mtybilly/Instruct2DS huggingface dataset:
Unified Language-driven Zero-shot Domain Adaptation
Authors: Zhuotao Tian, Li Jiang, Jiaya Jia Senqiao Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This paper introduces Unified Language-driven Zero-shot Domain Adaptation (ULDA) a novel task setting that enables a single model to adapt to diverse target domains without explicit domain-ID knowledge. We identify the constraints in the existing language-driven zero-shot domain adaptation task particularly the requirement for domain IDs and domain-specific models which may restrict flexibility and scalability. To overcome these issues we propose a new framework for ULDA consisting of Hierarchical Context Alignment (HCA) Domain Consistent Representation Learning (DCRL) and Text-Driven Rectifier (TDR). These components work synergistically to align simulated features with target text across multiple visual levels retain semantic correlations between different regional representations and rectify biases between simulated and real target visual features respectively. Our extensive empirical evaluations demonstrate that this framework achieves competitive performance in both settings surpassing even the model that requires domain-ID showcasing its superiority and generalization ability. The proposed method is not only effective but also maintains practicality and efficiency as it does not introduce additional computational costs during inference. The code is available on the project website.
Category-Level Multi-Part Multi-Joint 3D Shape Assembly
Authors: Kaichun Mo, Yueqi Duan, He Wang, Jiequan Zhang, Lin Shao Yichen Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Shape assembly composes complex shapes geometries by arranging simple part geometries and has wide applications in autonomous robotic assembly and CAD modeling. Existing works focus on geometry reasoning and neglect the actual physical assembly process of matching and fitting joints which are the contact surfaces connecting different parts. In this paper we consider contacting joints for the task of multi-part assembly. A successful joint-optimized assembly needs to satisfy the bilateral objectives of shape structure and joint alignment. We propose a hierarchical graph learning approach composed of two levels of graph representation learning. The part graph takes part geometries as input to build the desired shape structure. The joint-level graph uses part joints information and focuses on matching and aligning joints. The two kinds of information are combined to achieve the bilateral objectives. Extensive experiments demonstrate that our method outperforms previous methods achieving better shape structure and higher joint alignment accuracy.
Equivariant Multi-Modality Image Fusion
Authors: Haowen Bai, Jiangshe Zhang, Yulun Zhang, Kai Zhang, Shuang Xu, Dongdong Chen, Radu Timofte, Luc Van Gool Zixiang Zhao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Multi-modality image fusion is a technique that combines information from different sensors or modalities enabling the fused image to retain complementary features from each modality such as functional highlights and texture details. However effective training of such fusion models is challenging due to the scarcity of ground truth fusion data. To tackle this issue we propose the Equivariant Multi-Modality imAge fusion (EMMA) paradigm for end-to-end self-supervised learning. Our approach is rooted in the prior knowledge that natural imaging responses are equivariant to certain transformations. Consequently we introduce a novel training paradigm that encompasses a fusion module a pseudo-sensing module and an equivariant fusion module. These components enable the net training to follow the principles of the natural sensing-imaging process while satisfying the equivariant imaging prior. Extensive experiments confirm that EMMA yields high-quality fusion results for infrared-visible and medical images concurrently facilitating downstream multi-modal segmentation and detection tasks. The code is available at https://github.com/Zhaozixiang1228/MMIF-EMMA.
NeLF-Pro: Neural Light Field Probes for Multi-Scale Novel View Synthesis
Authors: Andreas Geiger, Anpei Chen Zinuo You
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present NeLF-Pro a novel representation to model and reconstruct light fields in diverse natural scenes that vary in extent and spatial granularity. In contrast to previous fast reconstruction methods that represent the 3D scene globally we model the light field of a scene as a set of local light field feature probes parameterized with position and multi-channel 2D feature maps. Our central idea is to bake the scene's light field into spatially varying learnable representations and to query point features by weighted blending of probes close to the camera - allowing for mipmap representation and rendering. We introduce a novel vector-matrix-matrix (VMM) factorization technique that effectively represents the light field feature probes as products of core factors (i.e. VM) shared among local feature probes and a basis factor (i.e. M) - efficiently encoding internal relationships and patterns within the scene.Experimentally we demonstrate that NeLF-Pro significantly boosts the performance of feature grid-based representations and achieves fast reconstruction with better rendering quality while maintaining compact modeling. Project page: sinoyou.github.io/nelf-pro
One-Shot Open Affordance Learning with Foundation Models
Authors: Deqing Sun, Laura Sevilla-Lara, Varun Jampani Gen Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We introduce One-shot Open Affordance Learning (OOAL) where a model is trained with just one example per base object category but is expected to identify novel objects and affordances. While vision-language models excel at recognizing novel objects and scenes they often struggle to understand finer levels of granularity such as affordances. To handle this issue we conduct a comprehensive analysis of existing foundation models to explore their inherent understanding of affordances and assess the potential for data-limited affordance learning. We then propose a vision-language framework with simple and effective designs that boost the alignment between visual features and affordance text embeddings. Experiments on two affordance segmentation benchmarks show that the proposed method outperforms state-of-the-art models with less than 1% of the full training data and exhibits reasonable generalization capability on unseen objects and affordances. Project page: https://reagan1311.github.io/ooal.
