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Volumetric Environment Representation for vision-Language Navigation

Volumetric Environment Representation for Vision-Language Na...

引用

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

作者： Liu, Rui Wang, Wenguan Yan, Yi Zhejiang Univ CCAI ReLER Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9798350353006

vision-language navigation (VLN) requires an agent to navigate through an 3D environment based on visual observations and natural language instructions. It is clear that the pivotal factor for successful navigation lies in the comprehensive scene understanding. Previous VLN agents employ monocular frameworks to extract 2D features of perspective views directly. Though straightforward, they struggle for capturing 3D geometry and semantics, leading to a partial and incomplete environment representation. To achieve a comprehensive 3D representation with fine-grained details, we introduce a Volumetric Environment Representation (VER), which voxelizes the physical world into structured 3D cells. For each cell, VER aggregates multi-view 2D features into such a unified 3D space via 2D-3D sampling. Through coarse-to-fine feature extraction and multi-task learning for VER, our agent predicts 3D occupancy, 3D room layout, and 3D bounding boxes jointly. Based on online collected VERs, our agent performs volume state estimation and builds episodic memory for predicting the next step. Experimental results show our environment representations from multi-task learning lead to evident performance gains on VLN. Our model achieves state-of-the-art performance across VLN benchmarks (R2R, REVERIE, and R4R).

关键词： Semantics

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Visual Program Distillation: Distilling Tools and Programmatic Reasoning into vision-Language Models

Visual Program Distillation: Distilling Tools and Programmat...

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

作者： Hu, Yushi Stretcu, Otilia Lu, Chun-Ta Viswanathan, Krishnamurthy Hata, Kenji Luo, Enming Krishna, Ranjay Fuxman, Ariel Google Res Mountain View CA 94043 USA Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9798350353006

Solving complex visual tasks such as "Who invented the musical instrument on the right?" involves a composition of skills: understanding space, recognizing instruments, and also retrieving prior knowledge. Recent work shows promise by decomposing such tasks using a large language model (LLM) into an executable program that invokes specialized vision models. However, generated programs are error-prone: they omit necessary steps, include spurious ones, and are unable to recover when the specialized models give incorrect outputs. Moreover, they require loading multiple models, incurring high latency and computation costs. We propose Visual Program Distillation (VPD), an instruction tuning framework that produces a vision-language model (VLM) capable of solving complex visual tasks with a single forward pass. VPD distills the reasoning ability of LLMs by using them to sample multiple candidate programs, which are then executed and verified to identify a correct one. It translates each correct program into a language description of the reasoning steps, which are then distilled into a VLM. Extensive experiments show that VPD improves the VLM's ability to count, understand spatial relations, and reason compositionally. Our VPD-trained PaLI-X outperforms all prior VLMs, achieving state-of-the-art performance across complex vision tasks, including MMBench, OK-VQA, A-OKVQA, TallyQA, POPE, and Hateful Memes. An evaluation with human annotators also confirms that VPD improves model response factuality and consistency. Finally, experiments on content moderation demonstrate that VPD is also helpful for adaptation to real-world applications with limited data.

关键词： cross-modal reasoning distillation instruction tuning large language model multimodal synthetic data tools vision-language model visual programming

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Co-designing a Sub-millisecond Latency Event-based Eye Tracking System with Submanifold Sparse CNN

Co-designing a Sub-millisecond Latency Event-based Eye Track...

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

作者： Zhang, Baoheng Gao, Yizhao Li, Jingyuan So, Hayden Kwok-Hay Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350365474

Eye-tracking technology is integral to numerous consumer electronics applications, particularly in the realm of virtual and augmented reality (VR/AR). These applications demand solutions that excel in three crucial aspects: low-latency, low-power consumption, and precision. Yet, achieving optimal performance across all these fronts presents a formidable challenge, necessitating a balance between sophisticated algorithms and efficient backend hardware implementations. In this study, we tackle this challenge through a synergistic software/hardware co-design of the system with an event camera. Leveraging the inherent sparsity of event-based input data, we integrate a novel sparse FPGA dataflow accelerator customized for submanifold sparse convolution neural networks (SCNN). The SCNN implemented on the accelerator can efficiently extract the embedding feature vector from each representation of event slices by only processing the non-zero activations. Subsequently, these vectors undergo further processing by a gated recurrent unit (GRU) and a fully connected layer on the host CPU to generate the eye centers. Deployment and evaluation of our system reveal outstanding performance metrics. On the Event-based Eye-Tracking-AIS2024 dataset, our system achieves 81% p5 accuracy, 99.5% p10 accuracy, and 3.71 Mean Euclidean Distance with 0.7 ms latency while only consuming 2.29 mJ per inference. Notably, our solution opens up opportunities for future eye-tracking systems. Code is available at https://***/CASRHKU/ESDA/tree/eye_tracking.

