pwc-archive/papers-with-abstracts huggingface dataset:
Neural Texture Puppeteer: A Framework for Neural Geometry and Texture Rendering of Articulated Shapes, Enabling Re-Identification at Interactive Speed
Authors: Urs Waldmann, Ole Johannsen, Bastian Goldluecke
Date: 2023-11-28 00:00:00
Tasks: Neural Rendering

In this paper, we present a neural rendering pipeline for textured articulated shapes that we call Neural Texture Puppeteer. Our method separates geometry and texture encoding. The geometry pipeline learns to capture spatial relationships on the surface of the articulated shape from ground truth data that provides this geometric information. A texture auto-encoder makes use of this information to encode textured images into a global latent code. This global texture embedding can be efficiently trained separately from the geometry, and used in a downstream task to identify individuals. The neural texture rendering and the identification of individuals run at interactive speeds. To the best of our knowledge, we are the first to offer a promising alternative to CNN- or transformer-based approaches for re-identification of articulated individuals based on neural rendering. Realistic looking novel view and pose synthesis for different synthetic cow textures further demonstrate the quality of our method. Restricted by the availability of ground truth data for the articulated shape's geometry, the quality for real-world data synthesis is reduced. We further demonstrate the flexibility of our model for real-world data by applying a synthetic to real-world texture domain shift where we reconstruct the texture from a real-world 2D RGB image. Thus, our method can be applied to endangered species where data is limited. Our novel synthetic texture dataset NePuMoo is publicly available to inspire further development in the field of neural rendering-based re-identification.
On the Calibration of Human Pose Estimation
Authors: Kerui Gu, Rongyu Chen, Angela Yao
Date: 2023-11-28 00:00:00
Tasks: 2D Human Pose Estimation, Human Mesh Recovery, Pose Estimation

Most 2D human pose estimation frameworks estimate keypoint confidence in an ad-hoc manner, using heuristics such as the maximum value of heatmaps. The confidence is part of the evaluation scheme, e.g., AP for the MSCOCO dataset, yet has been largely overlooked in the development of state-of-the-art methods. This paper takes the first steps in addressing miscalibration in pose estimation. From a calibration point of view, the confidence should be aligned with the pose accuracy. In practice, existing methods are poorly calibrated. We show, through theoretical analysis, why a miscalibration gap exists and how to narrow the gap. Simply predicting the instance size and adjusting the confidence function gives considerable AP improvements. Given the black-box nature of deep neural networks, however, it is not possible to fully close this gap with only closed-form adjustments. As such, we go one step further and learn network-specific adjustments by enforcing consistency between confidence and pose accuracy. Our proposed Calibrated ConfidenceNet (CCNet) is a light-weight post-hoc addition that improves AP by up to 1.4% on off-the-shelf pose estimation frameworks. Applied to the downstream task of mesh recovery, CCNet facilitates an additional 1.0mm decrease in 3D keypoint error.
A High-Quality Robust Diffusion Framework for Corrupted Dataset
Authors: Quan Dao, Binh Ta, Tung Pham, Anh Tran
Date: 2023-11-28 00:00:00
Tasks: Image Generation

Developing image-generative models, which are robust to outliers in the training process, has recently drawn attention from the research community. Due to the ease of integrating unbalanced optimal transport (UOT) into adversarial framework, existing works focus mainly on developing robust frameworks for generative adversarial model (GAN). Meanwhile, diffusion models have recently dominated GAN in various tasks and datasets. However, according to our knowledge, none of them are robust to corrupted datasets. Motivated by DDGAN, our work introduces the first robust-to-outlier diffusion. We suggest replacing the UOT-based generative model for GAN in DDGAN to learn the backward diffusion process. Additionally, we demonstrate that the Lipschitz property of divergence in our framework contributes to more stable training convergence. Remarkably, our method not only exhibits robustness to corrupted datasets but also achieves superior performance on clean datasets.
Robust Transductive Few-shot Learning via Joint Message Passing and Prototype-based Soft-label Propagation
Authors: Jiahui Wang, Qin Xu, Bo Jiang, Bin Luo
Date: 2023-11-28 00:00:00
Tasks: Few-Shot Learning