Don't Look into the Dark: Latent Codes for Pluralistic Image Inpainting
Authors: Yajie Zhao Haiwei Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present a method for large-mask pluralistic image inpainting based on the generative framework of discrete latent codes. Our method learns latent priors discretized as tokens by only performing computations at the visible locations of the image. This is realized by a restrictive partial encoder that predicts the token label for each visible block a bidirectional transformer that infers the missing labels by only looking at these tokens and a dedicated synthesis network that couples the tokens with the partial image priors to generate coherent and pluralistic complete image even under extreme mask settings. Experiments on public benchmarks validate our design choices as the proposed method outperforms strong baselines in both visual quality and diversity metrics.
Incremental Nuclei Segmentation from Histopathological Images via Future-class Awareness and Compatibility-inspired Distillation
Authors: Huisi Wu, Jing Qin Huyong Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present a novel semantic segmentation approach for incremental nuclei segmentation from histopathological images which is a very challenging task as we have to incrementally optimize existing models to make them perform well in both old and new classes without using training samples of old classes. Yet it is an indispensable component of computer-aided diagnosis systems. The proposed approach has two key techniques. First we propose a new future-class awareness mechanism by separating some potential regions for future classes from background based on their similarities to both old and new classes in the representation space. With this mechanism we can not only reserve more parameter space for future updates but also enhance the representation capability of learned features. We further propose an innovative compatibility-inspired distillation scheme to make our model take full advantage of the knowledge learned by the old model. We conducted extensive experiments on two famous histopathological datasets and the results demonstrate the proposed approach achieves much better performance than state-of-the-art approaches. The code is available at https://github.com/why19991/InSeg.
DiffEditor: Boosting Accuracy and Flexibility on Diffusion-based Image Editing
Authors: Xintao Wang, Jiechong Song, Ying Shan, Jian Zhang Chong Mou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Large-scale Text-to-Image (T2I) diffusion models have revolutionized image generation over the last few years. Although owning diverse and high-quality generation capabilities translating these abilities to fine-grained image editing remains challenging. In this paper we propose DiffEditor to rectify two weaknesses in existing diffusion-based image editing: (1) in complex scenarios editing results often lack editing accuracy and exhibit unexpected artifacts; (2) lack of flexibility to harmonize editing operations e.g. imagine new content. In our solution we introduce image prompts in fine-grained image editing cooperating with the text prompt to better describe the editing content. To increase the flexibility while maintaining content consistency we locally combine stochastic differential equation (SDE) into the ordinary differential equation (ODE) sampling. In addition we incorporate regional score-based gradient guidance and a time travel strategy into the diffusion sampling further improving the editing quality. Extensive experiments demonstrate that our method can efficiently achieve state-of-the-art performance on various fine-grained image editing tasks including editing within a single image (e.g. object moving resizing and content dragging) and across images (e.g. appearance replacing and object pasting). Our source code is released at https://github.com/MC-E/DragonDiffusion.
Solving Masked Jigsaw Puzzles with Diffusion Vision Transformers
Authors: Wondmgezahu Teshome, Sandesh Ghimire, Mario Sznaier, Octavia Camps Jinyang Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Solving image and video jigsaw puzzles poses the challenging task of rearranging image fragments or video frames from unordered sequences to restore meaningful images and video sequences. Existing approaches often hinge on discriminative models tasked with predicting either the absolute positions of puzzle elements or the permutation actions applied to the original data. Unfortunately these methods face limitations in effectively solving puzzles with a large number of elements. In this paper we propose JPDVT an innovative approach that harnesses diffusion transformers to address this challenge. Specifically we generate positional information for image patches or video frames conditioned on their underlying visual content. This information is then employed to accurately assemble the puzzle pieces in their correct positions even in scenarios involving missing pieces. Our method achieves state-of-the-art performance on several datasets.
