GenAI4ELab/papercli-papers huggingface dataset:
Neural Implicit Morphing of Face Images
Authors: Guilherme Schardong; Tiago Novello; Hallison Paz; Iurii Medvedev; Vinícius da Silva; Luiz Velho; Nuno Gonçalves
Venue: CVPR 2024 (main)

Abstract: Face morphing is a problem in computer graphics with numerous artistic and forensic applications. It is challenging due to variations in pose lighting gender and ethnicity. This task consists of a warping for feature alignment and a blending for a seamless transition between the warped images. We propose to leverage coord-based neural networks to represent such warpings and blendings of face images. During training we exploit the smoothness and flexibility of such networks by combining energy functionals employed in classical approaches without discretizations. Additionally our method is time-dependent allowing a continuous warping/blending of the images. During morphing inference we need both direct and inverse transformations of the time-dependent warping. The first (second) is responsible for warping the target (source) image into the source (target) image. Our neural warping stores those maps in a single network dismissing the need for inverting them. The results of our experiments indicate that our method is competitive with both classical and generative models under the lens of image quality and face-morphing detectors. Aesthetically the resulting images present a seamless blending of diverse faces not yet usual in the literature.
UniGS: Unified Representation for Image Generation and Segmentation
Authors: Lu Qi; Lehan Yang; Weidong Guo; Yu Xu; Bo Du; Varun Jampani; Ming-Hsuan Yang
Venue: CVPR 2024 (main)

Abstract: This paper introduces a novel unified representation of diffusion models for image generation and segmentation. Specifically we use a colormap to represent entity-level masks addressing the challenge of varying entity numbers while aligning the representation closely with the image RGB domain. Two novel modules including the location-aware color palette and progressive dichotomy module are proposed to support our mask representation. On the one hand a location-aware palette guarantees the colors' consistency to entities' locations. On the other hand the progressive dichotomy module can efficiently decode the synthesized colormap to high-quality entity-level masks in a depth-first binary search without knowing the cluster numbers. To tackle the issue of lacking large-scale segmentation training data we employ an inpainting pipeline and then improve the flexibility of diffusion models across various tasks including inpainting image synthesis referring segmentation and entity segmentation. Comprehensive experiments validate the efficiency of our approach demonstrating comparable segmentation mask quality to state-of-the-art and adaptability to multiple tasks.
Robust Synthetic-to-Real Transfer for Stereo Matching
Authors: Jiawei Zhang; Jiahe Li; Lei Huang; Xiaohan Yu; Lin Gu; Jin Zheng; Xiao Bai
Venue: CVPR 2024 (main)

Abstract: With advancements in domain generalized stereo matching networks models pre-trained on synthetic data demonstrate strong robustness to unseen domains. However few studies have investigated the robustness after fine-tuning them in real-world scenarios during which the domain generalization ability can be seriously degraded. In this paper we explore fine-tuning stereo matching networks without compromising their robustness to unseen domains. Our motivation stems from comparing Ground Truth (GT) versus Pseudo Label (PL) for fine-tuning: GT degrades but PL preserves the domain generalization ability. Empirically we find the difference between GT and PL implies valuable information that can regularize networks during fine-tuning. We also propose a framework to utilize this difference for fine-tuning consisting of a frozen Teacher an exponential moving average (EMA) Teacher and a Student network. The core idea is to utilize the EMA Teacher to measure what the Student has learned and dynamically improve GT and PL for fine-tuning. We integrate our framework with state-of-the-art networks and evaluate its effectiveness on several real-world datasets. Extensive experiments show that our method effectively preserves the domain generalization ability during fine-tuning.
Instance-Aware Group Quantization for Vision Transformers
Authors: Jaehyeon Moon; Dohyung Kim; Junyong Cheon; Bumsub Ham
Venue: CVPR 2024 (main)

