quality layouts and outperforms both task-specific and task-agnostic baselines on several layout tasks.(1)

关键词： vision + graphics

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