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Leverage Interactive Affinity for Affordance Learning

Leverage Interactive Affinity for Affordance Learning

引用

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

作者： Luo, Hongchen Zhai, Wei Zhang, Jing Cao, Yang Tao, Dacheng Univ Sci & Technol China Hefei Peoples R China Univ Sydney Camperdown Australia JD Explore Acad Beijing Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

ISBN: (纸本)9798350301298

Perceiving potential "action possibilities" (i.e., affordance) regions of images and learning interactive functionalities of objects from human demonstration is a challenging task due to the diversity of human-object interactions. Prevailing affordance learning algorithms often adopt the label assignment paradigm and presume that there is a unique relationship between functional region and affordance label, yielding poor performance when adapting to unseen environments with large appearance variations. In this paper, we propose to leverage interactive affinity for affordance learning, *** interactive affinity from human-object interaction and transferring it to non-interactive objects. Interactive affinity, which represents the contacts between different parts of the human body and local regions of the target object, can provide inherent cues of interconnectivity between humans and objects, thereby reducing the ambiguity of the perceived action possibilities. Specifically, we propose a pose-aided interactive affinity learning framework that exploits human pose to guide the network to learn the interactive affinity from human-object interactions. Particularly, a keypoint heuristic perception (KHP) scheme is devised to exploit the keypoint association of human pose to alleviate the uncertainties due to interaction diversities and contact occlusions. Besides, a contact-driven affordance learning (CAL) dataset is constructed by collecting and labeling over 5, 000 images. Experimental results demonstrate that our method outperforms the representative models regarding objective metrics and visual quality. Code and dataset: ***/lhc1224/PIAL-Net.

关键词： Embodied vision: Active agents simulation

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Grounding Counterfactual Explanation of Image Classifiers to Textual Concept Space

Grounding Counterfactual Explanation of Image Classifiers to...

引用

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

作者： Kim, Siwon Oh, Jinoh Lee, Sungjin Yu, Seunghak Doe, Jaeyoung Taghavi, Tara Seoul Natl Univ Data Sci & Artificial Intelligence Lab Seoul South Korea Amazon Alexa AI Seattle WA USA NAVER Search US Seongnam South Korea

ISBN: (纸本)9798350301298

Concept-based explanation aims to provide concise and human-understandable explanations of an image classifier. However, existing concept-based explanation methods typically require a significant amount of manually collected concept-annotated images. This is costly and runs the risk of human biases being involved in the explanation. In this paper, we propose Counterfactual explanation with text-driven concepts (CounTEX), where the concepts are defined only from text by leveraging a pre-trained multimodal joint embedding space without additional concept-annotated datasets. A conceptual counterfactual explanation is generated with text-driven concepts. To utilize the text-driven concepts defined in the joint embedding space to interpret target classifier outcome, we present a novel projection scheme for mapping the two spaces with a simple yet effective implementation. We show that CounTEX generates faithful explanations that provide a semantic understanding of model decision rationale robust to human bias.

关键词： Explainable computer vision

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Once for Both: Single Stage of Importance and Sparsity Search for vision Transformer Compression

Once for Both: Single Stage of Importance and Sparsity Searc...

引用

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

作者： Ye, Hancheng Yu, Chong Ye, Peng Xia, Renqiu Tang, Yansong Lu, Jiwen Chen, Tao Zhang, Bo Fudan Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Fudan Univ Acad Engn & Technol Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Recent vision Transformer Compression (VTC) works mainly follow a two-stage scheme, where the importance score of each model unit is first evaluated or preset in each submodule, followed by the sparsity score evaluation according to the target sparsity constraint. Such a separate evaluation process induces the gap between importance and sparsity score distributions, thus causing high search costs for VTC. In this work, for the first time, we investigate how to integrate the evaluations of importance and sparsity scores into a single stage, searching the optimal subnets in an efficient manner. Specifically, we present OFB, a cost-efficient approach that simultaneously evaluates both importance and sparsity scores, termed Once for Both (OFB), for VTC. First, a bi-mask scheme is developed by entangling the importance score and the differentiable sparsity score to jointly determine the pruning potential (prunability) of each unit. Such a bi-mask search strategy is further used together with a proposed adaptive one-hot loss to realize the progressive-andefficient search for the most important subnet. Finally, Progressive Masked Image Modeling (PMIM) is proposed to regularize the feature space to be more representative during the search process, which may be degraded by the dimension reduction. Extensive experiments demonstrate that OFB can achieve superior compression performance over state-of-the-art searching-based and pruning-based methods under various vision Transformer architectures, meanwhile promoting search efficiency significantly, e.g., costing one GPU search day for the compression of DeiT-S on ImageNet-1K.