Abstract: Post-training quantization (PTQ) is an efficient model compression technique that quantizes a pretrained full-precision model using only a small calibration set of unlabeled samples without retraining. PTQ methods for convolutional neural networks (CNNs) provide quantization results comparable to full-precision counterparts. Directly applying them to vision transformers (ViTs) however incurs severe performance degradation mainly due to the differences in architectures between CNNs and ViTs. In particular the distribution of activations for each channel vary drastically according to input instances making PTQ methods for CNNs inappropriate for ViTs. To address this we introduce instance-aware group quantization for ViTs (IGQ-ViT). To this end we propose to split the channels of activation maps into multiple groups dynamically for each input instance such that activations within each group share similar statistical properties. We also extend our scheme to quantize softmax attentions across tokens. In addition the number of groups for each layer is adjusted to minimize the discrepancies between predictions from quantized and full-precision models under a bit-operation (BOP) constraint. We show extensive experimental results on image classification object detection and instance segmentation with various transformer architectures demonstrating the effectiveness of our approach.
A General and Efficient Training for Transformer via Token Expansion
Authors: Wenxuan Huang; Yunhang Shen; Jiao Xie; Baochang Zhang; Gaoqi He; Ke Li; Xing Sun; Shaohui Lin
Venue: CVPR 2024 (main)

Abstract: The remarkable performance of Vision Transformers (ViTs) typically requires an extremely large training cost. Existing methods have attempted to accelerate the training of ViTs yet typically disregard method universality with accuracy dropping. Meanwhile they break the training consistency of the original transformers including the consistency of hyper-parameters architecture and strategy which prevents them from being widely applied to different Transformer networks. In this paper we propose a novel token growth scheme Token Expansion (termed ToE) to achieve consistent training acceleration for ViTs. We introduce an "initialization-expansion-merging" pipeline to maintain the integrity of the intermediate feature distribution of original transformers preventing the loss of crucial learnable information in the training process. ToE can not only be seamlessly integrated into the training and fine-tuning process of transformers (e.g. DeiT and LV-ViT) but also effective for efficient training frameworks (e.g. EfficientTrain) without twisting the original training hyper-parameters architecture and introducing additional training strategies. Extensive experiments demonstrate that ToE achieves about 1.3x faster for the training of ViTs in a lossless manner or even with performance gains over the full-token training baselines. Code is available at https://github.com/Osilly/TokenExpansion.
GenZI: Zero-Shot 3D Human-Scene Interaction Generation
Authors: Lei Li; Angela Dai
Venue: CVPR 2024 (main)

Abstract: Can we synthesize 3D humans interacting with scenes without learning from any 3D human-scene interaction data? We propose GenZI the first zero-shot approach to generating 3D human-scene interactions. Key to GenZI is our distillation of interaction priors from large vision-language models (VLMs) which have learned a rich semantic space of 2D human-scene compositions. Given a natural language description and a coarse point location of the desired interaction in a 3D scene we first leverage VLMs to imagine plausible 2D human interactions inpainted into multiple rendered views of the scene. We then formulate a robust iterative optimization to synthesize the pose and shape of a 3D human model in the scene guided by consistency with the 2D interaction hypotheses. In contrast to existing learning-based approaches GenZI circumvents the conventional need for captured 3D interaction data and allows for flexible control of the 3D interaction synthesis with easy-to-use text prompts. Extensive experiments show that our zero-shot approach has high flexibility and generality making it applicable to diverse scene types including both indoor and outdoor environments.
Tyche: Stochastic In-Context Learning for Medical Image Segmentation
Authors: Marianne Rakic; Hallee E. Wong; Jose Javier Gonzalez Ortiz; Beth A. Cimini; John V. Guttag; Adrian V. Dalca
Venue: CVPR 2024 (main)

Abstract: Existing learning-based solutions to medical image segmentation have two important shortcomings. First for most new segmentation tasks a new model has to be trained or fine-tuned. This requires extensive resources and machine-learning expertise and is therefore often infeasible for medical researchers and clinicians. Second most existing segmentation methods produce a single deterministic segmentation mask for a given image. In practice however there is often considerable uncertainty about what constitutes the correct segmentation and different expert annotators will often segment the same image differently. We tackle both of these problems with Tyche a framework that uses a context set to generate stochastic predictions for previously unseen tasks without the need to retrain. Tyche differs from other in-context segmentation methods in two important ways. (1) We introduce a novel convolution block architecture that enables interactions among predictions. (2) We introduce in-context test-time augmentation a new mechanism to provide prediction stochasticity. When combined with appropriate model design and loss functions Tyche can predict a set of plausible diverse segmentation candidates for new or unseen medical images and segmentation tasks without the need to retrain. Code available at: https://tyche.csail.mit.edu/.
DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassembly
Authors: Gianluca Scarpellini; Stefano Fiorini; Francesco Giuliari; Pietro Moreiro; Alessio Del Bue
Venue: CVPR 2024 (main)