Few-shot learning (FSL) aims to develop a learning model with the ability to generalize to new classes using a few support samples. For transductive FSL tasks, prototype learning and label propagation methods are commonly employed. Prototype methods generally first learn the representative prototypes from the support set and then determine the labels of queries based on the metric between query samples and prototypes. Label propagation methods try to propagate the labels of support samples on the constructed graph encoding the relationships between both support and query samples. This paper aims to integrate these two principles together and develop an efficient and robust transductive FSL approach, termed Prototype-based Soft-label Propagation (PSLP). Specifically, we first estimate the soft-label presentation for each query sample by leveraging prototypes. Then, we conduct soft-label propagation on our learned query-support graph. Both steps are conducted progressively to boost their respective performance. Moreover, to learn effective prototypes for soft-label estimation as well as the desirable query-support graph for soft-label propagation, we design a new joint message passing scheme to learn sample presentation and relational graph jointly. Our PSLP method is parameter-free and can be implemented very efficiently. On four popular datasets, our method achieves competitive results on both balanced and imbalanced settings compared to the state-of-the-art methods. The code will be released upon acceptance.
In Search of a Data Transformation That Accelerates Neural Field Training
Authors: Junwon Seo, Sangyoon Lee, Kwang In Kim, Jaeho Lee
Date: 2023-11-28 00:00:00
Proceeding: CVPR 2024 1

Neural field is an emerging paradigm in data representation that trains a neural network to approximate the given signal. A key obstacle that prevents its widespread adoption is the encoding speed-generating neural fields requires an overfitting of a neural network, which can take a significant number of SGD steps to reach the desired fidelity level. In this paper, we delve into the impacts of data transformations on the speed of neural field training, specifically focusing on how permuting pixel locations affect the convergence speed of SGD. Counterintuitively, we find that randomly permuting the pixel locations can considerably accelerate the training. To explain this phenomenon, we examine the neural field training through the lens of PSNR curves, loss landscapes, and error patterns. Our analyses suggest that the random pixel permutations remove the easy-to-fit patterns, which facilitate easy optimization in the early stage but hinder capturing fine details of the signal.
PAWS-VMK: A Unified Approach To Semi-Supervised Learning And Out-of-Distribution Detection
Authors: Evelyn Mannix, Howard Bondell
Date: 2023-11-28 00:00:00
Tasks: Benchmarking, image-classification, Image Classification, Out-of-Distribution Detection, Out of Distribution (OOD) Detection, Prototype Selection

This paper describes PAWS-VMK, a prototypical deep learning approach that obtains state-of-the-art results for image classification tasks in both a semi-supervised learning (SSL) and out-of-distribution (OOD) detection context. We consider developments in the fields of SSL, OOD detection, and computer vision foundation models to introduce a number of innovations that connect the key ideas within these works to create PAWS-VMK. These innovations include (1) parametric von Mises-Fisher Stochastic Neighbour Embedding (vMF-SNE) to initialise a projection head for SSL using the high-quality embeddings of the foundation model; (2) the PAWS-MixMatch loss, that creates more compact embeddings and obtains higher accuracy in comparison to the consistency loss used in PAWS and (3) simple $k$-Means prototype selection (SKMPS), a simple technique that obtains competitive performance with more complex unsupervised label selection approaches. PAWS-VMK sets new benchmarks in semi-supervised learning for CIFAR-10 (99.2%) and CIFAR-100 (89.8%) with four labelled instances per class, and Food-101 (90.1%) with two labelled instances per class. We also observe that PAWS-VMK can efficiently detect OOD samples in a manner that is competitive with specialised methods specifically designed for this purpose, achieving 93.1/98.0 and 95.2/96.3 on the CIFAR-10 and CIFAR-100 OpenOOD benchmarks.
SEED-Bench-2: Benchmarking Multimodal Large Language Models
Authors: Bohao Li, Yuying Ge, Yixiao Ge, Guangzhi Wang, Rui Wang, Ruimao Zhang, Ying Shan
Date: 2023-11-28 00:00:00
Tasks: Benchmarking, Image Generation, Multiple-choice

