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Block Selective Reprogramming for On-device Training of vision Transformers

Block Selective Reprogramming for On-device Training of Visi...

引用

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

作者： Sarkar, Sreetama Kundu, Souvik Zheng, Kai Beerel, Peter A. Univ Southern Calif Los Angeles CA 90007 USA Intel Labs San Diego CA USA

ISBN: (纸本)9798350365474

The ubiquity of vision transformers (ViTs) for various edge applications, including personalized learning, has created the demand for on-device fine-tuning. However, training with the limited memory and computation power of edge devices remains a significant challenge. In particular, the memory required for training is much higher than that needed for inference, primarily due to the need to store activations across all layers in order to compute the gradients needed for weight updates. Previous works have explored reducing this memory requirement via frozen-weight training as well storing the activations in a compressed format. However, these methods are deemed inefficient due to their inability to provide training or inference speedup. In this paper, we first investigate the limitations of existing on-device training methods aimed at reducing memory and compute requirements. We then present block selective reprogramming (BSR) in which we fine-tune only a fraction of total blocks of a pre-trained model and selectively drop tokens based on self-attention scores of the frozen layers. To show the efficacy of BSR, we present extensive evaluations on ViT-B and DeiT-S with five different datasets. Compared to the existing alternatives, our approach simultaneously reduces training memory by up to 1.4x and compute cost by up to 2x while maintaining similar accuracy. We also showcase results for Mixture-of-Expert (MoE) models, demonstrating the effectiveness of our approach in multitask learning scenarios. Code will be available at: https://***/sreetamasarkar/BSR.

关键词： on-device training token pruning vision transformer

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Generalized Deep 3D Shape Prior via Part-Discretized Diffusion Process

Generalized Deep 3D Shape Prior via Part-Discretized Diffusi...

引用

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

作者： Li, Yuhan Dou, Yishun Chen, Xuanhong Ni, Bingbing Sun, Yilin Liu, Yutian Wang, Fuzhen Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Huawei Shenzhen Peoples R China

ISBN: (纸本)9798350301298

We develop a generalized 3D shape generation prior model, tailored for multiple 3D tasks including unconditional shape generation, point cloud completion, and cross-modality shape generation, etc. On one hand, to precisely capture local fine detailed shape information, a vector quantized variational autoencoder (VQ-VAE) is utilized to index local geometry from a compactly learned code-book based on a broad set of task training data. On the other hand, a discrete diffusion generator is introduced to model the inherent structural dependencies among different tokens. In the meantime, a multi-frequency fusion module (MFM) is developed to suppress high-frequency shape feature fluctuations, guided by multi-frequency contextual information. The above designs jointly equip our proposed 3D shape prior model with high-fidelity, diverse features as well as the capability of cross-modality alignment, and extensive experiments have demonstrated superior performances on various 3D shape generation tasks.

关键词： vision + graphics

来源：评论

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Weakly-supervised Single-view Image Relighting

Weakly-supervised Single-view Image Relighting

引用

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

作者： Yi, Renjiao Zhu, Chenyang Xu, Kai Natl Univ Def Technol Changsha Hunan Peoples R China

ISBN: (纸本)9798350301298

We present a learning-based approach to relight a single image of Lambertian and low-frequency specular objects. Our method enables inserting objects from photographs into new scenes and relighting them under the new environment lighting, which is essential for AR applications. To relight the object, we solve both inverse rendering and re-rendering. To resolve the ill-posed inverse rendering, we propose a weakly-supervised method by a low-rank constraint. To facilitate the weakly-supervised training, we contribute Relit, a large-scale (750K images) dataset of videos with aligned objects under changing illuminations. For re-rendering, we propose a differentiable specular rendering layer to render low-frequency non-Lambertian materials under various illuminations of spherical harmonics. The whole pipeline is end-to-end and efficient, allowing for a mobile app implementation of AR object insertion. Extensive evaluations demonstrate that our method achieves state-of-the-art performance. Project page: https://***/relighting/.

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

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DEGPR: Deep Guided Posterior Regularization for Multi-Class Cell Detection and Counting

DEGPR: Deep Guided Posterior Regularization for Multi-Class ...

引用

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

作者： Tyagi, Aayush Kumar Mohapatra, Chirag Das, Prasenjit Makharia, Govind Mehra, Lalita Prathosh, A. P. Mausam IIT Delhi New Delhi India IISc Bangalore Karnataka India AIIMS New Delhi India

ISBN: (纸本)9798350301298

Multi-class cell detection and counting is an essential task for many pathological diagnoses. Manual counting is tedious and often leads to inter-observer variations among pathologists. While there exist multiple, general-purpose, deep learning-based object detection and counting methods, they may not readily transfer to detecting and counting cells in medical images, due to the limited data, presence of tiny overlapping objects, multiple cell types, severe class-imbalance, minute differences in size/shape of cells, etc. In response, we propose guided posterior regularization (DEGPR), which assists an object detector by guiding it to exploit discriminative features among cells. The features may be pathologist-provided or inferred directly from visual data. We validate our model on two publicly available datasets (CoNSeP and MoNuSAC), and on MuCeD, a novel dataset that we contribute. MuCeD consists of 55 biopsy images of the human duodenum for predicting celiac disease. We perform extensive experimentation with three object detection baselines on three datasets to show that DEGPR is model-agnostic, and consistently improves baselines obtaining up to 9% (absolute) mAP gains.

