mtybilly/Instruct2DS huggingface dataset:
SceneTex: High-Quality Texture Synthesis for Indoor Scenes via Diffusion Priors
Authors: Haoxuan Li, Hsin-Ying Lee, Sergey Tulyakov, Matthias Nießner Dave Zhenyu Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We propose SceneTex a novel method for effectively generating high-quality and style-consistent textures for indoor scenes using depth-to-image diffusion priors. Unlike previous methods that either iteratively warp 2D views onto a mesh surface or distillate diffusion latent features without accurate geometric and style cues SceneTex formulates the texture synthesis task as an optimization problem in the RGB space where style and geometry consistency are properly reflected. At its core SceneTex proposes a multiresolution texture field to implicitly encode the mesh appearance. We optimize the target texture via a score-distillation-based objective function in respective RGB renderings. To further secure the style consistency across views we introduce a cross-attention decoder to predict the RGB values by cross-attending to the pre-sampled reference locations in each instance. SceneTex enables various and accurate texture synthesis for 3D-FRONT scenes demonstrating significant improvements in visual quality and prompt fidelity over the prior texture generation methods.
Neural Underwater Scene Representation
Authors: Chengxuan Zhu, Renjie Wan, Chao Xu, Boxin Shi Yunkai Tang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Among the numerous efforts towards digitally recovering the physical world Neural Radiance Fields (NeRFs) have proved effective in most cases. However underwater scene introduces unique challenges due to the absorbing water medium the local change in lighting and the dynamic contents in the scene. We aim at developing a neural underwater scene representation for these challenges modeling the complex process of attenuation unstable in-scattering and moving objects during light transport. The proposed method can reconstruct the scenes from both established datasets and in-the-wild videos with outstanding fidelity.
Progress-Aware Online Action Segmentation for Egocentric Procedural Task Videos
Authors: Ehsan Elhamifar Yuhan Shen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We address the problem of online action segmentation for egocentric procedural task videos. While previous studies have mostly focused on offline action segmentation where entire videos are available for both training and inference the transition to online action segmentation is crucial for practical applications like AR/VR task assistants. Notably applying an offline-trained model directly to online inference results in a significant performance drop due to the inconsistency between training and inference. We propose an online action segmentation framework by first modifying existing architectures to make them causal. Second we develop a novel action progress prediction module to dynamically estimate the progress of ongoing actions and using them to refine the predictions of causal action segmentation. Third we propose to learn task graphs from training videos and leverage them to obtain smooth and procedure-consistent segmentations. With the combination of progress and task graph with casual action segmentation our framework effectively addresses prediction uncertainty and oversegmentation in online action segmentation and achieves significant improvement on three egocentric datasets.
TUMTraf V2X Cooperative Perception Dataset
Authors: Gerhard Arya Wardana, Suren Sritharan, Xingcheng Zhou, Rui Song, Alois C. Knoll Walter Zimmer
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Cooperative perception offers several benefits for enhancing the capabilities of autonomous vehicles and improving road safety. Using roadside sensors in addition to onboard sensors increases reliability and extends the sensor range. External sensors offer higher situational awareness for automated vehicles and prevent occlusions. We propose CoopDet3D a cooperative multi-modal fusion model and TUMTraf-V2X a perception dataset for the cooperative 3D object detection and tracking task. Our dataset contains 2000 labeled point clouds and 5000 labeled images from five roadside and four onboard sensors. It includes 30k 3D boxes with track IDs and precise GPS and IMU data. We labeled nine categories and covered occlusion scenarios with challenging driving maneuvers like traffic violations near-miss events overtaking and U-turns. Through multiple experiments we show that our CoopDet3D camera-LiDAR fusion model achieves an increase of +14.36 3D mAP compared to a vehicle camera-LiDAR fusion model. Finally we make our dataset model labeling tool and devkit publicly available on our website: https://tum-traffic-dataset.github.io/tumtraf-v2x.
Constrained Layout Generation with Factor Graphs
Authors: Yanfei Dong, Sicong Leng, Guoji Fu, Yong Liang Goh, Wei Lu, Wee Sun Lee Mohammed Haroon Dupty
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This paper addresses the challenge of object-centric layout generation under spatial constraints seen in multiple domains including floorplan design process. The design process typically involves specifying a set of spatial constraints that include object attributes like size and inter-object relations such as relative positioning. Existing works which typically represent objects as single nodes lack the granularity to accurately model complex interactions between objects. For instance often only certain parts of an object like a room's right wall interact with adjacent objects. To address this gap we introduce a factor graph based approach with four latent variable nodes for each room and a factor node for each constraint. The factor nodes represent dependencies among the variables to which they are connected effectively capturing constraints that are potentially of a higher order. We then develop message-passing on the bipartite graph forming a factor graph neural network that is trained to produce a floorplan that aligns with the desired requirements. Our approach is simple and generates layouts faithful to the user requirements demonstrated by a large improvement in IOU scores over existing methods. Additionally our approach being inferential and accurate is well-suited to the practical human-in-the-loop design process where specifications evolve iteratively offering a practical and powerful tool for AI-guided design.
