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LANA: A Language-Capable Navigator for Instruction Following and Generation

LANA: A Language-Capable Navigator for Instruction Following...

引用

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

作者： Wang, Xiaohan Wang, Wenguan Shao, Jiayi Yang, Yi Zhejiang Univ CCAI Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Recently, visual-language navigation (VLN) - entailing robot agents to follow navigation instructions - has shown great advance. However, existing literature put most emphasis on interpreting instructions into actions, only delivering "dumb" wayfinding agents. In this article, we devise LANA, a language-capable navigation agent which is able to not only execute human-written navigation commands, but also provide route descriptions to humans. This is achieved by simultaneously learning instruction following and generation with only one single model. More specifically, two encoders, respectively for route and language encoding, are built and shared by two decoders, respectively for action prediction and instruction generation, so as to exploit cross-task knowledge and capture task-specific characteristics. Throughout pretraining and fine-tuning, both instruction following and generation are set as optimization objectives. We empirically verify that, compared with recent advanced task-specific solutions, LANA attains better performances on both instruction following and route description, with nearly half complexity. In addition, endowed with language generation capability, LANA can explain to human its behaviours and assist human's wayfinding. This work is expected to foster future efforts towards building more trustworthy and socially intelligent navigation robots.

关键词： Embodied vision: Active agents simulation

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Towards Efficient Use of Multi-Scale Features in Transformer-Based Object Detectors

Towards Efficient Use of Multi-Scale Features in Transformer...

引用

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

作者： Zhang, Gongjie Luo, Zhipeng Tian, Zichen Zhang, Jingyi Zhang, Xiaoqin Lu, Shijian Nanyang Technol Univ S Lab Singapore Singapore Black Sesame Technol Singapore Singapore SenseTime Res Hong Kong Peoples R China Wenzhou Univ Wenzhou Peoples R China

ISBN: (纸本)9798350301298

Multi-scale features have been proven highly effective for object detection but often come with huge and even prohibitive extra computation costs, especially for the recent Transformer-based detectors. In this paper, we propose Iterative Multi-scale Feature Aggregation (IMFA) - a generic paradigm that enables efficient use of multi-scale features in Transformer-based object detectors. The core idea is to exploit sparse multi-scale features from just a few crucial locations, and it is achieved with two novel designs. First, IMFA rearranges the Transformer encoder-decoder pipeline so that the encoded features can be iteratively updated based on the detection predictions. Second, IMFA sparsely samples scale-adaptive features for refined detection from just a few keypoint locations under the guidance of prior detection predictions. As a result, the sampled multi-scale features are sparse yet still highly beneficial for object detection. Extensive experiments show that the proposed IMFA boosts the performance of multiple Transformer-based object detectors significantly yet with only slight computational overhead.

关键词： detection recognition: Categorization retrieval

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X-3D: Explicit 3D Structure Modeling for Point Cloud recognition

X-3D: Explicit 3D Structure Modeling for Point Cloud Recogni...

引用

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

作者： Sun, Shuofeng Rao, Yongming Lu, Jiwen Yan, Haibin Beijing Univ Posts & Telecommun Beijing Peoples R China Tencent Shenzhen Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Numerous prior studies predominantly emphasize constructing relation vectors for individual neighborhood points and generating dynamic kernels for each vector and embedding these into high-dimensional spaces to capture implicit local structures. However, we contend that such implicit high-dimensional structure modeling approch inadequately represents the local geometric structure of point clouds due to the absence of explicit structural information. Hence, we introduce X-3D, an explicit 3D structure modeling approach. X-3D functions by capturing the explicit local structural information within the input 3D space and employing it to produce dynamic kernels with shared weights for all neighborhood points within the current local region. This modeling approach introduces effective geometric prior and significantly diminishes the disparity between the local structure of the embedding space and the original input point cloud, thereby improving the extraction of local features. Experiments show that our method can be used on a variety of methods and achieves state-of-the-art performance on segmentation, classification, detection tasks with lower extra computational cost, such as 90.7% on ScanObjectNN for classification, 79.2% on S3DIS 6 fold and 74.3% on S3DIS Area 5 for segmentation, 76.3% on ScanNetV2 for segmentation and 64.5% mAP25, 46.9% mAP50 on SUN RGB-D and 69.0% mAP25, 51.1% mAP50 on ScanNetV2. Our code is available at https://***/sunshuofeng/X-3D.