Abstract: Reassembly tasks play a fundamental role in many fields and multiple approaches exist to solve specific reassembly problems. In this context we posit that a general unified model can effectively address them all irrespective of the input data type (image 3D etc.). We introduce DiffAssemble a Graph Neural Network (GNN)-based architecture that learns to solve reassembly tasks using a diffusion model formulation. Our method treats the elements of a set whether pieces of 2D patch or 3D object fragments as nodes of a spatial graph. Training is performed by introducing noise into the position and rotation of the elements and iteratively denoising them to reconstruct the coherent initial pose. DiffAssemble achieves state-of-the-art (SOTA) results in most 2D and 3D reassembly tasks and is the first learning-based approach that solves 2D puzzles for both rotation and translation. Furthermore we highlight its remarkable reduction in run-time performing 11 times faster than the quickest optimization-based method for puzzle solving.
NeISF: Neural Incident Stokes Field for Geometry and Material Estimation
Authors: Chenhao Li; Taishi Ono; Takeshi Uemori; Hajime Mihara; Alexander Gatto; Hajime Nagahara; Yusuke Moriuchi
Venue: CVPR 2024 (main)

Abstract: Multi-view inverse rendering is the problem of estimating the scene parameters such as shapes materials or illuminations from a sequence of images captured under different viewpoints. Many approaches however assume single light bounce and thus fail to recover challenging scenarios like inter-reflections. On the other hand simply extending those methods to consider multi-bounced light requires more assumptions to alleviate the ambiguity. To address this problem we propose Neural Incident Stokes Fields (NeISF) a multi-view inverse rendering framework that reduces ambiguities using polarization cues. The primary motivation for using polarization cues is that it is the accumulation of multi-bounced light providing rich information about geometry and material. Based on this knowledge the proposed incident Stokes field efficiently models the accumulated polarization effect with the aid of an original physically-based differentiable polarimetric renderer. Lastly experimental results show that our method outperforms the existing works in synthetic and real scenarios.
Training-Free Open-Vocabulary Segmentation with Offline Diffusion-Augmented Prototype Generation
Authors: Luca Barsellotti; Roberto Amoroso; Marcella Cornia; Lorenzo Baraldi; Rita Cucchiara
Venue: CVPR 2024 (main)

Abstract: Open-vocabulary semantic segmentation aims at segmenting arbitrary categories expressed in textual form. Previous works have trained over large amounts of image-caption pairs to enforce pixel-level multimodal alignments. However captions provide global information about the semantics of a given image but lack direct localization of individual concepts. Further training on large-scale datasets inevitably brings significant computational costs. In this paper we propose FreeDA a training-free diffusion-augmented method for open-vocabulary semantic segmentation which leverages the ability of diffusion models to visually localize generated concepts and local-global similarities to match class-agnostic regions with semantic classes. Our approach involves an offline stage in which textual-visual reference embeddings are collected starting from a large set of captions and leveraging visual and semantic contexts. At test time these are queried to support the visual matching process which is carried out by jointly considering class-agnostic regions and global semantic similarities. Extensive analyses demonstrate that FreeDA achieves state-of-the-art performance on five datasets surpassing previous methods by more than 7.0 average points in terms of mIoU and without requiring any training. Our source code is available at https://aimagelab.github.io/freeda/.
YOLO-World: Real-Time Open-Vocabulary Object Detection
Authors: Tianheng Cheng; Lin Song; Yixiao Ge; Wenyu Liu; Xinggang Wang; Ying Shan
Venue: CVPR 2024 (main)