InstructVideo: Instructing Video Diffusion Models with Human Feedback
Authors: Shiwei Zhang, Xiang Wang, Yujie Wei, Tao Feng, Yining Pan, Yingya Zhang, Ziwei Liu, Samuel Albanie, Dong Ni Hangjie Yuan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Diffusion models have emerged as the de facto paradigm for video generation. However their reliance on web-scale data of varied quality often yields results that are visually unappealing and misaligned with the textual prompts. To tackle this problem we propose InstructVideo to instruct text-to-video diffusion models with human feedback by reward fine-tuning. InstructVideo has two key ingredients: 1) To ameliorate the cost of reward fine-tuning induced by generating through the full DDIM sampling chain we recast reward fine-tuning as editing. By leveraging the diffusion process to corrupt a sampled video InstructVideo requires only partial inference of the DDIM sampling chain reducing fine-tuning cost while improving fine-tuning efficiency. 2) To mitigate the absence of a dedicated video reward model for human preferences we repurpose established image reward models e.g. HPSv2. To this end we propose Segmental Video Reward a mechanism to provide reward signals based on segmental sparse sampling and Temporally Attenuated Reward a method that mitigates temporal modeling degradation during fine-tuning. Extensive experiments both qualitative and quantitative validate the practicality and efficacy of using image reward models in InstructVideo significantly enhancing the visual quality of generated videos without compromising generalization capabilities. Code and models can be accessed through our project page https://instructvideo.github.io/.
Fully Exploiting Every Real Sample: SuperPixel Sample Gradient Model Stealing
Authors: Xiaoheng Deng, Yijing Liu, Xinjun Pei, Jiazhi Xia, Wei Chen Yunlong Zhao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Model stealing (MS) involves querying and observing the output of a machine learning model to steal its capabilities. The quality of queried data is crucial yet obtaining a large amount of real data for MS is often challenging. Recent works have reduced reliance on real data by using generative models. However when high-dimensional query data is required these methods are impractical due to the high costs of querying and the risk of model collapse. In this work we propose using sample gradients (SG) to enhance the utility of each real sample as SG provides crucial guidance on the decision boundaries of the victim model. However utilizing SG in the model stealing scenario faces two challenges: 1. Pixel-level gradient estimation requires extensive query volume and is susceptible to defenses. 2. The estimation of sample gradients has a significant variance. This paper proposes Superpixel Sample Gradient stealing (SPSG) for model stealing under the constraint of limited real samples. With the basic idea of imitating the victim model's low-variance patch-level gradients instead of pixel-level gradients SPSG achieves efficient sample gradient estimation through two steps. First we perform patch-wise perturbations on query images to estimate the average gradient in different regions of the image. Then we filter the gradients through a threshold strategy to reduce variance. Exhaustive experiments demonstrate that with the same number of real samples SPSG achieves accuracy agreements and adversarial success rate significantly surpassing the current state-of-the-art MS methods. Codes are available at https://github.com/zyl123456aB/SPSG_attack.
Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRI
Authors: Lanqing Guo, Yufei Wang, Hao Cheng, Yi Yu, Bihan Wen Chong Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Deep unfolding networks (DUN) have emerged as a popular iterative framework for accelerated magnetic resonance imaging (MRI) reconstruction. However conventional DUN aims to reconstruct all the missing information within the entire space in each iteration. Thus it could be challenging when dealing with highly ill-posed degradation often resulting in subpar reconstruction. In this work we propose a Progressive Divide-And-Conquer (PDAC) strategy aiming to break down the subsampling process in the actual severe degradation and thus perform reconstruction sequentially. Starting from decomposing the original maximum-a-posteriori problem of accelerated MRI we present a rigorous derivation of the proposed PDAC framework which could be further unfolded into an end-to-end trainable network. Each PDAC iteration specifically targets a distinct segment of moderate degradation based on the decomposition. Furthermore as part of the PDAC iteration such decomposition is adaptively learned as an auxiliary task through a degradation predictor which provides an estimation of the decomposed sampling mask. Following this prediction the sampling mask is further integrated via a severity conditioning module to ensure awareness of the degradation severity at each stage. Extensive experiments demonstrate that our proposed method achieves superior performance on the publicly available fastMRI and Stanford2D FSE datasets in both multi-coil and single-coil settings.