Multimodal large language models (MLLMs), building upon the foundation of powerful large language models (LLMs), have recently demonstrated exceptional capabilities in generating not only texts but also images given interleaved multimodal inputs (acting like a combination of GPT-4V and DALL-E 3). However, existing MLLM benchmarks remain limited to assessing only models' comprehension ability of single image-text inputs, failing to keep up with the strides made in MLLMs. A comprehensive benchmark is imperative for investigating the progress and uncovering the limitations of current MLLMs. In this work, we categorize the capabilities of MLLMs into hierarchical levels from $L_0$ to $L_4$ based on the modalities they can accept and generate, and propose SEED-Bench-2, a comprehensive benchmark that evaluates the \textbf{hierarchical} capabilities of MLLMs. Specifically, SEED-Bench-2 comprises 24K multiple-choice questions with accurate human annotations, which spans 27 dimensions, including the evaluation of both text and image generation. Multiple-choice questions with groundtruth options derived from human annotation enables an objective and efficient assessment of model performance, eliminating the need for human or GPT intervention during evaluation. We further evaluate the performance of 23 prominent open-source MLLMs and summarize valuable observations. By revealing the limitations of existing MLLMs through extensive evaluations, we aim for SEED-Bench-2 to provide insights that will motivate future research towards the goal of General Artificial Intelligence. Dataset and evaluation code are available at \href{https://github.com/AILab-CVC/SEED-Bench}
Beyond Sole Strength: Customized Ensembles for Generalized Vision-Language Models
Authors: Zhihe Lu, Jiawang Bai, Xin Li, Zeyu Xiao, Xinchao Wang
Date: 2023-11-28 00:00:00
Tasks: Prompt Engineering

Fine-tuning pre-trained vision-language models (VLMs), e.g., CLIP, for the open-world generalization has gained increasing popularity due to its practical value. However, performance advancements are limited when relying solely on intricate algorithmic designs for a single model, even one exhibiting strong performance, e.g., CLIP-ViT-B/16. This paper, for the first time, explores the collaborative potential of leveraging much weaker VLMs to enhance the generalization of a robust single model. The affirmative findings motivate us to address the generalization problem from a novel perspective, i.e., ensemble of pre-trained VLMs. We introduce three customized ensemble strategies, each tailored to one specific scenario. Firstly, we introduce the zero-shot ensemble, automatically adjusting the logits of different models based on their confidence when only pre-trained VLMs are available. Furthermore, for scenarios with extra few-shot samples, we propose the training-free and tuning ensemble, offering flexibility based on the availability of computing resources. The proposed ensemble strategies are evaluated on zero-shot, base-to-new, and cross-dataset generalization, achieving new state-of-the-art performance. Notably, this work represents an initial stride toward enhancing the generalization performance of VLMs via ensemble. The code is available at https://github.com/zhiheLu/Ensemble_VLM.git.
Multi-Scale 3D Gaussian Splatting for Anti-Aliased Rendering
Authors: Zhiwen Yan, Weng Fei Low, Yu Chen, Gim Hee Lee
Date: 2023-11-28 00:00:00
Proceeding: CVPR 2024 1
Tasks: 3D Reconstruction