Abstract: The You Only Look Once (YOLO) series of detectors have established themselves as efficient and practical tools. However their reliance on predefined and trained object categories limits their applicability in open scenarios. Addressing this limitation we introduce YOLO-World an innovative approach that enhances YOLO with open-vocabulary detection capabilities through vision-language modeling and pre-training on large-scale datasets. Specifically we propose a new Re-parameterizable Vision-Language Path Aggregation Network (RepVL-PAN) and region-text contrastive loss to facilitate the interaction between visual and linguistic information. Our method excels in detecting a wide range of objects in a zero-shot manner with high efficiency. On the challenging LVIS dataset YOLO-World achieves 35.4 AP with 52.0 FPS on V100 which outperforms many state-of-the-art methods in terms of both accuracy and speed. Furthermore the fine-tuned YOLO-World achieves remarkable performance on several downstream tasks including object detection and open-vocabulary instance segmentation. Code and models are available at https://github.com/AILab-CVC/YOLO-World
ViT-Lens: Towards Omni-modal Representations
Authors: Weixian Lei; Yixiao Ge; Kun Yi; Jianfeng Zhang; Difei Gao; Dylan Sun; Yuying Ge; Ying Shan; Mike Zheng Shou
Venue: CVPR 2024 (main)

Abstract: Aiming to advance AI agents large foundation models significantly improve reasoning and instruction execution yet the current focus on vision and language neglects the potential of perceiving diverse modalities in open-world environments. However the success of data-driven vision and language models is costly or even infeasible to be reproduced for rare modalities. In this paper we present ViT-Lens that facilitates efficient omni-modal representation learning by perceiving novel modalities with a pretrained ViT and aligning them to a pre-defined space. Specifically the modality-specific lens is tuned to project any-modal signals to an intermediate embedding space which are then processed by a strong ViT with pre-trained visual knowledge. The encoded representations are optimized toward aligning with the modal-independent space pre-defined by off-the-shelf foundation models. ViT-Lens provides a unified solution for representation learning of increasing modalities with two appealing advantages: (i) Unlocking the great potential of pretrained ViTs to novel modalities effectively with efficient data regime; (ii) Enabling emergent downstream capabilities through modality alignment and shared ViT parameters. We tailor ViT-Lens to learn representations for 3D point cloud depth audio tactile and EEG and set new state-of-the-art results across various understanding tasks such as zero-shot classification. By seamlessly integrating ViT-Lens into Multimodal Foundation Models we enable Any-modality to Text and Image Generation in a zero-shot manner. Code and models are available at https://github.com/TencentARC/ViT-Lens.
Cross-Dimension Affinity Distillation for 3D EM Neuron Segmentation
Authors: Xiaoyu Liu; Miaomiao Cai; Yinda Chen; Yueyi Zhang; Te Shi; Ruobing Zhang; Xuejin Chen; Zhiwei Xiong
Venue: CVPR 2024 (main)

Abstract: Accurate 3D neuron segmentation from electron microscopy (EM) volumes is crucial for neuroscience research. However the complex neuron morphology often leads to over-merge and over-segmentation results. Recent advancements utilize 3D CNNs to predict a 3D affinity map with improved accuracy but suffer from two challenges: high computational cost and limited input size especially for practical deployment for large-scale EM volumes. To address these challenges we propose a novel method to leverage lightweight 2D CNNs for efficient neuron segmentation. Our method employs a 2D Y-shape network to generate two embedding maps from adjacent 2D sections which are then converted into an affinity map by measuring their embedding distance. While the 2D network better captures pixel dependencies inside sections with larger input sizes it overlooks inter-section dependencies. To overcome this we introduce a cross-dimension affinity distillation (CAD) strategy that transfers inter-section dependency knowledge from a 3D teacher network to the 2D student network by ensuring consistency between their output affinity maps. Additionally we design a feature grafting interaction (FGI) module to enhance knowledge transfer by grafting embedding maps from the 2D student onto those from the 3D teacher. Extensive experiments on multiple EM neuron segmentation datasets including a newly built one by ourselves demonstrate that our method achieves superior performance over state-of-the-art methods with only 1/20 inference latency.
HUGS: Human Gaussian Splats
Authors: Muhammed Kocabas; Jen-Hao Rick Chang; James Gabriel; Oncel Tuzel; Anurag Ranjan
Venue: CVPR 2024 (main)