关键词： Vector spaces

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LEOD: Label-Efficient Object Detection for Event Cameras

LEOD: Label-Efficient Object Detection for Event Cameras

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

作者： Wu, Ziyi Gehrig, Mathias Lyu, Qing Liu, Xudong Gilitschenski, Igor Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada Univ Zurich Zurich Switzerland

ISBN: (纸本)9798350353006

Object detection with event cameras benefits from the sensor's low latency and high dynamic range. However, it is costly to fully label event streams for supervised training due to their high temporal resolution. To reduce this cost, we present LEOD, the first method for label-efficient event-based detection. Our approach unifies weakly- and semi-supervised object detection with a self-training mechanism. We first utilize a detector pre-trained on limited labels to produce pseudo ground truth on unlabeled events. Then, the detector is re-trained with both real and generated labels. Leveraging the temporal consistency of events, we run bi-directional inference and apply tracking-based post-processing to enhance the quality of pseudo labels. To stabilize training against label noise, we further design a soft anchor assignment strategy. We introduce new experimental protocols to evaluate the task of label-efficient event-based detection on Gen1 and 1Mpx datasets. LEOD consistently outperforms supervised baselines across various labeling ratios. For example, on Gen1, it improves mAP by 8.6% and 7.8% for RVT-S trained with 1% and 2% labels. On 1Mpx, RVT-S with 10% labels even surpasses its fully-supervised counterpart using 100% labels. LEOD maintains its effectiveness even when all labeled data are available, reaching new state-of-the-art results. Finally, we show that our method readily scales to improve larger detectors as well. Code: https://***/Wuziyi616/LEOD.

关键词： Event-based vision Object Detection Semi-supervised Learning Weakly-supervised Learning

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SOK-Bench: A Situated Video Reasoning Benchmark with Aligned Open-World Knowledge

SOK-Bench: A Situated Video Reasoning Benchmark with Aligned...

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

作者： Wang, Andong Wu, Bo Chen, Sunli Chen, Zhenfang Guan, Haotian Lee, Wei-Ning Li, Li Erran Gan, Chuang Univ Hong Kong Hong Kong Peoples R China MIT IBM Watson AI Lab Cambridge MA USA Tsinghua Univ Beijing Peoples R China AWS AI Seattle WA USA UMass Amherst Amherst MA USA

ISBN: (纸本)9798350353006

Learning commonsense reasoning from visual contexts and scenes in real-world is a crucial step toward advanced artificial intelligence. However, existing video reasoning benchmarks are still inadequate since they were mainly designed for factual or situated reasoning and rarely involve broader knowledge in the real world. Our work aims to delve deeper into reasoning evaluations, specifically within dynamic, open-world, and structured context knowledge. We propose a new benchmark (SOK-Bench), consisting of 44K questions and 10K situations with instance-level annotations depicted in the videos. The reasoning process is required to understand and apply situated knowledge and general knowledge for problem-solving. To create such a dataset, we propose an automatic and scalable generation method to generate question-answer pairs, knowledge graphs, and rationales by instructing the combina-tions of LLMs and MLLMs. Concretely, we first extract observable situated entities, relations, and processes from videos for situated knowledge and then extend to open-world knowledge beyond the visible content. The task generation is facilitated through multiple dialogues as iterations and subsequently corrected and refined by our designed self-promptings and demonstrations. With a corpus of both explicit situated facts and implicit commonsense, we generate associated question-answer pairs and reasoning processes, finally followed by manual reviews for quality assurance. We evaluated recent mainstream large vision-language models on the benchmark and found several insightful conclusions. For more information, please refer to our benchmark at ***/SOKBench.

关键词： benchmark commonsense reasoning video understanding vision language foundation models visual reasoning

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InternVL: Scaling up vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks

InternVL: Scaling up Vision Foundation Models and Aligning f...

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

作者： Chen, Zhe Wu, Jiannan Wang, Wenhai Su, Weijie Chen, Guo Xing, Sen Zhong, Muyan Zhang, Qinglong Zhu, Xizhou Lu, Lewei Li, Bin Luo, Ping Lu, Tong Qiao, Yu Dai, Jifeng Shanghai AI Lab OpenGVLab Shanghai Peoples R China Nanjing Univ Nanjing Peoples R China Univ Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Tsinghua Univ Beijing Peoples R China Univ Sci & Technol China Hefei Peoples R China SenseTime Res Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350353006

The exponential growth of large language models (LLMs) has opened up numerous possibilities for multi-modal AGI systems. However, the progress in vision and vision-language foundation models, which are also critical elements of multi-modal AGI, has not kept pace with LLMs. In this work, we design a large-scale vision-language foundation model (InternVL), which scales up the vision foundation model to 6 billion parameters and progressively aligns it with the LLM, using web-scale image-text data from various sources. This model can be broadly applied to and achieve state-of-the-art performance on 32 generic visual-linguistic benchmarks including visual perception tasks such as image-level or pixel-level recognition, vision-language tasks such as zero-shot image/video classification, zero-shot image/video-text retrieval, and link with LLMs to create multi-modal dialogue systems. It has powerful visual capabilities and can be a good alternative to the ViT-22B. We hope that our research could contribute to the development of multi-modal large models.