SLICE: Stabilized LIME for Consistent Explanations for Image Classification
Authors: Philipp Terhörst, Raymond Veldhuis, Raghavendra Ramachandra, Kiran Raja Revoti Prasad Bora
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Local Interpretable Model-agnostic Explanations (LIME) - a widely used post-ad-hoc model agnostic explainable AI (XAI) technique. It works by training a simple transparent (surrogate) model using random samples drawn around the neighborhood of the instance (image) to be explained (IE). Explanations are then extracted for a black-box model and a given IE using the surrogate model. However the explanations of LIME suffer from inconsistency across different runs for the same model and the same IE. We identify two main types of inconsistencies: variance in the sign and importance ranks of the segments (superpixels). These factors hinder LIME from obtaining consistent explanations. We analyze these inconsistencies and propose a new method Stabilized LIME for Consistent Explanations (SLICE). The proposed method handles the stabilization problem in two aspects: using a novel feature selection technique to eliminate spurious superpixels and an adaptive perturbation technique to generate perturbed images in the neighborhood of IE. Our results demonstrate that the explanations from SLICE exhibit significantly better consistency and fidelity than LIME (and its variant BayLime).
Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection
Authors: Choubo Ding, Yu Tian, Guansong Pang Jiawen Zhu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Open-set supervised anomaly detection (OSAD) - a recently emerging anomaly detection area - aims at utilizing a few samples of anomaly classes seen during training to detect unseen anomalies (i.e. samples from open-set anomaly classes) while effectively identifying the seen anomalies. Benefiting from the prior knowledge illustrated by the seen anomalies current OSAD methods can often largely reduce false positive errors. However these methods are trained in a closed-set setting and treat the anomaly examples as from a homogeneous distribution rendering them less effective in generalizing to unseen anomalies that can be drawn from any distribution. This paper proposes to learn heterogeneous anomaly distributions using the limited anomaly examples to address this issue. To this end we introduce a novel approach namely Anomaly Heterogeneity Learning (AHL) that simulates a diverse set of heterogeneous anomaly distributions and then utilizes them to learn a unified heterogeneous abnormality model in surrogate open-set environments. Further AHL is a generic framework that existing OSAD models can plug and play for enhancing their abnormality modeling. Extensive experiments on nine real-world anomaly detection datasets show that AHL can 1) substantially enhance different state-of-the-art OSAD models in detecting seen and unseen anomalies and 2) effectively generalize to unseen anomalies in new domains. Code is available at https://github.com/mala-lab/AHL.
SPECAT: SPatial-spEctral Cumulative-Attention Transformer for High-Resolution Hyperspectral Image Reconstruction
Authors: Shuyang Liu, Xiaoyun Yuan, Lu Fang Zhiyang Yao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Compressive spectral image reconstruction is a critical method for acquiring images with high spatial and spectral resolution. Current advanced methods which involve designing deeper networks or adding more self-attention modules are limited by the scope of attention modules and the irrelevance of attentions across different dimensions. This leads to difficulties in capturing non-local mutation features in the spatial-spectral domain and results in a significant parameter increase but only limited performance improvement. To address these issues we propose SPECAT a SPatial-spEctral Cumulative-Attention Transformer designed for high-resolution hyperspectral image reconstruction. SPECAT utilizes Cumulative-Attention Blocks (CABs) within an efficient hierarchical framework to extract features from non-local spatial-spectral details. Furthermore it employs a projection-object Dual-domain Loss Function (DLF) to integrate the optical path constraint a physical aspect often overlooked in current methodologies. Ultimately SPECAT not only significantly enhances the reconstruction quality of spectral details but also breaks through the bottleneck of mutual restriction between the cost and accuracy in existing algorithms. Our experimental results demonstrate the superiority of SPECAT achieving 40.3 dB in hyperspectral reconstruction benchmarks outperforming the state-of-the-art (SOTA) algorithms by 1.2 dB while using only 5% of the network parameters and 10% of the computational cost. The code is available at https://github.com/THU-luvision/SPECAT.
