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Conjugate Adder Net (CAddNet) - a Space-Efficient Approximate CNN

Conjugate Adder Net (CAddNet) - a Space-Efficient Approximat...

引用

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

作者： Shen, Lulan Ziaeefard, Maryam Meyer, Brett Gross, Warren Clark, James J. McGill Univ Montreal PQ Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The AdderNet was recently developed as a way to implement deep neural networks without needing multiplication operations to combine weights and inputs. Instead, absolute values of the difference between weights and inputs are used, greatly reducing the gate-level implementation complexity. Training of AdderNets is challenging, however, and the loss curves during training tend to fluctuate significantly. In this paper we propose the Conjugate Adder Network, or CAddNet, which uses the difference between the absolute values of conjugate pairs of inputs and the weights. We show that this can be implemented simply via a single minimum operation, resulting in a roughly 50% reduction in logic gate complexity as compared with AdderNets. The CAddNet method also stabilizes training as compared with AdderNets, yielding training curves similar to standard CNNs.

关键词： Training Deep learning conferences Neural networks Logic gates Complexity theory pattern recognition

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HMIway-env: A Framework for Simulating Behaviors and Preferences to Support Human-AI Teaming in Driving

HMIway-env: A Framework for Simulating Behaviors and Prefere...

引用

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

作者： Gopinath, Deepak DeCastro, Jonathan Rosman, Guy Sumner, Emily Morgan, Allison Hakimi, Shabnam Stent, Simon Toyota Res Inst Los Altos CA 94022 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We introduce a lightweight simulation and modeling framework, HMIway-env, for studying human-machine teaming in the context of driving. The goal of the framework is to accelerate the development of adaptive AI systems which can respond to individual driver states, traits, and preferences, by serving as a data-generation engine and training environment for learning personalized human-AI teaming policies. We extend highway-env, an OpenAI Gym-based simulator environment, to enable specification of human driver behavior, and design of vehicle-driver interactions and outcomes. We describe one instance of our framework incorporating models for distracted and cautious driving, which we validate through crowd-sourced feedback, and show early experimental results toward the training of better intervention policies.

关键词： Training Adaptation models Fatigue Behavioral sciences pattern recognition Artificial intelligence Task analysis

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Language Models as Black-Box Optimizers for vision-Language Models

Language Models as Black-Box Optimizers for Vision-Language ...

引用

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

作者： Liu, Shihong Yu, Samuel Lin, Zhiqiu Pathak, Deepak Ramanan, Deva Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798350353006

vision-language models (VLMs) pre-trained on web-scale datasets have demonstrated remarkable capabilities on downstream tasks when fine-tuned with minimal data. However, many VLMs rely on proprietary data and are not open-source, which restricts the use of white-box approaches for fine-tuning. As such, we aim to develop a black-box approach to optimize VLMs through natural language prompts, thereby avoiding the need to access model parameters, feature embeddings, or even output logits. We propose employing chat-based LLMs to search for the best text prompt for VLMs. Specifically, we adopt an automatic "hill-climbing" procedure that converges to an effective prompt by evaluating the performance of current prompts and asking LLMs to refine them based on textual feedback, all within a conversational process without human-in-the-loop. In a challenging 1-shot image classification setup, our simple approach surpasses the white-box continuous prompting method (CoOp) by an average of 1.5% across 11 datasets including ImageNet. Our approach also outperforms both human-engineered and LLM-generated prompts. We highlight the advantage of conversational feedback that incorporates both positive and negative prompts, suggesting that LLMs can utilize the implicit "gradient" direction in textual feedback for a more efficient search. In addition, we find that the text prompts generated through our strategy are not only more interpretable but also transfer well across different VLM architectures in a black-box manner. Lastly, we demonstrate our framework on a state-of-the-art black-box VLM (DALL-E 3) for text-to-image optimization.