Abstract: Recent advances in neural rendering have improved both training and rendering times by orders of magnitude. While these methods demonstrate state-of-the-art quality and speed they are designed for photogrammetry of static scenes and do not generalize well to freely moving humans in the environment. In this work we introduce Human Gaussian Splats (HUGS) that represents an animatable human together with the scene using 3D Gaussian Splatting (3DGS). Our method takes only a monocular video with a small number of (50-100) frames and it automatically learns to disentangle the static scene and a fully animatable human avatar within 30 minutes. We utilize the SMPL body model to initialize the human Gaussians. To capture details that are not modeled by SMPL (e.g cloth hairs) we allow the 3D Gaussians to deviate from the human body model. Utilizing 3D Gaussians for animated humans brings new challenges including the artifacts created when articulating the Gaussians. We propose to jointly optimize the linear blend skinning weights to coordinate the movements of individual Gaussians during animation. Our approach enables novel-pose synthesis of human and novel view synthesis of both the human and the scene. We achieve state-of-the-art rendering quality with a rendering speed of 60 FPS while being  100x faster to train over previous work.
GeoChat: Grounded Large Vision-Language Model for Remote Sensing
Authors: Kartik Kuckreja; Muhammad Sohail Danish; Muzammal Naseer; Abhijit Das; Salman Khan; Fahad Shahbaz Khan
Venue: CVPR 2024 (main)

Abstract: Recent advancements in Large Vision-Language Models (VLMs) have shown great promise in natural image domains allowing users to hold a dialogue about given visual content. However such general-domain VLMs perform poorly for Remote Sensing (RS) scenarios leading to inaccurate or fabricated information when presented with RS domain-specific queries. Such a behavior emerges due to the unique challenges introduced by RS imagery. For example to handle high-resolution RS imagery with diverse scale changes across categories and many small objects region-level reasoning is necessary alongside holistic scene interpretation. Furthermore the lack of domain-specific multimodal instruction following data as well as strong backbone models for RS make it hard for the models to align their behavior with user queries. To address these limitations we propose GeoChat - the first versatile remote sensing VLM that offers multitask conversational capabilities with high-resolution RS images. Specifically GeoChat can not only answer image-level queries but also accepts region inputs to hold region-specific dialogue. Furthermore it can visually ground objects in its responses by referring to their spatial coordinates. To address the lack of domain-specific datasets we generate a novel RS multimodal instruction-following dataset by extending image-text pairs from existing diverse RS datasets. Leveraging this rich dataset we fine-tune our remote sensing VLM based on the LLaVA-1.5 architecture. We establish a comprehensive benchmark for RS multitask conversations and compare with a number of baseline methods. GeoChat demonstrates robust zero-shot performance on various remote sensing tasks e.g. image and region captioning visual question answering scene classification visually grounded conversations and referring object detection. Our codes will be open-sourced.
PhysPT: Physics-aware Pretrained Transformer for Estimating Human Dynamics from Monocular Videos
Authors: Yufei Zhang; Jeffrey O. Kephart; Zijun Cui; Qiang Ji
Venue: CVPR 2024 (main)

Abstract: While current methods have shown promising progress on estimating 3D human motion from monocular videos their motion estimates are often physically unrealistic because they mainly consider kinematics. In this paper we introduce Physics-aware Pretrained Transformer (PhysPT) which improves kinematics-based motion estimates and infers motion forces. PhysPT exploits a Transformer encoder-decoder backbone to effectively learn human dynamics in a self-supervised manner. Moreover it incorporates physics principles governing human motion. Specifically we build a physics-based body representation and contact force model. We leverage them to impose novel physics-inspired training losses (i.e. force loss contact loss and Euler-Lagrange loss) enabling PhysPT to capture physical properties of the human body and the forces it experiences. Experiments demonstrate that once trained PhysPT can be directly applied to kinematics-based estimates to significantly enhance their physical plausibility and generate favourable motion forces. Furthermore we show that these physically meaningful quantities translate into improved accuracy of an important downstream task: human action recognition.