3D Gaussians have recently emerged as a highly efficient representation for 3D reconstruction and rendering. Despite its high rendering quality and speed at high resolutions, they both deteriorate drastically when rendered at lower resolutions or from far away camera position. During low resolution or far away rendering, the pixel size of the image can fall below the Nyquist frequency compared to the screen size of each splatted 3D Gaussian and leads to aliasing effect. The rendering is also drastically slowed down by the sequential alpha blending of more splatted Gaussians per pixel. To address these issues, we propose a multi-scale 3D Gaussian splatting algorithm, which maintains Gaussians at different scales to represent the same scene. Higher-resolution images are rendered with more small Gaussians, and lower-resolution images are rendered with fewer larger Gaussians. With similar training time, our algorithm can achieve 13\%-66\% PSNR and 160\%-2400\% rendering speed improvement at 4$\times$-128$\times$ scale rendering on Mip-NeRF360 dataset compared to the single scale 3D Gaussian splitting. Our code and more results are available on our project website https://jokeryan.github.io/projects/ms-gs/
Unsupervised Multimodal Deepfake Detection Using Intra- and Cross-Modal Inconsistencies
Authors: Mulin Tian, Mahyar Khayatkhoei, Joe Mathai, Wael AbdAlmageed
Date: 2023-11-28 00:00:00
Tasks: DeepFake Detection, Face Swapping

Deepfake videos present an increasing threat to society with potentially negative impact on criminal justice, democracy, and personal safety and privacy. Meanwhile, detecting deepfakes, at scale, remains a very challenging task that often requires labeled training data from existing deepfake generation methods. Further, even the most accurate supervised deepfake detection methods do not generalize to deepfakes generated using new generation methods. In this paper, we propose a novel unsupervised method for detecting deepfake videos by directly identifying intra-modal and cross-modal inconsistency between video segments. The fundamental hypothesis behind the proposed detection method is that motion or identity inconsistencies are inevitable in deepfake videos. We will mathematically and empirically support this hypothesis, and then proceed to constructing our method grounded in our theoretical analysis. Our proposed method outperforms prior state-of-the-art unsupervised deepfake detection methods on the challenging FakeAVCeleb dataset, and also has several additional advantages: it is scalable because it does not require pristine (real) samples for each identity during inference and therefore can apply to arbitrarily many identities, generalizable because it is trained only on real videos and therefore does not rely on a particular deepfake method, reliable because it does not rely on any likelihood estimation in high dimensions, and explainable because it can pinpoint the exact location of modality inconsistencies which are then verifiable by a human expert.
Rethinking Mixup for Improving the Adversarial Transferability
Authors: Xiaosen Wang, Zeyuan Yin
Date: 2023-11-28 00:00:00

Mixup augmentation has been widely integrated to generate adversarial examples with superior adversarial transferability when immigrating from a surrogate model to other models. However, the underlying mechanism influencing the mixup's effect on transferability remains unexplored. In this work, we posit that the adversarial examples located at the convergence of decision boundaries across various categories exhibit better transferability and identify that Admix tends to steer the adversarial examples towards such regions. However, we find the constraint on the added image in Admix decays its capability, resulting in limited transferability. To address such an issue, we propose a new input transformation-based attack called Mixing the Image but Separating the gradienT (MIST). Specifically, MIST randomly mixes the input image with a randomly shifted image and separates the gradient of each loss item for each mixed image. To counteract the imprecise gradient, MIST calculates the gradient on several mixed images for each input sample. Extensive experimental results on the ImageNet dataset demonstrate that MIST outperforms existing SOTA input transformation-based attacks with a clear margin on both Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) w/wo defense mechanisms, supporting MIST's high effectiveness and generality.
Beyond Visual Cues: Synchronously Exploring Target-Centric Semantics for Vision-Language Tracking
Authors: Jiawei Ge, Xiangmei Chen, Jiuxin Cao, Xuelin Zhu, Bo Liu
Date: 2023-11-28 00:00:00
Tasks: Object Tracking, Representation Learning