关键词： generative models large language models prompting text-to-image generation vision-language models

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Retrieval-Augmented Open-Vocabulary Object Detection

Retrieval-Augmented Open-Vocabulary Object Detection

引用

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

作者： Kim, Jooyeon Cho, Eulrang Kim, Sehyung Kim, Hyunwoo J. Korea Univ Dept Comp Sci & Engn Seoul South Korea Samsung Res Mountain View CA USA

ISBN: (纸本)9798350353006

Open-vocabulary object detection (OVD) has been studied with vision-Language Models (VLMs) to detect novel objects beyond the pre-trained categories. Previous approaches improve the generalization ability to expand the knowledge of the detector, using 'positive' pseudo-labels with additional 'class' names, e.g., sock, iPod, and alligator. To extend the previous methods in two aspects, we propose Retrieval-Augmented Losses and visual Features ( RALF). Our method retrieves related 'negative' classes and augments loss functions. Also, visual features are augmented with 'verbalized concepts' of classes, e.g., worn on the feet, handheld music player, and sharp teeth. Specifically, RALF consists of two modules: Retrieval Aug-mented Losses ( RAL) and Retrieval-Augmented visual Features (RAF). RAL constitutes two losses reflecting the semantic similarity with negative vocabularies. In addition, RAF augments visual features with the verbalized concepts from a large language model (LLM). Our experiments demonstrate the effectiveness of RALF on COCO and LVIS benchmark datasets. We achieve improvement up to 3.4 box APN on novel categories of the COCO dataset and 3.6 mask APr gains on the LVIS dataset. Code is available at https://***/mlvlab/RALF.

关键词： computer vision Object Detection Open-Vocabulary Object Detection Retrieval-Augmentation

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EgoThink: Evaluating First-Person Perspective Thinking Capability of vision-Language Models

EgoThink: Evaluating First-Person Perspective Thinking Capab...

引用

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

作者： Cheng, Sijie Guo, Zhicheng Wu, Jingwen Fang, Kechen Li, Peng Liu, Huaping Liu, Yang Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Tsinghua Univ Inst AI Ind Res AIR Beijing Peoples R China Univ Toronto Dept Elect & Comp Engn Toronto ON Canada Tsinghua Univ Zhili Coll Beijing Peoples R China 01 Ai Beijing Peoples R China

ISBN: (纸本)9798350353006

vision-language models (VLMs) have recently shown promising results in traditional downstream tasks. Evaluation studies have emerged to assess their abilities, with the majority focusing on the third-person perspective, and only a few addressing specific tasks from the first-person perspective. However, the capability of VLMs to "think" from a first-person perspective, a crucial attribute for advancing autonomous agents and robotics, remains largely unexplored. To bridge this research gap, we introduce EgoThink, a novel visual question-answering benchmark that encompasses six core capabilities with twelve detailed dimensions. The benchmark is constructed using selected clips from ego-centric videos, with manually annotated question-answer pairs containing first-person information. To comprehensively assess VLMs, we evaluate twenty-one popular VLMs on EgoThink. Moreover, given the open-ended format of the answers, we use GPT-4 as the automatic judge to compute single-answer grading. Experimental results indicate that although GPT-4V leads in numerous dimensions, all evaluated VLMs still possess considerable potential for improvement in first-person perspective tasks. Meanwhile, enlarging the number of trainable parameters has the most significant impact on model performance on EgoThink. In conclusion, EgoThink serves as a valuable addition to existing evaluation benchmarks for VLMs, providing an indispensable resource for future research in the realm of embodied artificial intelligence and robotics.

关键词： Benchmark Egocentric vision-Language Models

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The Topology and Language of Relationships in the Visual Genome Dataset

The Topology and Language of Relationships in the Visual Gen...

引用

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

作者： Abou Chacra, David Zelek, John Univ Waterloo Waterloo ON Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The Visual Genome Dataset is the de facto standard dataset used in Scene Graph generation. It contains a large collection of images with corresponding object and relationship labels. We explore the lingual aspect of the relationship predicates and find that very few symmetric/inverse relationships are represented in the dataset(for example, 'above' and 'under'). We believe this is linked to human spatial cognition, and posit that labelling bias stemming from human representations of relationships creates asymmetric relationship labels that span the whole dataset. We also perform a 2D topological analysis of the bounding boxes linked by different relationship predicates. This analysis sheds light on certain classes and their ambiguity wherein more frequent classes are semantically overloaded and therefore quite confusing. Finally we show that when reduced to more lingually and topologically well defined spatial relationships scene graph generation algorithm performance improves tremendously, but scene graph generators are still far from perfect.