关键词： dynamic vision sensor event camera event-based vision eye tracking FPGA hardware-software codesign sparse processing

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EgoGen: An Egocentric Synthetic Data Generator

EgoGen: An Egocentric Synthetic Data Generator

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

作者： Li, Gen Zhao, Kaifeng Zhang, Siwei Lyu, Xiaozhong Dusmanu, Mihai Zhang, Yan Pollefeys, Marc Tang, Siyu Swiss Fed Inst Technol Zurich Switzerland Microsoft Redmond WA USA

ISBN: (纸本)9798350353006

Understanding the world in first-person view is fundamental in Augmented Reality (AR). This immersive perspective brings dramatic visual changes and unique challenges compared to third-person views. Synthetic data has empowered third-person-view vision models, but its application to embodied egocentric perception tasks remains largely unexplored. A critical challenge lies in simulating natural human movements and behaviors that effectively steer the embodied cameras to capture a faithful egocentric representation of the 3D world. To address this challenge, we introduce EgoGen, a new synthetic data generator that can produce accurate and rich ground-truth training data for egocentric perception tasks. At the heart of EgoGen is a novel human motion synthesis model that directly leverages egocentric visual inputs of a virtual human to sense the 3D environment. Combined with collision-avoiding motion primitives and a two-stage reinforcement learning approach, our motion synthesis model offers a closed-loop solution where the embodied perception and movement of the virtual human are seamlessly coupled. Compared to previous works, our model eliminates the need for a predefined global path, and is directly applicable to dynamic environments. Combined with our easy-to-use and scalable data generation pipeline, we demonstrate EgoGen's efficacy in three tasks: mapping and localization for head-mounted cameras, egocentric camera tracking, and human mesh recovery from egocentric views. EgoGen will be fully open-sourced, offering a practical solution for creating realistic egocentric training data and aiming to serve as a useful tool for egocentric computer vision research.

关键词： autonomous virtual humans egocentric vision reinforcement learning synthetic data

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MovieChat: From Dense Token to Sparse Memory for Long Video Understanding

MovieChat: From Dense Token to Sparse Memory for Long Video ...

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

作者： Song, Enxin Chai, Wenhao Wang, Guanhong Zhang, Yucheng Zhou, Haoyang Wu, Feiyang Chi, Haozhe Guo, Xun Ye, Tian Zhang, Yanting Lu, Yan Hwang, Jenq-Neng Wang, Gaoang Zhejiang Univ Hangzhou Peoples R China Univ Washington Seattle WA 98195 USA Microsoft Res Asia Florence Italy Hong Kong Univ Sci & Technol GZ Hong Kong Peoples R China Donghua Univ Shanghai Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798350353006

Recently, integrating video foundation models and large language models to build a video understanding system can overcome the limitations of specific pre-defined vision tasks. Yet, existing systems can only handle videos with very few frames. For long videos, the computation complexity, memory cost, and long-term temporal connection impose additional challenges. Taking advantage of the Atkinson-Shiffrin memory model, with tokens in Transformers being employed as the carriers of memory in combination with our specially designed memory mechanism, we propose the MovieChat to overcome these challenges. MovieChat achieves state-of-the-art performance in long video understanding, along with the released MovieChat-1K bench-mark with 1K long video and 14K manual annotations for validation of the effectiveness of our method. The code, models and data can be found in https://***/MovieChat.

关键词： computer simulation languages

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SCoFT: Self-Contrastive Fine-Tuning for Equitable Image Generation

SCoFT: Self-Contrastive Fine-Tuning for Equitable Image Gene...