Attentive Illumination Decomposition Model for Multi-Illuminant White Balancing
Authors: Jinwoo Kim, Junsang Yu, Seon Joo Kim Dongyoung Kim
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: White balance (WB) algorithms in many commercial cameras assume single and uniform illumination leading to undesirable results when multiple lighting sources with different chromaticities exist in the scene. Prior research on multi-illuminant WB typically predicts illumination at the pixel level without fully grasping the scene's actual lighting conditions including the number and color of light sources. This often results in unnatural outcomes lacking in overall consistency. To handle this problem we present a deep white balancing model that leverages the slot attention where each slot is in charge of representing individual illuminants. This design enables the model to generate chromaticities and weight maps for individual illuminants which are then fused to compose the final illumination map. Furthermore we propose the centroid-matching loss which regulates the activation of each slot based on the color range thereby enhancing the model to separate illumination more effectively. Our method achieves the state-of-the-art performance on both single- and multi-illuminant WB benchmarks and also offers additional information such as the number of illuminants in the scene and their chromaticity. This capability allows for illumination editing an application not feasible with prior methods.
Efficient Stitchable Task Adaptation
Authors: Zizheng Pan, Jing Liu, Jianfei Cai, Bohan Zhuang Haoyu He
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The paradigm of pre-training and fine-tuning has laid the foundation for deploying deep learning models. However most fine-tuning methods are designed to meet a specific resource budget. Recently considering diverse deployment scenarios with various resource budgets SN-Net is introduced to quickly obtain numerous new networks (stitches) from the pre-trained models (anchors) in a model family via model stitching. Although promising SN-Net confronts new challenges when adapting it to new target domains including huge memory and storage requirements and a long and sub-optimal multistage adaptation process. In this work we present a novel framework Efficient Stitchable Task Adaptation (ESTA) to efficiently produce a palette of fine-tuned models that adhere to diverse resource constraints. Specifically we first tailor parameter-efficient fine-tuning to share low-rank updates among the stitches while maintaining independent bias terms. In this way we largely reduce fine-tuning memory burdens and mitigate the interference among stitches that arises in task adaptation. Furthermore we streamline a simple yet effective one-stage deployment pipeline which estimates the important stitches to deploy with training-time gradient statistics. By assigning higher sampling probabilities to important stitches we also get a boosted Pareto frontier. Extensive experiments on 25 downstream visual recognition tasks demonstrate that our ESTA is capable of generating stitches with smooth accuracy-efficiency trade-offs and surpasses the direct SN-Net adaptation by remarkable margins with significantly lower training time and fewer trainable parameters. Furthermore we demonstrate the flexibility and scalability of our ESTA framework by stitching LLMs from LLaMA family obtaining chatbot stitches of assorted sizes.
Image Processing GNN: Breaking Rigidity in Super-Resolution
Authors: Hanting Chen, Chao Xu, Yunhe Wang Yuchuan Tian
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Super-Resolution (SR) reconstructs high-resolution images from low-resolution ones. CNNs and window-attention methods are two major categories of canonical SR models. However these measures are rigid: in both operations each pixel gathers the same number of neighboring pixels hindering their effectiveness in SR tasks. Alternatively we leverage the flexibility of graphs and propose the Image Processing GNN (IPG) model to break the rigidity that dominates previous SR methods. Firstly SR is unbalanced in that most reconstruction efforts are concentrated to a small proportion of detail-rich image parts. Hence we leverage degree flexibility by assigning higher node degrees to detail-rich image nodes. Then in order to construct graphs for SR-effective aggregation we treat images as pixel node sets rather than patch nodes. Lastly we hold that both local and global information are crucial for SR performance. In the hope of gathering pixel information from both local and global scales efficiently via flexible graphs we search node connections within nearby regions to construct local graphs; and find connections within a strided sampling space of the whole image for global graphs. The flexibility of graphs boosts the SR performance of the IPG model. Experiment results on various datasets demonstrates that the proposed IPG outperforms State-of-the-Art baselines. Codes are available at https://github.com/huawei-noah/Efficient-Computing/tree/master/LowLevel/IPG.
Revisiting Counterfactual Problems in Referring Expression Comprehension
Authors: Ruifan Li Zhihan Yu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Traditional referring expression comprehension (REC) aims to locate the target referent in an image guided by a text query. Several previous methods have studied on the Counterfactual problem in REC (C-REC) where the objects for a given query cannot be found in the image. However these methods focus on the overall image-text or specific attribute mismatch only. In this paper we address the C-REC problem from a deep perspective of fine-grained attributes. To this aim we first propose a fine-grained counterfactual sample generation method to construct C-REC datasets. Specifically we leverage pre-trained language model such as BERT to modify the attribute words in the queries obtaining the corresponding counterfactual samples. Furthermore we propose a C-REC framework. We first adopt three encoders to extract image text and attribute features. Then our dual-branch attentive fusion module fuses these cross-modal features with two branches by an attention mechanism. At last two prediction heads generate a bounding box and a counterfactual label respectively. In addition we incorporate contrastive learning with the generated counterfactual samples as negatives to enhance the counterfactual perception. Extensive experiments show that our framework achieves promising performance on both public REC datasets RefCOCO/+/g and our constructed C-REC datasets C-RefCOCO/+/g. The code and data are available at https://github.com/Glacier0012/CREC.