DiffMOT: A Real-time Diffusion-based Multiple Object Tracker with Non-linear Prediction
Authors: Yuhang Huang, Ning Zhang, Ruei-Sung Lin, Mei Han, Dan Zeng Weiyi Lv
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In Multiple Object Tracking objects often exhibit non-linear motion of acceleration and deceleration with irregular direction changes. Tacking-by-detection (TBD) trackers with Kalman Filter motion prediction work well in pedestrian-dominant scenarios but fall short in complex situations when multiple objects perform non-linear and diverse motion simultaneously. To tackle the complex non-linear motion we propose a real-time diffusion-based MOT approach named DiffMOT. Specifically for the motion predictor component we propose a novel Decoupled Diffusion-based Motion Predictor (D^2MP). It models the entire distribution of various motion presented by the data as a whole. It also predicts an individual object's motion conditioning on an individual's historical motion information. Furthermore it optimizes the diffusion process with much fewer sampling steps. As a MOT tracker the DiffMOT is real-time at 22.7FPS and also outperforms the state-of-the-art on DanceTrack and SportsMOT datasets with 62.3% and 76.2% in HOTA metrics respectively. To the best of our knowledge DiffMOT is the first to introduce a diffusion probabilistic model into the MOT to tackle non-linear motion prediction.
MV-Adapter: Multimodal Video Transfer Learning for Video Text Retrieval
Authors: Bowen Zhang, Weibo Gong, Kai Xu, Xueqing Deng, Peng Wang, Zhao Zhang, Xiaohui Shen, Jiashi Feng Xiaojie Jin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: State-of-the-art video-text retrieval (VTR) methods typically involve fully fine-tuning a pre-trained model (e.g. CLIP) on specific datasets. However this can result in significant storage costs in practical applications as a separate model per task must be stored. To address this issue we present our pioneering work that enables parameter-efficient VTR using a pre-trained model with only a small number of tunable parameters during training. Towards this goal we propose a new method dubbed Multimodal Video Adapter (MV-Adapter) for efficiently transferring the knowledge in the pre-trained CLIP from image-text to video-text. Specifically MV-Adapter utilizes bottleneck structures in both video and text branches along with two novel components. The first is a Temporal Adaptation Module that is incorporated in the video branch to introduce global and local temporal contexts. We also train weights calibrations to adjust to dynamic variations across frames. The second is Cross Modality Tying that generates weights for video/text branches through sharing cross modality factors for better aligning between modalities. Thanks to above innovations MV-Adapter can achieve comparable or better performance than standard fine-tuning with negligible parameters overhead. Notably MV-Adapter consistently outperforms various competing methods in V2T/T2V tasks with large margins on five widely used VTR benchmarks (MSR-VTT MSVD LSMDC DiDemo and ActivityNet). Codes will be released.
Rethinking Multi-view Representation Learning via Distilled Disentangling
Authors: Bo Wang, Xiaoli Wang, Shengfeng He Guanzhou Ke
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Multi-view representation learning aims to derive robust representations that are both view-consistent and view-specific from diverse data sources. This paper presents an in-depth analysis of existing approaches in this domain highlighting a commonly overlooked aspect: the redundancy between view-consistent and view-specific representations. To this end we propose an innovative framework for multi-view representation learning which incorporates a technique we term 'distilled disentangling'. Our method introduces the concept of masked cross-view prediction enabling the extraction of compact high-quality view-consistent representations from various sources without incurring extra computational overhead. Additionally we develop a distilled disentangling module that efficiently filters out consistency-related information from multi-view representations resulting in purer view-specific representations. This approach significantly reduces redundancy between view-consistent and view-specific representations enhancing the overall efficiency of the learning process. Our empirical evaluations reveal that higher mask ratios substantially improve the quality of view-consistent representations. Moreover we find that reducing the dimensionality of view-consistent representations relative to that of view-specific representations further refines the quality of the combined representations.
Just Add π! Pose Induced Video Transformers for Understanding Activities of Daily Living
Authors: Srijan Das Dominick Reilly
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Video transformers have become the de facto standard for human action recognition yet their exclusive reliance on the RGB modality still limits their adoption in certain domains. One such domain is Activities of Daily Living (ADL) where RGB alone is not sufficient to distinguish between visually similar actions or actions observed from multiple viewpoints. To facilitate the adoption of video transformers for ADL we hypothesize that the augmentation of RGB with human pose information known for its sensitivity to fine-grained motion and multiple viewpoints is essential. Consequently we introduce the first Pose Induced Video Transformer: PI-ViT (or π-ViT) a novel approach that augments the RGB representations learned by video transformers with 2D and 3D pose information. The key elements of π-ViT are two plug-in modules 2D Skeleton Induction Module and 3D Skeleton Induction Module that are responsible for inducing 2D and 3D pose information into the RGB representations. These modules operate by performing pose-aware auxiliary tasks a design choice that allows π-ViT to discard the modules during inference. Notably π-ViT achieves the state-of-the-art performance on three prominent ADL datasets encompassing both real-world and large-scale RGB-D datasets without requiring poses or additional computational overhead at inference.
ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learning for Whole Slide Image Classification
Authors: Chen Li, Tieliang Gong, Yefeng Zheng, Huazhu Fu Jiangbo Shi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Multiple instance learning (MIL)-based framework has become the mainstream for processing the whole slide image (WSI) with giga-pixel size and hierarchical image context in digital pathology. However these methods heavily depend on a substantial number of bag-level labels and solely learn from the original slides which are easily affected by variations in data distribution. Recently vision language model (VLM)-based methods introduced the language prior by pre-training on large-scale pathological image-text pairs. However the previous text prompt lacks the consideration of pathological prior knowledge therefore does not substantially boost the model's performance. Moreover the collection of such pairs and the pre-training process are very time-consuming and source-intensive. To solve the above problems we propose a dual-scale vision-language multiple instance learning (ViLa-MIL) framework for whole slide image classification. Specifically we propose a dual-scale visual descriptive text prompt based on the frozen large language model (LLM) to boost the performance of VLM effectively. To transfer the VLM to process WSI efficiently for the image branch we propose a prototype-guided patch decoder to aggregate the patch features progressively by grouping similar patches into the same prototype; for the text branch we introduce a context-guided text decoder to enhance the text features by incorporating the multi-granular image contexts. Extensive studies on three multi-cancer and multi-center subtyping datasets demonstrate the superiority of ViLa-MIL.
Targeted Representation Alignment for Open-World Semi-Supervised Learning
Authors: Lei Feng, Kai Tang, Junbo Zhao, Yixuan Li, Gang Chen, Haobo Wang Ruixuan Xiao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Open-world Semi-Supervised Learning aims to classify unlabeled samples utilizing information from labeled data while unlabeled samples are not only from the labeled known categories but also from novel categories previously unseen. Despite the promise current approaches solely rely on hazardous similarity-based clustering algorithms and give unlabeled samples free rein to spontaneously group into distinct novel class clusters. Nevertheless due to the absence of novel class supervision these methods typically suffer from the representation collapse dilemma---features of different novel categories can get closely intertwined and indistinguishable even collapsing into the same cluster and leading to degraded performance. To alleviate this we propose a novel framework TRAILER which targets to attain an optimal feature arrangement revealed by the recently uncovered neural collapse phenomenon. To fulfill this we adopt targeted prototypes that are pre-assigned uniformly with maximum separation and then progressively align the representations to them. To further tackle the potential downsides of such stringent alignment we encapsulate a sample-target allocation mechanism with coarse-to-fine refinery that is able to infer label assignments with high quality. Extensive experiments demonstrate that TRAILER outperforms current state-of-the-art methods on generic and fine-grained benchmarks. The code is available at https://github.com/Justherozen/TRAILER.
Efficient Solution of Point-Line Absolute Pose
Authors: Timothy Duff, Marc Pollefeys Petr Hruby
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We revisit certain problems of pose estimation based on 3D--2D correspondences between features which may be points or lines. Specifically we address the two previously-studied minimal problems of estimating camera extrinsics from p \in \  1 2 \  point--point correspondences and l=3-p line--line correspondences. To the best of our knowledge all of the previously-known practical solutions to these problems required computing the roots of degree \ge 4 (univariate) polynomials when p=2 or degree \ge 8 polynomials when p=1. We describe and implement two elementary solutions which reduce the degrees of the needed polynomials from 4 to 2 and from 8 to 4 respectively. We show experimentally that the resulting solvers are numerically stable and fast: when compared to the previous state-of-the art we may obtain nearly an order of magnitude speedup. The code is available at https://github.com/petrhruby97/efficient_absolute
Text-to-3D using Gaussian Splatting
Authors: Feng Wang, Yikai Wang, Huaping Liu Zilong Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Automatic text-to-3D generation that combines Score Distillation Sampling (SDS) with the optimization of volume rendering has achieved remarkable progress in synthesizing realistic 3D objects. Yet most existing text-to-3D methods by SDS and volume rendering suffer from inaccurate geometry e.g. the Janus issue since it is hard to explicitly integrate 3D priors into implicit 3D representations. Besides it is usually time-consuming for them to generate elaborate 3D models with rich colors. In response this paper proposes GSGEN a novel method that adopts Gaussian Splatting a recent state-of-the-art representation to text-to-3D generation. GSGEN aims at generating high-quality 3D objects and addressing existing shortcomings by exploiting the explicit nature of Gaussian Splatting that enables the incorporation of 3D prior. Specifically our method adopts a progressive optimization strategy which includes a geometry optimization stage and an appearance refinement stage. In geometry optimization a coarse representation is established under 3D point cloud diffusion prior along with the ordinary 2D SDS optimization ensuring a sensible and 3D-consistent rough shape. Subsequently the obtained Gaussians undergo an iterative appearance refinement to enrich texture details. In this stage we increase the number of Gaussians by compactness-based densification to enhance continuity and improve fidelity. With these designs our approach can generate 3D assets with delicate details and accurate geometry. Extensive evaluations demonstrate the effectiveness of our method especially for capturing high-frequency components.