关键词： multi-modal vision foundation model vision-language model

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Real-World Mobile Image Denoising Dataset with Efficient Baselines

Real-World Mobile Image Denoising Dataset with Efficient Bas...

引用

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

作者： Flepp, Roman Ignatov, Andrey Timofte, Radu Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Univ Wurzburg Comp Vis Lab Wurzburg Germany AI Witchlabs Ltd Zollikerberg Switzerland

ISBN: (纸本)9798350353006

The recently increased role of mobile photography has raised the standards of on-device photo processing tremendously. Despite the latest advancements in camera hardware, the mobile camera sensor area cannot be increased significantly due to physical constraints, leading to a pixel size of 0.6-2.0 mu m, which results in strong image noise even in moderate lighting conditions. In the era of deep learning, one can train a CNN model to perform robust image denoising. However, there is still a lack of a substantially diverse dataset for this task. To address this problem, we introduce a novel Mobile Image Denoising Dataset (MIDD) comprising over 400,000 noisy / noise-free image pairs captured under various conditions by 20 different mobile camera sensors. Additionally, we propose a new DPreview test set consisting of data from 294 different cameras for precise model evaluation. Furthermore, we present the efficient baseline model SplitterNet for the considered mobile image denoising task that achieves high numerical and visual results, while being able to process 8MP photos directly on smartphone GPUs in under one second. Thereby outperforming models with similar runtimes. This model is also compatible with recent mobile NPUs, demonstrating an even higher speed when deployed on them. The conducted experiments demonstrate high robustness of the proposed solution when applied to images from previously unseen sensors, showing its high generalizability. The datasets, code and models can be found on the official project website (1,2).

关键词： Computational Photography computer vision Dataset Deep Learning Denoising Denoising Dataset Efficient Denoising Model Image Denoising Image Enhancement Image Restoration Mobile Image Denoising

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ST2ST: Self-Supervised Test-time Adaptation for Video Action recognition

ST2ST: Self-Supervised Test-time Adaptation for Video Action...

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

作者： Fahim, Masud An-Nur Islam Innat, Mohammed Boutellier, Jani Univ Vaasa Vaasa Finland Khulna Univ Engn & Technol KUET Khulna Bangladesh

ISBN: (纸本)9798350365474

The performance of trained deep neural network (DNN) models relies on the assumption that the test data has largely the same feature distribution as the training data. In deployed video recognition systems, the feature distribution of acquired samples can however become shifted due to environmental conditions (rain, lighting variations) or technological factors such as lossy data compression. To improve action recognition performance under feature distribution shifts, we propose a simple test-time self-distillation strategy where the DNN model goes through an intra-video logit minimization phase. As a result, the model can update its predictions for the given input. The proposed approach is agnostic to the neural network type (CNN, transformer) and applies to various action recognition models. In contrast to many test-time adaption studies, the proposed approach does not require access to the training data. The performance of the proposed method is evaluated with multiple state-of-the-art action recognition models and widely used benchmark datasets Kinetics-400 and Something-Something V2.

关键词： Action recogniton Self-Supervised TTA

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Generating Diverse Agricultural Data for vision-Based Farming Applications

Generating Diverse Agricultural Data for Vision-Based Farmin...

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

作者： Cieslak, Mikolaj Govindarajan, Umabharathi Garcia, Alejandro Chandrashekar, Anuradha Haedrich, Torsten Mendoza-Drosik, Aleksander Michels, Dominik L. Pirk, Soeren Fu, Chia-Chun Palubicki, Wojciech GreenMatterAI Berlin Germany Blue River Technol Santa Clara CA USA King Abdullah Univ Sci & Technol Thuwal Saudi Arabia Tech Univ Darmstadt Darmstadt Germany Christian Albrecht Univ Kiel Kiel Germany Adam Mickiewicz Univ Poznan Poland

ISBN: (纸本)9798350365474

We present a specialized procedural model for generating synthetic agricultural scenes, focusing on soybean crops, along with various weeds. The model simulates distinct growth stages of these plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. The integration of real-world textures and environmental factors into the procedural generation process enhances the photorealism and applicability of the synthetic data. We validate our model's effectiveness by comparing the synthetic data against real agricultural images, demonstrating its potential to significantly augment training data for machine learning models in agriculture. This approach not only provides a cost-effective solution for generating high-quality, diverse data but also addresses specific needs in agricultural vision tasks that are not fully covered by general-purpose models.

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IDGI: A Framework to Eliminate Explanation Noise from Integrated Gradients

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

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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

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