关键词： Image compression

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Fusing Pre-trained Language Models with Multimodal Prompts through Reinforcement Learning

Fusing Pre-trained Language Models with Multimodal Prompts t...

引用

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

作者： Yu, Youngjae Chung, Jiwan Yun, Heeseung Hessel, Jack Park, Jae Sung Lu, Ximing Zellers, Rowan Ammanabrolu, Prithviraj Le Bras, Ronan Kim, Gunhee Choi, Yejin Allen Inst Artificial Intelligence Seattle WA USA OpenAI Seattle WA USA Yonsei Univ Dept Artificial Intelligence Seoul South Korea Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea Univ Washington Paul G Allen Sch Comp Sci Seattle WA 98195 USA

ISBN: (纸本)9798350301298

Language models are capable of commonsense reasoning: while domain-specific models can learn from explicit knowledge (e.g. commonsense graphs [6], ethical norms [25]), and larger models like GPT-3 [7] manifest broad commonsense reasoning capacity. Can their knowledge be extended to multimodal inputs such as images and audio without paired domain data? In this work, we propose ESPER (Extending Sensory PErception with Reinforcement learning) which enables text-only pretrained models to address multimodal tasks such as visual commonsense reasoning. Our key novelty is to use reinforcement learning to align multimodal inputs to language model generations without direct supervision: for example, our reward optimization relies only on cosine similarity derived from CLIP [52] and requires no additional paired (image, text) data. Experiments demonstrate that ESPER outperforms baselines and prior work on a variety of multimodal text generation tasks ranging from captioning to commonsense reasoning;these include a new benchmark we collect and release, the ESP dataset, which tasks models with generating the text of several different domains for each image. Our code and data are publicly released at https://***/JiwanChung/esper.

关键词： and reasoning language vision

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GeneCIS: A Benchmark for General Conditional Image Similarity

GeneCIS: A Benchmark for General Conditional Image Similarit...

引用

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

作者： Vaze, Sagar Carion, Nicolas Misra, Ishan Meta AI FAIR Menlo Pk CA 94025 USA Univ Oxford VGG Oxford England

ISBN: (纸本)9798350301298

We argue that there are many notions of 'similarity' and that models, like humans, should be able to adapt to these dynamically. This contrasts with most representation learning methods, supervised or self-supervised, which learn a fixed embedding function and hence implicitly assume a single notion of similarity. For instance, models trained on ImageNet are biased towards object categories, while a user might prefer the model to focus on colors, textures or specific elements in the scene. In this paper, we propose the GeneCIS ('genesis') benchmark, which measures models' ability to adapt to a range of similarity conditions. Extending prior work, our benchmark is designed for zero-shot evaluation only, and hence considers an open-set of similarity conditions. We find that baselines from powerful CLIP models struggle on GeneCIS and that performance on the benchmark is only weakly correlated with ImageNet accuracy, suggesting that simply scaling existing methods is not fruitful. We further propose a simple, scalable solution based on automatically mining information from existing image-caption datasets. We find our method offers a substantial boost over the baselines on GeneCIS, and further improves zero-shot performance on related image retrieval benchmarks. In fact, though evaluated zero-shot, our model surpasses state-of-the-art supervised models on MIT-States.