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

作者： Liu, Zhixuan Schaldenbrand, Peter Okogwu, Beverley-Claire Peng, Wenxuan Yun, Youngsik Hundt, Andrew Kim, Jihie Oh, Jean Carnegie Mellon Univ Pittsburgh PA 15213 USA Nanyang Technol Univ Singapore Singapore Dongguk Univ Seoul South Korea

ISBN: (纸本)9798350353006

Accurate representation in media is known to improve the well-being of the people who consume it. Generative image models trained on large web-crawled datasets such as LAION are known to produce images with harmful stereotypes and misrepresentations of cultures. We improve inclusive representation in generated images by (1) engaging with communities to collect a culturally representative dataset that we call the Cross-Cultural Under-standing Benchmark (CCUB) and (2) proposing a novel Self- Contrastive Fine-Tuning (SCoFT, pronounced /soft/) method that leverages the model's known biases to self-improve. SCoFT is designed to prevent overfitting on small datasets, encode only high-level information from the data, and shift the generated distribution away from misrepresentations encoded in a pretrained model. Our user study conducted on 51 participants from 5 different countries based on their self-selected national cultural affiliation shows that fine-tuning on CCUB consistently generates images with higher cultural relevance and fewer stereotypes when compared to the Stable Diffusion baseline, which is further improved with our SCoFT technique. Resources and code are at https://***/SCoFT.

关键词： computer vision for Social Good Image Synthesis

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MULTIFLOW: Shifting Towards Task-Agnostic vision-Language Pruning

MULTIFLOW: Shifting Towards Task-Agnostic Vision-Language Pr...

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

作者： Farina, Matteo Mancini, Massimiliano Cunegatti, Elia Liu, Gaowen Iacca, Giovanni Ricci, Elisa Univ Trento Trento Italy Cisco Res Res Triangle Pk NC USA Fdn Bruno Kessler Povo Italy

ISBN: (纸本)9798350353006

While excellent in transfer learning, vision-Language models (VLMs) come with high computational costs due to their large number of parameters. To address this issue, removing parameters via model pruning is a viable solution. However, existing techniques for VLMs are task-specific, and thus require pruning the network from scratch for each new task of interest. In this work, we explore a new direction: Task-Agnostic vision-Language Pruning (TA-VLP). Given a pretrained VLM, the goal is to find a unique pruned counterpart transferable to multiple unknown downstream tasks. In this challenging setting, the transferable representations already encoded in the pretrained model are a key aspect to preserve. Thus, we propose Multimodal Flow Pruning (MULTIFLOW), a first, gradient-free, pruning framework for TA-VLP where: (i) the importance of a parameter is expressed in terms of its magnitude and its information flow, by incorporating the saliency of the neu-rons it connects;and (ii) pruning is driven by the emergent (multimodal) distribution of the VLM parameters after pretraining. We benchmark eight state-of-the-art pruning algorithms in the context of TA-VLP, experimenting with two VLMs, three vision-language tasks, and three pruning ratios. Our experimental results show that MULTIFLOW outperforms recent sophisticated, combinatorial competitors in the vast majority of the cases, paving the way towards addressing TA-VLP. The code is publicly available at https://***/FarinaMatteo/multiflow.

关键词： multimodal learning neural network pruning sparse neural networks transfer learning vision-language models

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Sharingan: A Transformer Architecture for Multi-Person Gaze Following

Sharingan: A Transformer Architecture for Multi-Person Gaze ...

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

作者： Tafasca, Samy Gupta, Anshul Odobez, Jean-Marc Idiap Res Inst Martigny Switzerland Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9798350353013;9798350353006