关键词： Visualization conferences Genomics Generators Topology pattern recognition Labeling

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Differentiable Shadow Mapping for Efficient Inverse Graphics

Differentiable Shadow Mapping for Efficient Inverse Graphics

引用

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

作者： Worchel, Markus Alexa, Marc TU Berlin Berlin Germany

ISBN: (纸本)9798350301298

We show how shadows can be efficiently generated in differentiable rendering of triangle meshes. Our central observation is that pre-filtered shadow mapping, a technique for approximating shadows based on rendering from the perspective of a light, can be combined with existing differentiable rasterizers to yield differentiable visibility information. We demonstrate at several inverse graphics problems that differentiable shadow maps are orders of magnitude faster than differentiable light transport simulation with similar accuracy - while differentiable rasterization without shadows often fails to converge.

关键词： vision + graphics

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DAtRNet: Disentangling Fashion Attribute Embedding for Substitute Item Retrieval

DAtRNet: Disentangling Fashion Attribute Embedding for Subst...

引用

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

作者： Bhattacharya, Gaurab Kilari, Nikhil Gubbi, Jayavardhana Lakshmi, Bagya, V Pal, Arpan Balamuralidhar, P. TCS Res Mumbai Maharashtra India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Interactive substitute recommendation for fashion products improves the online retail customer experience. Traditional fashion search platforms incorporate product metadata between the query products and the products to be retrieved. In this paper, we propose DAtRNet, an attribute representation network to disentangle the features in the query product. It is used to recommend attribute-aware substitute items based on the conditional similarity of the retrieved products. The proposed architecture relies on attribute-level similarity providing a fine-grained recommendation. In addition, a concurrent axial attention mechanism is proposed that generates global information embedding and adaptively re-calibrates the soft attention masks. Overall, the end-to-end framework enables the system to disentangle the attribute features and independently deals with them to enhance its flexibility towards one or multiple attributes. The proposed method outperforms the state-ofthe-art by a significant margin.

关键词： computer vision conferences computer architecture Metadata Search problems Feature extraction pattern recognition

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Revisiting the Receptive Field of Conv-GRU in DROID-SLAM

Revisiting the Receptive Field of Conv-GRU in DROID-SLAM

引用

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

作者： Bangunharcana, Antyanta Kim, Soohyun Kim, Kyung-Soo Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This work focuses on improving the Conv-GRU-based optical flow update within a DROID-SLAM framework. Prior optical flow models typically follow a UNet or coarse-to-fine architecture in order to extract long-range cross-correlation and context cues. This helps flow estimation in the presence of large motion and challenging image regions, e.g., textureless regions. We propose modifications to the Conv-GRU module which follows the rationale of these prior models by integrating (Atrous) Spatial Pyramid Pooling and global self-attention into the Conv-GRU block. By enlarging the receptive field through the aforementioned modifications, the model is able to integrate information from a larger context window, thus improving the robustness even when given inputs that comprise challenging image regions. We show empirically through extensive experiments the gain in accuracy through these modifications.

关键词： computer vision Simultaneous localization and mapping conferences Computational modeling Estimation computer architecture Robustness

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Self-Supervised Variable Rate Image Compression using Visual Attention

Self-Supervised Variable Rate Image Compression using Visual...

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

作者： Sinha, Abhishek Kumar Moorthi, S. Manthira Dhar, Debajyoti Space Applicat Ctr Signal & Image Proc Grp Ahmadabad Gujarat India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The recent success of self-supervised learning relies on its ability to learn the representations from self-defined pseudo-labels that are applied to several downstream tasks. Motivated by this ability, we present a deep image compression technique, which learns the lossy reconstruction of raw images from the self-supervised learned representation of SimCLR ResNet-50 architecture. Our framework uses a feature pyramid to achieve the variable rate compression of the image using a self-attention map for the optimal allocation of bits. The paper provides an overview to observe the effects of contrastive self-supervised representations and the self-attention map on the distortion and perceptual quality of the reconstructed image. The experiments are performed on a different class of images to show that the proposed method outperforms the other variable rate deep compression models without compromising the perceptual quality of the images.

关键词： Visualization Image coding Image analysis conferences Self-supervised learning Distortion pattern recognition

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