关键词： language reasoning vision

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LAVENDER: Unifying Video-Language Understanding as Masked Language Modeling

LAVENDER: Unifying Video-Language Understanding as Masked La...

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

作者： Li, Linjie Can, Zhe Lin, Kevin Lin, Chung-Ching Liu, Zicheng Liu, Ce Wang, Lijuan Microsoft Albuquerque NM 87108 USA

ISBN: (纸本)9798350301298

Unified vision-language frameworks have greatly advanced in recent years, most of which adopt an encoder-decoder architecture to unify image-text tasks as sequence-to-sequence generation. However, existing video-language (VidL) models still require task-specific designs in model architecture and training objectives for each task. In this work, we explore a unified VidL framework LAVENDER, where Masked Language Modeling [13] (MLM) is used as the common interface for all pre-training and downstream tasks. Such unification leads to a simplified model architecture, where only a lightweight MLM head, instead of a decoder with much more parameters, is needed on top of the multimodal encoder. Surprisingly, experimental results show that this unified framework achieves competitive performance on 14 VidL benchmarks, covering video question answering, text-to-video retrieval and video captioning. Extensive analyses further demonstrate LAVENDER can (i) seamlessly support all downstream tasks with just a single set of parameter values when multi-task fine-tuned;(ii) generalize to various downstream tasks with limited training samples;and (iii) enable zero-shot evaluation on video question answering tasks. Code is available at https://***/microsoft/LAVENDER.

关键词： and reasoning language vision

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S³C: Semi-Supervised VQA Natural Language Explanation via Self-Critical Learning

S<SUP>3</SUP>C: Semi-Supervised VQA Natural Language Explana...

引用

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

作者： Suo, Wei Sun, Mengyang Liu, Weisong Gao, Yiqi Wang, Peng Zhang, Yanning Wu, Qi Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Northwestern Polytech Univ Ningbo Inst Xian Peoples R China Northwestern Polytech Univ Sch Cybersecur Xian Peoples R China Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9798350301298

VQA Natural Language Explanation (VQA-NLE) task aims to explain the decision-making process of VQA models in natural language. Unlike traditional attention or gradient analysis, free-text rationales can be easier to understand and gain users' trust. Existing methods mostly use post-hoc or self-rationalization models to obtain a plausible explanation. However, these frameworks are bottlenecked by the following challenges: 1) the reasoning process cannot be faithfully responded to and suffer from the problem of logical inconsistency. 2) Human-annotated explanations are expensive and time-consuming to collect. In this paper, we propose a new Semi-Supervised VQA-NLE via Self-Critical Learning ((SC)-C-3), which evaluates the candidate explanations by answering rewards to improve the logical consistency between answers and rationales. With a semi-supervised learning framework, the (SC)-C-3 can benefit from a tremendous amount of samples without human-annotated explanations. A large number of automatic measures and human evaluations all show the effectiveness of our method. Meanwhile, the framework achieves a new state-of-the-art performance on the two VQA-NLE datasets.

关键词： and reasoning language vision

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PromptKD: Unsupervised Prompt Distillation for vision-Language Models

PromptKD: Unsupervised Prompt Distillation for Vision-Langua...

引用

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

作者： Li, Zheng Li, Xiang Fu, Xinyi Zhang, Xin Wang, Weiqiang Chen, Shuo Yang, Jian Nankai Univ Coll Comp Sci PCA Lab VCIP Tianjin Peoples R China NKIARI Shenzhen Futian Peoples R China Ant Grp Tiansuan Lab Hangzhou Peoples R China RIKEN Wako Saitama Japan