Gaze is a powerful form of non-verbal communication that humans develop from an early age. As such, modeling this behavior is an important task that can benefit a broad set of application domains ranging from robotics to sociology. In particular, the gaze following task in computer vision is defined as the prediction of the 2D pixel coordinates where a person in the image is looking. Previous attempts in this area have primarily centered on CNN-based architectures, but they have been constrained by the need to process one person at a time, which proves to be highly inefficient. In this paper, we introduce a novel and effective multi-person transformer-based architecture for gaze prediction. While there exist prior works using transformers for multi-person gaze prediction [38, 39], they use a fixed set of learnable embeddings to decode both the person and its gaze target, which requires a matching step afterward to link the predictions with the annotations. Thus, it is difficult to quantitatively evaluate these methods reliably with the available benchmarks, or integrate them into a larger human behavior understanding system. Instead, we are the first to propose a multi-person transformer-based architecture that maintains the original task formulation and ensures control over the people fed as input. Our main contribution lies in encoding the person-specific information into a single controlled token to be processed alongside image tokens and using its output for prediction based on a novel multiscale decoding mechanism. Our new architecture achieves state-of-the-art results on the GazeFollow, VideoAttentionTarget, and ChildPlay datasets and outper-forms comparable multi-person architectures with a notable margin. Our code, checkpoints, and data extractions will be made publicly available soon.

关键词： computer vision deep learning gaze following

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VideoGrounding-DINO: Towards Open-Vocabulary Spatio-Temporal Video Grounding

VideoGrounding-DINO: Towards Open-Vocabulary Spatio-Temporal...

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

作者： Wasim, Syed Talal Naseer, Muzammal Khan, Salman Yang, Ming-Hsuan Khan, Fahad Shahbaz Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates Australian Natl Univ Canberra Australia Univ Calif Merced Merced CA USA Google Res Mountain View CA USA Linkoping Univ Linkoping Sweden

ISBN: (纸本)9798350353006

Video grounding aims to localize a spatio-temporal section in a video corresponding to an input text query. This paper addresses a critical limitation in current video grounding methodologies by introducing an Open-Vocabulary Spatio-Temporal Video Grounding task. Unlike prevalent closed-set approaches that struggle with open-vocabulary scenarios due to limited training data and pre-defined vocabularies, our model leverages pre-trained representations from foundational spatial grounding models. This empowers it to effectively bridge the semantic gap between natural language and diverse visual content, achieving strong performance in closed-set and open-vocabulary settings. Our contributions include a novel spatio-temporal video grounding model, surpassing state-of-the-art results in closed-set evaluations on multiple datasets and demonstrating superior performance in open-vocabulary scenarios. Notably, the proposed model outperforms state-of-the-art methods in closed-set settings on VidSTG (Declarative and Interrogative) and HC-STVG (V1 and V2) datasets. Furthermore, in open-vocabulary evaluations on HC-STVG V1 and YouCook-Interactions, our model surpasses the recent best-performing models by 4.88 m vIoU and 1.83% accuracy, demonstrating its efficacy in handling diverse linguistic and visual concepts for improved video understanding. Our codes will be publicly released.

关键词： MultiModal Open Vocabulary Video Grounding vision Language

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Language-driven Grasp Detection

Language-driven Grasp Detection

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

作者： An Dinh Vuong Minh Nhat Vu Baoru Huang Nghia Nguyen Hieu Le Thieu Vo Anh Nguyen FPT Software AI Ctr Hanoi Vietnam TU Wien Automat Control Inst Vienna Austria Imperial Coll London London England Ton Duc Thang Univ Ho Chi Minh City Vietnam Univ Liverpool Liverpool Merseyside England

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353006

Grasp detection is a persistent and intricate challenge with various industrial applications. Recently, many methods and datasets have been proposed to tackle the grasp detection problem. However, most of them do not consider using natural language as a condition to detect the grasp poses. In this paper, we introduce Grasp-Anything++, a new language- driven grasp detection dataset featuring 1M samples, over 3M objects, and upwards of 10M grasping instructions. We utilize foundation models to create a large-scale scene corpus with corresponding images and grasp prompts. We approach the language-driven grasp detection task as a conditional generation problem. Drawing on the success of diffusion models in generative tasks and given that language plays a vital role in this task, we propose a new language-driven grasp detection method based on diffusion models. Our key contribution is the contrastive training objective, which explicitly contributes to the denoising process to detect the grasp pose given the language instructions. We illustrate that our approach is theoretically supportive. The intensive experiments show that our method outperforms state-of-the-art approaches and allows real-world robotic grasping. Finally, we demonstrate our large-scale dataset enables zero-short grasp detection and is a challenging benchmark for future work.

关键词： Training computer vision Noise reduction Natural languages Grasping Benchmark testing Diffusion models

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