DyBluRF: Dynamic Neural Radiance Fields from Blurry Monocular Video
Authors: Xingyi Li, Liao Shen, Xinyi Ye, Ke Xian, Zhiguo Cao Huiqiang Sun
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent advancements in dynamic neural radiance field methods have yielded remarkable outcomes. However these approaches rely on the assumption of sharp input images. When faced with motion blur existing dynamic NeRF methods often struggle to generate high-quality novel views. In this paper we propose DyBluRF a dynamic radiance field approach that synthesizes sharp novel views from a monocular video affected by motion blur. To account for motion blur in input images we simultaneously capture the camera trajectory and object Discrete Cosine Transform (DCT) trajectories within the scene. Additionally we employ a global cross-time rendering approach to ensure consistent temporal coherence across the entire scene. We curate a dataset comprising diverse dynamic scenes that are specifically tailored for our task. Experimental results on our dataset demonstrate that our method outperforms existing approaches in generating sharp novel views from motion-blurred inputs while maintaining spatial-temporal consistency of the scene.
Compressed 3D Gaussian Splatting for Accelerated Novel View Synthesis
Authors: Josef Stumpfegger, Rüdiger Westermann Simon Niedermayr
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recently high-fidelity scene reconstruction with an optimized 3D Gaussian splat representation has been introduced for novel view synthesis from sparse image sets. Making such representations suitable for applications like network streaming and rendering on low-power devices requires significantly reduced memory consumption as well as improved rendering efficiency. We propose a compressed 3D Gaussian splat representation that utilizes sensitivity-aware vector clustering with quantization-aware training to compress directional colors and Gaussian parameters. The learned codebooks have low bitrates and achieve a compression rate of up to 31 on real-world scenes with only minimal degradation of visual quality. We demonstrate that the compressed splat representation can be efficiently rendered with hardware rasterization on lightweight GPUs at up to 4 higher framerates than reported via an optimized GPU compute pipeline. Extensive experiments across multiple datasets demonstrate the robustness and rendering speed of the proposed approach.
Separating the "Chirp" from the "Chat": Self-supervised Visual Grounding of Sound and Language
Authors: Andrew Zisserman, John R. Hershey, William T. Freeman Mark Hamilton
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present DenseAV a novel dual encoder grounding architecture that learns high-resolution semantically meaningful and audio-visual aligned features solely through watching videos. We show that DenseAV can discover the "meaning" of words and the "location" of sounds without explicit localization supervision. Furthermore it automatically discovers and distinguishes between these two types of associations without supervision. We show that DenseAV's localization abilities arise from a new multi-head feature aggregation operator that directly compares dense image and audio representations for contrastive learning. In contrast many other systems that learn "global" audio and video representations cannot localize words and sound. Finally we contribute two new datasets to improve the evaluation of AV representations through speech and sound prompted semantic segmentation. On these and other datasets we show DenseAV dramatically outperforms the prior art on speech and sound prompted semantic segmentation. DenseAV outperforms the current state-of-the-art ImageBind on cross-modal retrieval using fewer than half of the parameters. Project Page: https://aka.ms/denseav
Towards Generalizing to Unseen Domains with Few Labels
Authors: Sanoojan Baliah, Malitha Gunawardhana, Muhammad Haris Khan Chamuditha Jayanga Galappaththige
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We approach the challenge of addressing semi-supervised domain generalization (SSDG). Specifically our aim is to obtain a model that learns domain-generalizable features by leveraging a limited subset of labelled data alongside a substantially larger pool of unlabeled data. Existing domain generalization (DG) methods which are unable to exploit unlabeled data perform poorly compared to semi-supervised learning (SSL) methods under SSDG setting. Nevertheless SSL methods have considerable room for performance improvement when compared to fully-supervised DG training. To tackle this underexplored yet highly practical problem of SSDG we make the following core contributions. First we propose a feature-based conformity technique that matches the posterior distributions from the feature space with the pseudo-label from the model's output space. Second we develop a semantics alignment loss to learn semantically-compatible representations by regularizing the semantic structure in the feature space. Our method is plug-and-play and can be readily integrated with different SSL-based SSDG baselines without introducing any additional parameters. Extensive experimental results across five challenging DG benchmarks with four strong SSL baselines suggest that our method provides consistent and notable gains in two different SSDG settings.
MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding
Authors: Hengduo Li, Young Kyun Jang, Menglin Jia, Xuefei Cao, Ashish Shah, Abhinav Shrivastava, Ser-Nam Lim Bo He
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: With the success of large language models (LLMs) integrating the vision model into LLMs to build vision-language foundation models has gained much more interest recently. However existing LLM-based large multimodal models (e.g. Video-LLaMA VideoChat) can only take in a limited number of frames for short video understanding. In this study we mainly focus on designing an efficient and effective model for long-term video understanding. Instead of trying to process more frames simultaneously like most existing work we propose to process videos in an online manner and store past video information in a memory bank. This allows our model to reference historical video content for long-term analysis without exceeding LLMs' context length constraints or GPU memory limits. Our memory bank can be seamlessly integrated into current multimodal LLMs in an off-the-shelf manner. We conduct extensive experiments on various video understanding tasks such as long-video understanding video question answering and video captioning and our model can achieve state-of-the-art performances across multiple datasets.
AAMDM: Accelerated Auto-regressive Motion Diffusion Model
Authors: Calvin Qiao, Guanqiao Ren, KangKang Yin, Sehoon Ha Tianyu Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Interactive motion synthesis is essential in creating immersive experiences in entertainment applications such as video games and virtual reality. However generating animations that are both high-quality and contextually responsive remains a challenge. Traditional techniques in the game industry can produce high-fidelity animations but suffer from high computational costs and poor scalability. Trained neural network models alleviate the memory and speed issues yet fall short on generating diverse motions. Diffusion models offer diverse motion synthesis with low memory usage but require expensive reverse diffusion processes. This paper introduces the Accelerated Auto-regressive Motion Diffusion Model (AAMDM) a novel motion synthesis framework designed to achieve quality diversity and efficiency all together. AAMDM integrates Denoising Diffusion GANs as a fast Generation Module and an Auto-regressive Diffusion Model as a Polishing Module. Furthermore AAMDM operates in a lower-dimensional embedded space rather than the full-dimensional pose space which reduces the training complexity as well as further improves the performance. We show that AAMDM outperforms existing methods in motion quality diversity and runtime efficiency through comprehensive quantitative analyses and visual comparisons. We also demonstrate the effectiveness of each algorithmic component through ablation studies.
Towards Understanding Cross and Self-Attention in Stable Diffusion for Text-Guided Image Editing
Authors: Chengyu Wang, Tingfeng Cao, Kui Jia, Jun Huang Bingyan Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Deep Text-to-Image Synthesis (TIS) models such as Stable Diffusion have recently gained significant popularity for creative text-to-image generation. However for domain-specific scenarios tuning-free Text-guided Image Editing (TIE) is of greater importance for application developers. This approach modifies objects or object properties in images by manipulating feature components in attention layers during the generation process. Nevertheless little is known about the semantic meanings that these attention layers have learned and which parts of the attention maps contribute to the success of image editing. In this paper we conduct an in-depth probing analysis and demonstrate that cross-attention maps in Stable Diffusion often contain object attribution information which can result in editing failures. In contrast self-attention maps play a crucial role in preserving the geometric and shape details of the source image during the transformation to the target image. Our analysis offers valuable insights into understanding cross and self-attention mechanisms in diffusion models. Furthermore based on our findings we propose a simplified yet more stable and efficient tuning-free procedure that modifies only the self-attention maps of specified attention layers during the denoising process. Experimental results show that our simplified method consistently surpasses the performance of popular approaches on multiple datasets.
Dr2Net: Dynamic Reversible Dual-Residual Networks for Memory-Efficient Finetuning
Authors: Shuming Liu, Karttikeya Mangalam, Guocheng Qian, Fatimah Zohra, Abdulmohsen Alghannam, Jitendra Malik, Bernard Ghanem Chen Zhao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Large pretrained models are increasingly crucial in modern computer vision tasks. These models are typically used in downstream tasks by end-to-end finetuning which is highly memory-intensive for tasks with high-resolution data e.g. video understanding small object detection and point cloud analysis. In this paper we propose Dynamic Reversible Dual-Residual Networks or Dr2Net a novel family of network architectures that acts as a surrogate network to finetune a pretrained model with substantially reduced memory consumption. Dr2Net contains two types of residual connections one maintaining the residual structure in the pretrained models and the other making the network reversible. Due to its reversibility intermediate activations which can be reconstructed from output are cleared from memory during training. We use two coefficients on either type of residual connections respectively and introduce a dynamic training strategy that seamlessly transitions the pretrained model to a reversible network with much higher numerical precision. We evaluate Dr2Net on various pretrained models and various tasks and show that it can reach comparable performance to conventional finetuning but with significantly less memory usage.