Single object tracking aims to locate one specific target in video sequences, given its initial state. Classical trackers rely solely on visual cues, restricting their ability to handle challenges such as appearance variations, ambiguity, and distractions. Hence, Vision-Language (VL) tracking has emerged as a promising approach, incorporating language descriptions to directly provide high-level semantics and enhance tracking performance. However, current VL trackers have not fully exploited the power of VL learning, as they suffer from limitations such as heavily relying on off-the-shelf backbones for feature extraction, ineffective VL fusion designs, and the absence of VL-related loss functions. Consequently, we present a novel tracker that progressively explores target-centric semantics for VL tracking. Specifically, we propose the first Synchronous Learning Backbone (SLB) for VL tracking, which consists of two novel modules: the Target Enhance Module (TEM) and the Semantic Aware Module (SAM). These modules enable the tracker to perceive target-related semantics and comprehend the context of both visual and textual modalities at the same pace, facilitating VL feature extraction and fusion at different semantic levels. Moreover, we devise the dense matching loss to further strengthen multi-modal representation learning. Extensive experiments on VL tracking datasets demonstrate the superiority and effectiveness of our methods.
DepthSSC: Monocular 3D Semantic Scene Completion via Depth-Spatial Alignment and Voxel Adaptation
Authors: Jiawei Yao, Jusheng Zhang, Xiaochao Pan, Tong Wu, Canran Xiao
Date: 2023-11-28 00:00:00
Tasks: 3D Semantic Scene Completion, Autonomous Driving, Object

The task of 3D semantic scene completion using monocular cameras is gaining significant attention in the field of autonomous driving. This task aims to predict the occupancy status and semantic labels of each voxel in a 3D scene from partial image inputs. Despite numerous existing methods, many face challenges such as inaccurately predicting object shapes and misclassifying object boundaries. To address these issues, we propose DepthSSC, an advanced method for semantic scene completion using only monocular cameras. DepthSSC integrates the Spatial Transformation Graph Fusion (ST-GF) module with Geometric-Aware Voxelization (GAV), enabling dynamic adjustment of voxel resolution to accommodate the geometric complexity of 3D space. This ensures precise alignment between spatial and depth information, effectively mitigating issues such as object boundary distortion and incorrect depth perception found in previous methods. Evaluations on the SemanticKITTI and SSCBench-KITTI-360 dataset demonstrate that DepthSSC not only captures intricate 3D structural details effectively but also achieves state-of-the-art performance.
CLiC: Concept Learning in Context
Authors: Mehdi Safaee, Aryan Mikaeili, Or Patashnik, Daniel Cohen-Or, Ali Mahdavi-Amiri
Date: 2023-11-28 00:00:00
Proceeding: CVPR 2024 1
Tasks: Object

This paper addresses the challenge of learning a local visual pattern of an object from one image, and generating images depicting objects with that pattern. Learning a localized concept and placing it on an object in a target image is a nontrivial task, as the objects may have different orientations and shapes. Our approach builds upon recent advancements in visual concept learning. It involves acquiring a visual concept (e.g., an ornament) from a source image and subsequently applying it to an object (e.g., a chair) in a target image. Our key idea is to perform in-context concept learning, acquiring the local visual concept within the broader context of the objects they belong to. To localize the concept learning, we employ soft masks that contain both the concept within the mask and the surrounding image area. We demonstrate our approach through object generation within an image, showcasing plausible embedding of in-context learned concepts. We also introduce methods for directing acquired concepts to specific locations within target images, employing cross-attention mechanisms, and establishing correspondences between source and target objects. The effectiveness of our method is demonstrated through quantitative and qualitative experiments, along with comparisons against baseline techniques.
DreamPropeller: Supercharge Text-to-3D Generation with Parallel Sampling
Authors: Linqi Zhou, Andy Shih, Chenlin Meng, Stefano Ermon
Date: 2023-11-28 00:00:00
Proceeding: CVPR 2024 1
Tasks: 3D Generation, Text to 3D

Recent methods such as Score Distillation Sampling (SDS) and Variational Score Distillation (VSD) using 2D diffusion models for text-to-3D generation have demonstrated impressive generation quality. However, the long generation time of such algorithms significantly degrades the user experience. To tackle this problem, we propose DreamPropeller, a drop-in acceleration algorithm that can be wrapped around any existing text-to-3D generation pipeline based on score distillation. Our framework generalizes Picard iterations, a classical algorithm for parallel sampling an ODE path, and can account for non-ODE paths such as momentum-based gradient updates and changes in dimensions during the optimization process as in many cases of 3D generation. We show that our algorithm trades parallel compute for wallclock time and empirically achieves up to 4.7x speedup with a negligible drop in generation quality for all tested frameworks.