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Pre-trained vision and Language Transformers Are Few-Shot Incremental Learners

Pre-trained Vision and Language Transformers Are Few-Shot In...

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

作者： Park, Keon-Hee Song, Kyungwoo Park, Gyeong-Moon Kyung Hee Univ Seoul South Korea Yonsei Univ Seoul South Korea

ISBN: (纸本)9798350353006

Few-Shot Class Incremental Learning (FSCIL) is a task that requires a model to learn new classes incrementally without forgetting when only a few samples for each class are given. FSCIL encounters two significant challenges: catastrophic forgetting and overfitting, and these challenges have driven prior studies to primarily rely on shallow models, such as ResNet-18. Even though their limited capacity can mitigate both forgetting and overfitting issues, it leads to inadequate knowledge transfer during few-shot incremental sessions. In this paper, we argue that large models such as vision and language transformers pre-trained on large datasets can be excellent few-shot incremental learners. To this end, we propose a novel FSCIL framework called PriViLege, Pre-trained vision and Language transformers with prompting functions and knowledge distillation. Our framework effectively addresses the challenges of catastrophic forgetting and overfitting in large models through new pre-trained knowledge tuning (PKT) and two losses: entropy-based divergence loss and semantic knowledge distillation loss. Experimental results show that the proposed PriViLege significantly outperforms the existing state-of-the-art methods with a large margin, e.g., +9.38% in CUB200, +20.58% in CIFAR-100, and +13.36% in miniImageNet. Our implementation code is available at https://***/KHU-AGI/PriViLege.

关键词： Few-shot learning Incremental learning Parameter Efficient Tuning

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ESR-NeRF: Emissive Source Reconstruction Using LDR Multi-view Images

ESR-NeRF: Emissive Source Reconstruction Using LDR Multi-vie...

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

作者： Jeong, Jinseo Koo, Junseo Zhang, Qimeng Kim, Gunhee Seoul Natl Univ Seoul South Korea Korea Univ Seoul South Korea

ISBN: (纸本)9798350353013;9798350353006

Existing NeRF-based inverse rendering methods suppose that scenes are exclusively illuminated by distant light sources, neglecting the potential influence of emissive sources within a scene. In this work, we confront this limitation using LDR multi-view images captured with emissive sources turned on and off. Two key issues must be addressed: 1) ambiguity arising from the limited dynamic range along with unknown lighting details, and 2) the expensive computational cost in volume rendering to backtrace the paths leading to final object colors. We present a novel approach, ESR-NeRF, leveraging neural networks as learnable functions to represent ray-traced fields. By training networks to satisfy light transport segments, we regulate outgoing radiances, progressively identifying emissive sources while being aware of reflection areas. The results on scenes encompassing emissive sources with various properties demonstrate the superiority of ESR-NeRF in qualitative and quantitative ways. Our approach also extends its applicability to the scenes devoid of emissive sources, achieving lower CD metrics on the DTU dataset.

关键词： Inverse-rendering NeRF Scene-editing vision + graphics

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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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VideoCon: Robust Video-Language Alignment via Contrast Captions

VideoCon: Robust Video-Language Alignment via Contrast Capti...

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

作者： Bansall, Hritik Bitton, Yonatan Szpektor, Idan Chang, Kai-Wei Grover, Aditya UCLA Los Angeles CA 90095 USA Google Res Mountain View CA USA

ISBN: (纸本)9798350353006

Despite being (pre)trained on a massive amount of data, state-of-the-art video-language alignment models are not robust to semantically-plausible contrastive changes in the video captions. Our work addresses this by identifying a broad spectrum of contrast misalignments, such as replacing entities, actions, and flipping event order, which alignment models should be robust against. To this end, we introduce the VideoCon, a video-language alignment dataset constructed by a large language model that generates plausible contrast video captions and explanations for differences between original and contrast video captions. Then, a generative video-language model is fine-tuned with VideoCon to assess video-language entailment and generate explanations. Our VideoCon-based alignment model significantly outperforms current models. It exhibits a 12-point increase in AUC for the video-language alignment task on human-generated contrast captions. Finally, our model sets new state of the art zero-shot performance in temporally-extensive video-language tasks such as text-to-video retrieval (SSv2-Temporal) and video question answering (ATP-Hard). Moreover, our model shows superior performance on novel videos and human-crafted captions and explanations.

关键词： alignment explanation Language large multimodal models Multimodal Video Video-Language vision-language

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Rethinking Supervised Depth Estimation for 360° Panoramic Imagery

Rethinking Supervised Depth Estimation for 360° Panoramic I...