PNeRV: Enhancing Spatial Consistency via Pyramidal Neural Representation for Videos
Authors: M. Salman Asif, Zhan Ma Qi Zhao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The primary focus of Neural Representation for Videos (NeRV) is to effectively model its spatiotemporal consistency. However current NeRV systems often face a significant issue of spatial inconsistency leading to decreased perceptual quality. To address this issue we introduce the Pyramidal Neural Representation for Videos (PNeRV) which is built on a multi-scale information connection and comprises a lightweight rescaling operator Kronecker Fully-connected layer (KFc) and a Benign Selective Memory (BSM) mechanism. The KFc inspired by the tensor decomposition of the vanilla Fully-connected layer facilitates low-cost rescaling and global correlation modeling. BSM merges high-level features with granular ones adaptively. Furthermore we provide an analysis based on the Universal Approximation Theory of the NeRV system and validate the effectiveness of the proposed PNeRV. We conducted comprehensive experiments to demonstrate that PNeRV surpasses the performance of contemporary NeRV models achieving the best results in video regression on UVG and DAVIS under various metrics (PSNR SSIM LPIPS and FVD). Compared to vanilla NeRV PNeRV achieves a +4.49 dB gain in PSNR and a 231% increase in FVD on UVG along with a +3.28 dB PSNR and 634% FVD increase on DAVIS.
LTGC: Long-tail Recognition via Leveraging LLMs-driven Generated Content
Authors: Yalun Dai, Hao Li, Wei Hu, Fan Zhang, Jun Liu Qihao Zhao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Long-tail recognition is challenging because it requires the model to learn good representations from tail categories and address imbalances across all categories. In this paper we propose a novel generative and fine-tuning framework LTGC to handle long-tail recognition via leveraging generated content. Firstly inspired by the rich implicit knowledge in large-scale models (e.g. large language models LLMs) LTGC leverages the power of these models to parse and reason over the original tail data to produce diverse tail-class content. We then propose several novel designs for LTGC to ensure the quality of the generated data and to efficiently fine-tune the model using both the generated and original data. The visualization demonstrates the effectiveness of the generation module in LTGC which produces accurate and diverse tail data. Additionally the experimental results demonstrate that our LTGC outperforms existing state-of-the-art methods on popular long-tailed benchmarks.
DiverGen: Improving Instance Segmentation by Learning Wider Data Distribution with More Diverse Generative Data
Authors: Muzhi Zhu, Hao Chen, Yang Liu, Weijia Wu, Huaqi Zhang, Chunhua Shen Chengxiang Fan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Instance segmentation is data-hungry and as model capacity increases data scale becomes crucial for improving the accuracy. Most instance segmentation datasets today require costly manual annotation limiting their data scale. Models trained on such data are prone to overfitting on the training set especially for those rare categories. While recent works have delved into exploiting generative models to create synthetic datasets for data augmentation these approaches do not efficiently harness the full potential of generative models. To address these issues we introduce a more efficient strategy to construct generative datasets for data augmentation termed DiverGen. Firstly we provide an explanation of the role of generative data from the perspective of distribution discrepancy. We investigate the impact of different data on the distribution learned by the model. We argue that generative data can expand the data distribution that the model can learn thus mitigating overfitting. Additionally we find that the diversity of generative data is crucial for improving model performance and enhance it through various strategies including category diversity prompt diversity and generative model diversity. With these strategies we can scale the data to millions while maintaining the trend of model performance improvement. On the LVIS dataset DiverGen significantly outperforms the strong model X-Paste achieving +1.1 box AP and +1.1 mask AP across all categories and +1.9 box AP and +2.5 mask AP for rare categories. Our codes are available at https://github.com/aim-uofa/DiverGen.
Neural Refinement for Absolute Pose Regression with Feature Synthesis
Authors: Yash Bhalgat, Xinghui Li, Jia-Wang Bian, Kejie Li, Zirui Wang, Victor Adrian Prisacariu Shuai Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Absolute Pose Regression (APR) methods use deep neural networks to directly regress camera poses from RGB images. However the predominant APR architectures only rely on 2D operations during inference resulting in limited accuracy of pose estimation due to the lack of 3D geometry constraints or priors. In this work we propose a test-time refinement pipeline that leverages implicit geometric constraints using a robust feature field to enhance the ability of APR methods to use 3D information during inference. We also introduce a novel Neural Feature Synthesizer (NeFeS) model which encodes 3D geometric features during training and directly renders dense novel view features at test time to refine APR methods. To enhance the robustness of our model we introduce a feature fusion module and a progressive training strategy. Our proposed method achieves state-of-the-art single-image APR accuracy on indoor and outdoor datasets. Code will be released at https://github.com/ActiveVisionLab/NeFeS.