CapsFusion: Rethinking Image-Text Data at Scale
Authors: Quan Sun, Xiaosong Zhang, Yufeng Cui, Fan Zhang, Yue Cao, Xinlong Wang, Jingjing Liu Qiying Yu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Large multimodal models demonstrate remarkable generalist ability to perform diverse multimodal tasks in a zero-shot manner. Large-scale web-based image-text pairs contribute fundamentally to this success but suffer from excessive noise. Recent studies use alternative captions synthesized by captioning models and have achieved notable benchmark performance. However our experiments reveal significant Scalability Deficiency and World Knowledge Loss issues in models trained with synthetic captions which have been largely obscured by their initial benchmark success. Upon closer examination we identify the root cause as the overly-simplified language structure and lack of knowledge details in existing synthetic captions. To provide higher-quality and more scalable multimodal pretraining data we propose CapsFusion an advanced framework that leverages large language models to consolidate and refine information from both web-based image-text pairs and synthetic captions. Extensive experiments show that CapsFusion captions exhibit remarkable all-round superiority over existing captions in terms of model performance (e.g. 18.8 and 18.3 improvements in CIDEr score on COCO and NoCaps) sample efficiency (requiring 11-16 times less computation than baselines) world knowledge depth and scalability. These effectiveness efficiency and scalability advantages position CapsFusion as a promising candidate for future scaling of LMM training.
On the Content Bias in Frechet Video Distance
Authors: Aniruddha Mahapatra, Gaurav Parmar, Jun-Yan Zhu, Jia-Bin Huang Songwei Ge
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Frechet Video Distance (FVD) a prominent metric for evaluating video generation models is known to conflict with human perception occasionally. In this paper we aim to explore the extent of FVD's bias toward frame quality over temporal realism and identify its sources. We first quantify the FVD's sensitivity to the temporal axis by decoupling the frame and motion quality and find that the FVD only increases slightly with larger temporal corruption. We then analyze the generated videos and show that via careful sampling from a large set of generated videos that do not contain motions one can drastically decrease FVD without improving the temporal quality. Both studies suggest FVD's basis towards the quality of individual frames. We show that FVD with features extracted from the recent large-scale self-supervised video models is less biased toward image quality. Finally we revisit a few real-world examples to validate our hypothesis.
Tumor Micro-environment Interactions Guided Graph Learning for Survival Analysis of Human Cancers from Whole-slide Pathological Images
Authors: YangYang Shi, Daoqiang Zhang, JunJie Zhou, Peng Wan Wei Shao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The recent advance of deep learning technology brings the possibility of assisting the pathologist to predict the patients' survival from whole-slide pathological images (WSIs). However most of the prevalent methods only worked on the sampled patches in specifically or randomly selected tumor areas of WSIs which has very limited capability to capture the complex interactions between tumor and its surrounding micro-environment components. As a matter of fact tumor is supported and nurtured in the heterogeneous tumor micro-environment(TME) and the detailed analysis of TME and their correlation with tumors are important to in-depth analyze the mechanism of cancer development. In this paper we considered the spatial interactions among tumor and its two major TME components (i.e. lymphocytes and stromal fibrosis) and presented a Tumor Micro-environment Interactions Guided Graph Learning (TMEGL) algorithm for the prognosis prediction of human cancers. Specifically we firstly selected different types of patches as nodes to build graph for each WSI. Then a novel TME neighborhood organization guided graph embedding algorithm was proposed to learn node representations that can preserve their topological structure information. Finally a Gated Graph Attention Network is applied to capture the survival-associated intersections among tumor and different TME components for clinical outcome prediction. We tested TMEGL on three cancer cohorts derived from The Cancer Genome Atlas (TCGA) and the experimental results indicated that TMEGL not only outperforms the existing WSI-based survival analysis models but also has good explainable ability for survival prediction.