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

作者： He, Lu Jian, Bing Wen, Yangming Zhu, Haichao Liu, Kelin Feng, Weiwei Liu, Shan Tencent Amer Palo Alto CA 94306 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Depth estimation from a single 360 degrees panorama image is a difficult task. It is an ill-posed problem to estimate depth maps from an RGB panorama image due to the intrinsic scale ambiguity issue. To mitigate the scale inconsistency issue in the ground truth depth map, we propose a simple yet effective method to normalize the depth data based on estimated camera height. In addition, we design a multiple head planar-guided depth network, to provide more geometric constraints for depth estimation. Experimental results show that our relative depth estimation task is more accurate than the absolute depth estimation task, and our proposed model produces state-of-the-art performance on both Matterport3D and Stanford2D3D datasets.

关键词： Measurement computer vision Head conferences Estimation Data models pattern recognition

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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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Generating Enhanced Negatives for Training Language-Based Object Detectors

Generating Enhanced Negatives for Training Language-Based Ob...

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

作者： Zhao, Shiyu Zhao, Long Kumar, Vijay B. G. Suh, Yumin Metaxas, Dimitris N. Chandraker, Manmohan Schulter, Samuel Rutgers State Univ New Brunswick NJ 08901 USA NEC Labs Amer Princeton NJ USA Google Res Mountain View CA USA Univ Calif San Diego La Jolla CA USA

ISBN: (纸本)9798350353006

The recent progress in language-based open-vocabulary object detection can be largely attributed to finding better ways of leveraging large-scale data with free-form text annotations. Training such models with a discriminative objective function has proven successful, but requires good positive and negative samples. However, the free-form nature and the open vocabulary of object descriptions make the space of negatives extremely large. Prior works randomly sample negatives or use rule-based techniques to build them. In contrast, we propose to leverage the vast knowledge built into modern generative models to automatically build negatives that are more relevant to the original data. Specifically, we use large-Language-models to generate negative text descriptions, and text-to-image diffusion models to also generate corresponding negative images. Our experimental analysis confirms the relevance of the generated negative data, and its use in language-based detectors improves performance on two complex benchmarks. Code is available at https://***/xiaofeng94/Gen-Enhanced-Negs.

关键词： large language model negative example mining open-vocabulary object detection vision and language

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MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-Task Learning

MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-T...

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

作者： Agiza, Ahmed Neseem, Marina Reda, Sherief Brown Univ Providence RI 02912 USA

ISBN: (纸本)9798350353006

Adapting models pre-trained on large-scale datasets to a variety of downstream tasks is a common strategy in deep learning. Consequently, parameter-efficient fine-tuning methods have emerged as a promising way to adapt pre-trained models to different tasks while training only a minimal number of parameters. While most of these methods are designed for single-task adaptation, parameter-efficient training in Multi-Task Learning (MTL) architectures is still unexplored. In this paper, we introduce MTLoRA, a novel framework for parameter-efficient training of MTL models. MTLoRA employs Task-Agnostic and Task-Specific Low-Rank Adaptation modules, which effectively disentangle the parameter space in MTL fine-tuning, thereby enabling the model to adeptly handle both task specialization and interaction within MTL contexts. We applied MTLoRA to hierarchical-transformer-based MTL architectures, adapting them to multiple downstream dense prediction tasks. Our extensive experiments on the PASCAL dataset show that MTLoRA achieves higher accuracy on downstream tasks compared to fully fine-tuning the MTL model while reducing the number of trainable parameters by 3.6x. Furthermore, MTLoRA establishes a Pareto-optimal trade-off between the number of trainable parameters and the accuracy of the downstream tasks, outperforming current state-of-the-art parameter-efficient training methods in both accuracy and efficiency. Our code is publicly available.(1)

关键词： efficient multi-task learning hierarchical transformers LoRA low-rank adapatation multi-task learning parameter-efficient training PEFT vision transformers

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DeCAtt: Efficient vision Transformers with Decorrelated Attention Heads

DeCAtt: Efficient Vision Transformers with Decorrelated Atte...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Bhattacharyya, Mayukh Chattopadhyay, Soumitri Nag, Sayan Stony Brook University United States Jadavpur University India University of Toronto Canada

ISBN: (纸本)9798350302493

The advent of vision Transformers (ViT) has led to significant performance gains across various computer vision tasks over the last few years, surpassing the de facto standard CNN architectures. However, most of the prominent variations of vision Transformers are resource-intensive architectures with huge parameter sizes. They are known to be data-hungry and overfit quickly on comparatively smaller datasets. Consequently, this holds back their widespread usage across low-resource settings, which brings forth the need to develop resource-efficient vision transformers. To this end, we introduce a regularization loss that prioritizes efficient utilization of model parameters by decorrelating the heads of a multi-headed attention block in a vision transformer. This forces the heads to learn distinct features rather than focus on the same ones. Using this loss provides a consistent performance improvement over a wide range of varying scenarios of models and datasets as we show in our experiments, which proves its superior effectiveness. © 2023 ieee.

关键词： computer vision

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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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