Learning Disentangled Identifiers for Action-Customized Text-to-Image Generation
Authors: Biao Gong, Yutong Feng, Xi Chen, Yuqian Fu, Yu Liu, Donglin Wang Siteng Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This study focuses on a novel task in text-to-image (T2I) generation namely action customization. The objective of this task is to learn the co-existing action from limited data and generalize it to unseen humans or even animals. Experimental results show that existing subject-driven customization methods fail to learn the representative characteristics of actions and struggle in decoupling actions from context features including appearance. To overcome the preference for low-level features and the entanglement of high-level features we propose an inversion-based method Action-Disentangled Identifier (ADI) to learn action-specific identifiers from the exemplar images. ADI first expands the semantic conditioning space by introducing layer-wise identifier tokens thereby increasing the representational richness while distributing the inversion across different features. Then to block the inversion of action-agnostic features ADI extracts the gradient invariance from the constructed sample triples and masks the updates of irrelevant channels. To comprehensively evaluate the task we present an ActionBench that includes a variety of actions each accompanied by meticulously selected samples. Both quantitative and qualitative results show that our ADI outperforms existing baselines in action-customized T2I generation. Our project page is at https://adi-t2i.github.io/ADI.
Automatic Controllable Colorization via Imagination
Authors: Yue Wu, Qifeng Chen, Chenyang Lei Xiaoyan Cong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We propose a framework for automatic colorization that allows for iterative editing and modifications. The core of our framework lies in an imagination module: by understanding the content within a grayscale image we utilize a pre-trained image generation model to generate multiple images that contain the same content. These images serve as references for coloring mimicking the process of human experts. As the synthesized images can be imperfect or different from the original grayscale image we propose a Reference Refinement Module to select the optimal reference composition. Unlike most previous end-to-end automatic colorization algorithms our framework allows for iterative and localized modifications of the colorization results because we explicitly model the coloring samples. Extensive experiments demonstrate the superiority of our framework over existing automatic colorization algorithms in editability and flexibility. Project page: https://xy-cong.github.io/imagine-colorization/.
Point Transformer V3: Simpler Faster Stronger
Authors: Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao Xiaoyang Wu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This paper is not motivated to seek innovation within the attention mechanism. Instead it focuses on overcoming the existing trade-offs between accuracy and efficiency within the context of point cloud processing leveraging the power of scale. Drawing inspiration from recent advances in 3D large-scale representation learning we recognize that model performance is more influenced by scale than by intricate design. Therefore we present Point Transformer V3 (PTv3) which prioritizes simplicity and efficiency over the accuracy of certain mechanisms that are minor to the overall performance after scaling such as replacing the precise neighbor search by KNN with an efficient serialized neighbor mapping of point clouds organized with specific patterns. This principle enables significant scaling expanding the receptive field from 16 to 1024 points while remaining efficient (a 3x increase in processing speed and a 10x improvement in memory efficiency compared with its predecessor PTv2). PTv3 attains state-of-the-art results on over 20 downstream tasks that span both indoor and outdoor scenarios. Further enhanced with multi-dataset joint training PTv3 pushes these results to a higher level.
DiffCast: A Unified Framework via Residual Diffusion for Precipitation Nowcasting
Authors: Xutao Li, Yunming Ye, Baoquan Zhang, Chuyao Luo, Kuai Dai, Rui Wang, Xunlai Chen Demin Yu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Precipitation nowcasting is an important spatio-temporal prediction task to predict the radar echoes sequences based on current observations which can serve both meteorological science and smart city applications. Due to the chaotic evolution nature of the precipitation systems it is a very challenging problem. Previous studies address the problem either from the perspectives of deterministic modeling or probabilistic modeling. However their predictions suffer from the blurry high-value echoes fading away and position inaccurate issues. The root reason of these issues is that the chaotic evolutionary precipitation systems are not appropriately modeled. Inspired by the nature of the systems we propose to decompose and model them from the perspective of global deterministic motion and local stochastic variations with residual mechanism. A unified and flexible framework that can equip any type of spatio-temporal models is proposed based on residual diffusion which effectively tackles the shortcomings of previous methods. Extensive experimental results on four publicly available radar datasets demonstrate the effectiveness and superiority of the proposed framework compared to state-of-the-art techniques. Our code is publicly available at https://github.com/DeminYu98/DiffCast.