Towards Generalizable Multi-Object Tracking
Authors: Le Wang, Sanping Zhou, Panpan Fu, Gang Hua, Wei Tang Zheng Qin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Multi-Object Tracking (MOT) encompasses various tracking scenarios each characterized by unique traits. Effective trackers should demonstrate a high degree of generalizability across diverse scenarios. However existing trackers struggle to accommodate all aspects or necessitate hypothesis and experimentation to customize the association information (motion and/or appearance) for a given scenario leading to narrowly tailored solutions with limited generalizability. In this paper we investigate the factors that influence trackers' generalization to different scenarios and concretize them into a set of tracking scenario attributes to guide the design of more generalizable trackers. Furthermore we propose a "point-wise to instance-wise relation" framework for MOT i.e. GeneralTrack which can generalize across diverse scenarios while eliminating the need to balance motion and appearance. Thanks to its superior generalizability our proposed GeneralTrack achieves state-of-the-art performance on multiple benchmarks and demonstrates the potential for domain generalization.
POPDG: Popular 3D Dance Generation with PopDanceSet
Authors: Min Ren, Xuecai Hu, Yongzhen Huang, Li Yao Zhenye Luo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Generating dances that are both lifelike and well-aligned with music continues to be a challenging task in the cross-modal domain. This paper introduces PopDanceSet the first dataset tailored to the preferences of young audiences enabling the generation of aesthetically oriented dances. And it surpasses the AIST++ dataset in music genre diversity and the intricacy and depth of dance movements. Moreover the proposed POPDG model within the iDDPM framework enhances dance diversity and through the Space Augmentation Algorithm strengthens spatial physical connections between human body joints ensuring that increased diversity does not compromise generation quality. A streamlined Alignment Module is also designed to improve the temporal alignment between dance and music. Extensive experiments show that POPDG achieves SOTA results on two datasets. Furthermore the paper also expands on current evaluation metrics. The dataset and code are available at https://github.com/Luke-Luo1/POPDG.
Image Neural Field Diffusion Models
Authors: Oliver Wang, Richard Zhang, Eli Shechtman, Xiaolong Wang, Michael Gharbi Yinbo Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Diffusion models have shown an impressive ability to model complex data distributions with several key advantages over GANs such as stable training better coverage of the training distribution's modes and the ability to solve inverse problems without extra training. However most diffusion models learn the distribution of fixed-resolution images. We propose to learn the distribution of continuous images by training diffusion models on image neural fields which can be rendered at any resolution and show its advantages over fixed-resolution models. To achieve this a key challenge is to obtain a latent space that represents photorealistic image neural fields. We propose a simple and effective method inspired by several recent techniques but with key changes to make the image neural fields photorealistic. Our method can be used to convert existing latent diffusion autoencoders into image neural field autoencoders. We show that image neural field diffusion models can be trained using mixed-resolution image datasets outperform fixed-resolution diffusion models followed by super-resolution models and can solve inverse problems with conditions applied at different scales efficiently.
Discriminative Probing and Tuning for Text-to-Image Generation
Authors: Wenjie Wang, Yongqi Li, Hanwang Zhang, Liqiang Nie, Tat-Seng Chua Leigang Qu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Despite advancements in text-to-image generation (T2I) prior methods often face text-image misalignment problems such as relation confusion in generated images. Existing solutions involve cross-attention manipulation for better compositional understanding or integrating large language models for improved layout planning. However the inherent alignment capabilities of T2I models are still inadequate. By reviewing the link between generative and discriminative modeling we posit that T2I models' discriminative abilities may reflect their text-image alignment proficiency during generation. In this light we advocate bolstering the discriminative abilities of T2I models to achieve more precise text-to-image alignment for generation. We present a discriminative adapter built on T2I models to probe their discriminative abilities on two representative tasks and leverage discriminative fine-tuning to improve their text-image alignment. As a bonus of the discriminative adapter a self-correction mechanism can leverage discriminative gradients to better align generated images to text prompts during inference. Comprehensive evaluations across three benchmark datasets including both in-distribution and out-of-distribution scenarios demonstrate our method's superior generation performance. Meanwhile it achieves state-of-the-art discriminative performance on the two discriminative tasks compared to other generative models. The code is available at https://dpt-t2i.github.io/.