关键词： cell microscopy Medical and biological vision

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A Practical Upper Bound for the Worst-Case Attribution Deviations

A Practical Upper Bound for the Worst-Case Attribution Devia...

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

作者： Wang, Fan Kong, Adams Wai-Kin Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Nanyang Technol Univ Rapid Rich Object Search ROSE Lab IGP Singapore Singapore

ISBN: (纸本)9798350301298

Model attribution is a critical component of deep neural networks (DNNs) for its interpretability to complex models. Recent studies bring up attention to the security of attribution methods as they are vulnerable to attribution attacks that generate similar images with dramatically different attributions. Existing works have been investigating empirically improving the robustness of DNNs against those attacks;however, none of them explicitly quantifies the actual deviations of attributions. In this work, for the first time, a constrained optimization problem is formulated to derive an upper bound that measures the largest dissimilarity of attributions after the samples are perturbed by any noises within a certain region while the classification results remain the same. Based on the formulation, different practical approaches are introduced to bound the attributions above using Euclidean distance and cosine similarity under both l(2) and l(infinity)-norm perturbations constraints. The bounds developed by our theoretical study are validated on various datasets and two different types of attacks (PGD attack and IFIA attribution attack). Over 10 million attacks in the experiments indicate that the proposed upper bounds effectively quantify the robustness of models based on the worst-case attribution dissimilarities.

关键词： Explainable computer vision

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Slimmable Dataset Condensation

Slimmable Dataset Condensation

引用

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

作者： Liu, Songhua Ye, Jingwen Yu, Runpeng Wang, Xinchao Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9798350301298

Dataset distillation, also known as dataset condensation, aims to compress a large dataset into a compact synthetic one. Existing methods perform dataset condensation by assuming a fixed storage or transmission budget. When the budget changes, however, they have to repeat the synthesizing process with access to original datasets, which is highly cumbersome if not infeasible at all. In this paper, we explore the problem of slimmable dataset condensation, to extract a smaller synthetic dataset given only previous condensation results. We first study the limitations of existing dataset condensation algorithms on such a successive compression setting and identify two key factors: (1) the inconsistency of neural networks over different compression times and (2) the underdetermined solution space for synthetic data. Accordingly, we propose a novel training objective for slimmable dataset condensation to explicitly account for both factors. Moreover, synthetic datasets in our method adopt a significance-aware parameterization. Theoretical derivation indicates that an upper-bounded error can be achieved by discarding the minor components without training. Alternatively, if training is allowed, this strategy can serve as a strong initialization that enables a fast convergence. Extensive comparisons and ablations demonstrate the superiority of the proposed solution over existing methods on multiple benchmarks.

关键词： Efficient and scalable vision

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

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

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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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Language-Guided Audio-Visual Source Separation via Trimodal Consistency

Language-Guided Audio-Visual Source Separation via Trimodal ...

引用

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

作者： Tan, Reuben Ray, Arijit Burns, Andrea Plummer, Bryan A. Salamon, Justin Nieto, Oriol Russell, Bryan Saenko, Kate Boston Univ Boston MA 02215 USA Adobe Res San Francisco CA USA IBM Res MIT IBM Watson AI Lab Cambridge MA USA

ISBN: (纸本)9798350301298

We propose a self-supervised approach for learning to perform audio source separation in videos based on natural language queries, using only unlabeled video and audio pairs as training data. A key challenge in this task is learning to associate the linguistic description of a sound-emitting object to its visual features and the corresponding components of the audio waveform, all without access to annotations during training. To overcome this challenge, we adapt off-the-shelf vision-language foundation models to provide pseudo-target supervision via two novel loss functions and encourage a stronger alignment between the audio, visual and natural language modalities. During inference, our approach can separate sounds given text, video and audio input, or given text and audio input alone. We demonstrate the effectiveness of our self-supervised approach on three audio-visual separation datasets, including MUSIC, SOLOS and AudioSet, where we outperform state-of-the-art strongly supervised approaches despite not using object detectors or text labels during training. Our project page including publicly available code can be found at https://***/rxtan/projects/VAST.

关键词： and reasoning language vision

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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...

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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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Discovering the Real Association: Multimodal Causal Reasoning in Video Question Answering

Discovering the Real Association: Multimodal Causal Reasonin...

引用

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

作者： Zang, Chuanqi Wang, Hanqing Pei, Mingtao Liang, Wei Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Beijing Inst Technol Yangtze Delta Reg Acad Jiaxing Peoples R China

ISBN: (纸本)9798350301298

Video Question Answering (VideoQA) is challenging as it requires capturing accurate correlations between modalities from redundant information. Recent methods focus on the explicit challenges of the task, e.g. multimodal feature extraction, video-text alignment and fusion. Their frameworks reason the answer relying on statistical evidence causes, which ignores potential bias in the multimodal data. In our work, we investigate relational structure from a causal representation perspective on multimodal data and propose a novel inference framework. For visual data, question-irrelevant objects may establish simple matching associations with the answer. For textual data, the model prefers the local phrase semantics which may deviate from the global semantics in long sentences. Therefore, to enhance the generalization of the model, we discover the real association by explicitly capturing visual features that are causally related to the question semantics and weakening the impact of local language semantics on question answering. The experimental results on two large causal VideoQA datasets verify that our proposed framework 1) improves the accuracy of the existing VideoQA backbone, 2) demonstrates robustness on complex scenes and questions. The code will be released at https://***/Chuanqi-Zang/Discovering-the-Real-Association.

关键词： language reasoning vision

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