ISBN: (纸本)9798350353006

Prompt learning has emerged as a valuable technique in enhancing vision-language models (VLMs) such as CLIP for downstream tasks in specific domains. Existing work mainly focuses on designing various learning forms of prompts, neglecting the potential of prompts as effective distillers for learning from larger teacher models. In this paper, we introduce an unsupervised domain prompt distillation framework, which aims to transfer the knowledge of a larger teacher model to a lightweight target model through prompt-driven imitation using unlabeled domain images. Specifically, our framework consists of two distinct stages. In the initial stage, we pre-train a large CLIP teacher model using domain (few-shot) labels. After pre-training, we leverage the unique decoupled-modality characteristics of CLIP by pre-computing and storing the text features as class vectors only once through the teacher text encoder. In the subsequent stage, the stored class vectors are shared across teacher and student image encoders for calculating the predicted logits. Further, we align the logits of both the teacher and student models via KL divergence, encouraging the student image encoder to generate similar probability distributions to the teacher through the learnable prompts. The proposed prompt distillation process eliminates the reliance on labeled data, enabling the algorithm to leverage a vast amount of unlabeled images within the domain. Finally, the well-trained student image encoders and pre-stored text features (class vectors) are utilized for inference. To our best knowledge, we are the first to (1) perform unsupervised domain-specific prompt-driven knowledge distillation for CLIP, and (2) establish a practical pre-storing mechanism of text features as shared class vectors between teacher and student. Extensive experiments on 11 datasets demonstrate the effectiveness of our method. Code is publicly available at https://***/zhengli97/PromptKD.

关键词： knowledge distillation prompt learning vision-language models zero-shot learning

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Improving Robustness of vision Transformers by Reducing Sensitivity to Patch Corruptions

Improving Robustness of Vision Transformers by Reducing Sens...

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

作者： Guo, Yong Stutz, David Schiele, Bernt Saarland Informat Campus Max Planck Inst Informat Saarbrucken Germany

ISBN: (纸本)9798350301298

Despite their success, vision transformers still remain vulnerable to image corruptions, such as noise or blur. Indeed, we find that the vulnerability mainly stems from the unstable self-attention mechanism, which is inherently built upon patch-based inputs and often becomes overly sensitive to the corruptions across patches. For example, when we only occlude a small number of patches with random noise (e.g., 10%), these patch corruptions would lead to severe accuracy drops and greatly distract intermediate attention layers. To address this, we propose a new training method that improves the robustness of transformers from a new perspective - reducing sensitivity to patch corruptions (RSPC). Specifically, we first identify and occlude/corrupt the most vulnerable patches and then explicitly reduce sensitivity to them by aligning the intermediate features between clean and corrupted examples. We highlight that the construction of patch corruptions is learned adversarially to the following feature alignment process, which is particularly effective and essentially different from existing methods. In experiments, our RSPC greatly improves the stability of attention layers and consistently yields better robustness on various benchmarks, including CIFAR-10/100-C, ImageNet-A, ImageNet-C, and ImageNet-P.

关键词： Deep learning architectures and techniques

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Fresnel Microfacet BRDF: Unification of Polari-Radiometric Surface-Body Reflection

Fresnel Microfacet BRDF: Unification of Polari-Radiometric S...

引用

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

作者： Ichikawa, Tomoki Fukao, Yoshiki Nobuhara, Shohei Nishino, Ko Kyoto Univ Grad Sch Informat Kyoto Japan

ISBN: (纸本)9798350301298

computer vision applications have heavily relied on the linear combination of Lambertian diffuse and microfacet specular reflection models for representing reflected radiance, which turns out to be physically incompatible and limited in applicability. In this paper, we derive a novel analytical reflectance model, which we refer to as Fresnel Microfacet BRDF model, that is physically accurate and generalizes to various real-world surfaces. Our key idea is to model the Fresnel reflection and transmission of the surface microgeometry with a collection of oriented mirror facets, both for body and surface reflections. We carefully derive the Fresnel reflection and transmission for each microfacet as well as the light transport between them in the subsurface. This physically-grounded modeling also allows us to express the polarimetric behavior of reflected light in addition to its radiometric behavior. That is, FMBRDF unifies not only body and surface reflections but also light reflection in radiometry and polarization and represents them in a single model. Experimental results demonstrate its effectiveness in accuracy, expressive power, image-based estimation, and geometry recovery.

关键词： Physics-based vision and shape-from-X

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