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
Authors: Andrew Westbury, Lorenzo Torresani, Kris Kitani, Jitendra Malik, Triantafyllos Afouras, Kumar Ashutosh, Vijay Baiyya, Siddhant Bansal, Bikram Boote, Eugene Byrne, Zach Chavis, Joya Chen, Feng Cheng, Fu-Jen Chu, Sean Crane, Avijit Dasgupta, Jing Dong, Maria Escobar, Cristhian Forigua, Abrham Gebreselasie, Sanjay Haresh, Jing Huang, Md Mohaiminul Islam, Suyog Jain, Rawal Khirodkar, Devansh Kukreja, Kevin J Liang, Jia-Wei Liu, Sagnik Majumder, Yongsen Mao, Miguel Martin, Effrosyni Mavroudi, Tushar Nagarajan, Francesco Ragusa, Santhosh Kumar Ramakrishnan, Luigi Seminara, Arjun Somayazulu, Yale Song, Shan Su, Zihui Xue, Edward Zhang, Jinxu Zhang, Angela Castillo, Changan Chen, Xinzhu Fu, Ryosuke Furuta, Cristina Gonzalez, Prince Gupta, Jiabo Hu, Yifei Huang, Yiming Huang, Weslie Khoo, Anush Kumar, Robert Kuo, Sach Lakhavani, Miao Liu, Mi Luo, Zhengyi Luo, Brighid Meredith, Austin Miller, Oluwatumininu Oguntola, Xiaqing Pan, Penny Peng, Shraman Pramanick, Merey Ramazanova, Fiona Ryan, Wei Shan, Kiran Somasundaram, Chenan Song, Audrey Southerland, Masatoshi Tateno, Huiyu Wang, Yuchen Wang, Takuma Yagi, Mingfei Yan, Xitong Yang, Zecheng Yu, Shengxin Cindy Zha, Chen Zhao, Ziwei Zhao, Zhifan Zhu, Jeff Zhuo, Pablo Arbelaez, Gedas Bertasius, Dima Damen, Jakob Engel, Giovanni Maria Farinella, Antonino Furnari, Bernard Ghanem, Judy Hoffman, C.V. Jawahar, Richard Newcombe, Hyun Soo Park, James M. Rehg, Yoichi Sato, Manolis Savva, Jianbo Shi, Mike Zheng Shou, Michael Wray Kristen Grauman
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present Ego-Exo4D a diverse large-scale multimodal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric and exocentric video of skilled human activities (e.g. sports music dance bike repair). 740 participants from 13 cities worldwide performed these activities in 123 different natural scene contexts yielding long-form captures from 1 to 42 minutes each and 1286 hours of video combined. The multimodal nature of the dataset is unprecedented: the video is accompanied by multichannel audio eye gaze 3D point clouds camera poses IMU and multiple paired language descriptions---including a novel "expert commentary" done by coaches and teachers and tailored to the skilled-activity domain. To push the frontier of first-person video understanding of skilled human activity we also present a suite of benchmark tasks and their annotations including fine-grained activity understanding proficiency estimation cross-view translation and 3D hand/body pose. All resources are open sourced to fuel new research in the community.
Point Cloud Pre-training with Diffusion Models
Authors: Xiaoshui Huang, Guofeng Mei, Yuenan Hou, Zhaoyang Lyu, Bo Dai, Wanli Ouyang, Yongshun Gong Xiao Zheng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Pre-training a model and then fine-tuning it on downstream tasks has demonstrated significant success in the 2D image and NLP domains. However due to the unordered and non-uniform density characteristics of point clouds it is non-trivial to explore the prior knowledge of point clouds and pre-train a point cloud backbone. In this paper we propose a novel pre-training method called Point cloud Diffusion pre-training PointDif. We consider the point cloud pre-training task as a conditional point-to-point generation problem and introduce a conditional point generator. This generator aggregates the features extracted by the backbone and employs them as the condition to guide the point-to-point recovery from the noisy point cloud thereby assisting the backbone in capturing both local and global geometric priors as well as the global point density distribution of the object. We also present a recurrent uniform sampling optimization strategy which enables the model to uniformly recover from various noise levels and learn from balanced supervision. Our PointDif achieves substantial improvement across various real-world datasets for diverse downstream tasks such as classification segmentation and detection. Specifically PointDif attains 70.0% mIoU on S3DIS Area 5 for the segmentation task and achieves an average improvement of 2.4% on ScanObjectNN for the classification task compared to TAP. Furthermore our pre-training framework can be flexibly applied to diverse point cloud backbones and bring considerable gains. Code is available at https://github.com/zhengxiaozx/PointDif