Slice3D: Multi-Slice Occlusion-Revealing Single View 3D Reconstruction
Authors: Wallace Lira, Wenqi Wang, Ali Mahdavi-Amiri, Hao Zhang Yizhi Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We introduce multi-slice reasoning a new notion for single-view 3D reconstruction which challenges the current and prevailing belief that multi-view synthesis is the most natural conduit between single-view and 3D. Our key observation is that object slicing is a more direct and hence more advantageous means to reveal occluded structures than altering camera views. Specifically slicing can peel through any occluder without obstruction and in the limit (i.e. with infinitely many slices) it is guaranteed to unveil all hidden object parts. We realize our idea by developing Slice3D a novel method for single-view 3D reconstruction which first predicts multi-slice images from a single RGB input image and then integrates the slices into a 3D model using a coordinate-based transformer network to product a signed distance function. The slice images can be regressed or generated both through a U-Net based network. For the former we inject a learnable slice indicator code to designate each decoded image into a spatial slice location while the slice generator is a denoising diffusion model operating on the entirety of slice images stacked on the input channels. We conduct extensive evaluation against state-of-the-art alternatives to demonstrate superiority of our method especially in recovering complex and severely occluded shape structures amid ambiguities. All Slice3D results were produced by networks trained on a single Nvidia A40 GPU with an inference time of less than 20 seconds.
Towards More Accurate Diffusion Model Acceleration with A Timestep Tuner
Authors: Yujun Shen, Changsong Lei, Yu Zhou, Deli Zhao, Ran Yi, Wenping Wang, Yong-Jin Liu Mengfei Xia
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: A diffusion model which is formulated to produce an image using thousands of denoising steps usually suffers from a slow inference speed. Existing acceleration algorithms simplify the sampling by skipping most steps yet exhibit considerable performance degradation. By viewing the generation of diffusion models as a discretized integral process we argue that the quality drop is partly caused by applying an inaccurate integral direction to a timestep interval. To rectify this issue we propose a timestep tuner that helps find a more accurate integral direction for a particular interval at the minimum cost. Specifically at each denoising step we replace the original parameterization by conditioning the network on a new timestep enforcing the sampling distribution towards the real one. Extensive experiments show that our plug-in design can be trained efficiently and boost the inference performance of various state-of-the-art acceleration methods especially when there are few denoising steps. For example when using 10 denoising steps on LSUN Bedroom dataset we improve the FID of DDIM from 9.65 to 6.07 simply by adopting our method for a more appropriate set of timesteps. Code is available at \href https://github.com/THU-LYJ-Lab/time-tuner  https://github.com/THU-LYJ-Lab/time-tuner .
Rethinking Generalizable Face Anti-spoofing via Hierarchical Prototype-guided Distribution Refinement in Hyperbolic Space
Authors: Ke-Yue Zhang, Taiping Yao, Shouhong Ding, Lizhuang Ma Chengyang Hu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Generalizable face anti-spoofing (FAS) approaches have drawn growing attention due to their robustness for diverse presentation attacks in unseen scenarios. Most previous methods always utilize domain generalization (DG) frameworks via directly aligning diverse source samples into a common feature space. However these methods neglect the hierarchical relations in FAS samples which may hinder the generalization ability by direct alignment. To address these issues we propose a novel Hierarchical Prototype-guided Distribution Refinement (HPDR) framework to learn embedding in hyperbolic space which facilitates the hierarchical relation construction. We also collaborate with prototype learning for hierarchical distribution refinement in hyperbolic space. In detail we propose the Hierarchical Prototype Learning to simultaneously guide domain alignment and improve the discriminative ability via constraining the multi-level relations between prototypes and instances in hyperbolic space. Moreover we design a Prototype-oriented Classifier which further considers relations between the sample and prototypes to improve the robustness of the final decision. Extensive experiments and visualizations demonstrate the effectiveness of our method against previous competitors.
IIRP-Net: Iterative Inference Residual Pyramid Network for Enhanced Image Registration
Authors: Suwei Zhang, Jiafeng Li, Ying Wen Tai Ma
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Deep learning-based image registration (DLIR) methods have achieved remarkable success in deformable image registration. We observe that iterative inference can exploit the well-trained registration network to the fullest extent. In this work we propose a novel Iterative Inference Residual Pyramid Network (IIRP-Net) to enhance registration performance without any additional training costs. In IIRP-Net we construct a streamlined pyramid registration network consisting of a feature extractor and residual flow estimators (RP-Net) to achieve generalized capabilities in feature extraction and registration. Then in the inference phase IIRP-Net employs an iterative inference strategy to enhance RP-Net by iteratively reutilizing residual flow estimators from coarse to fine. The number of iterations is adaptively determined by the proposed IterStop mechanism. We conduct extensive experiments on the FLARE and Mindboggle datasets and the results verify the effectiveness of the proposed method outperforming state-of-the-art deformable image registration methods. Our code is available at https://github.com/Torbjorn1997/IIRP-Net.