mtybilly/Instruct2DS huggingface dataset:
Tri-Perspective View Decomposition for Geometry-Aware Depth Completion
Authors: Yuankai Lin, Kun Wang, Yupeng Zheng, Yufei Wang, Zhenyu Zhang, Jun Li, Jian Yang Zhiqiang Yan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Depth completion is a vital task for autonomous driving as it involves reconstructing the precise 3D geometry of a scene from sparse and noisy depth measurements. However most existing methods either rely only on 2D depth representations or directly incorporate raw 3D point clouds for compensation which are still insufficient to capture the fine-grained 3D geometry of the scene. To address this challenge we introduce Tri-Perspective View Decomposition (TPVD) a novel framework that can explicitly model 3D geometry. In particular (1) TPVD ingeniously decomposes the original point cloud into three 2D views one of which corresponds to the sparse depth input. (2) We design TPV Fusion to update the 2D TPV features through recurrent 2D-3D-2D aggregation where a Distance-Aware Spherical Convolution (DASC) is applied. (3) By adaptively choosing TPV affinitive neighbors the newly proposed Geometric Spatial Propagation Network (GSPN) further improves the geometric consistency. As a result our TPVD outperforms existing methods on KITTI NYUv2 and SUN RGBD. Furthermore we build a novel depth completion dataset named TOFDC which is acquired by the time-of-flight (TOF) sensor and the color camera on smartphones.
Boosting Adversarial Training via Fisher-Rao Norm-based Regularization
Authors: Wenjie Ruan Xiangyu Yin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Adversarial training is extensively utilized to improve the adversarial robustness of deep neural networks. Yet mitigating the degradation of standard generalization performance in adversarial-trained models remains an open problem. This paper attempts to resolve this issue through the lens of model complexity. First We leverage the Fisher-Rao norm a geometrically invariant metric for model complexity to establish the non-trivial bounds of the Cross-Entropy Loss-based Rademacher complexity for a ReLU-activated Multi-Layer Perceptron. Building upon this observation we propose a novel regularization framework called Logit-Oriented Adversarial Training (LOAT) which can mitigate the trade-off between robustness and accuracy while imposing only a negligible increase in computational overhead. Our extensive experiments demonstrate that the proposed regularization strategy can boost the performance of the prevalent adversarial training algorithms including PGD-AT TRADES TRADES (LSE) MART and DM-AT across various network architectures. Our code will be available at https://github.com/TrustAI/LOAT.
Learned Representation-Guided Diffusion Models for Large-Image Generation
Authors: Srikar Yellapragada, Minh-Quan Le, Saarthak Kapse, Prateek Prasanna, Joel Saltz, Dimitris Samaras Alexandros Graikos
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: To synthesize high-fidelity samples diffusion models typically require auxiliary data to guide the generation process. However it is impractical to procure the painstaking patch-level annotation effort required in specialized domains like histopathology and satellite imagery; it is often performed by domain experts and involves hundreds of millions of patches. Modern-day self-supervised learning (SSL) representations encode rich semantic and visual information. In this paper we posit that such representations are expressive enough to act as proxies to fine-grained human labels. We introduce a novel approach that trains diffusion models conditioned on embeddings from SSL. Our diffusion models successfully project these features back to high-quality histopathology and remote sensing images. In addition we construct larger images by assembling spatially consistent patches inferred from SSL embeddings preserving long-range dependencies. Augmenting real data by generating variations of real images improves downstream classifier accuracy for patch-level and larger image-scale classification tasks. Our models are effective even on datasets not encountered during training demonstrating their robustness and generalizability. Generating images from learned embeddings is agnostic to the source of the embeddings. The SSL embeddings used to generate a large image can either be extracted from a reference image or sampled from an auxiliary model conditioned on any related modality (e.g. class labels text genomic data). As proof of concept we introduce the text-to-large image synthesis paradigm where we successfully synthesize large pathology and satellite images out of text descriptions.
DAVE - A Detect-and-Verify Paradigm for Low-Shot Counting
Authors: Alan Lukeži?, Vitjan Zavrtanik, Matej Kristan Jer Pelhan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Low-shot counters estimate the number of objects corresponding to a selected category based on only few or no exemplars annotated in the image. The current state-of-the-art estimates the total counts as the sum over the object location density map but do not provide object locations and sizes which are crucial for many applications. This is addressed by detection-based counters which however fall behind in the total count accuracy. Furthermore both approaches tend to overestimate the counts in the presence of other object classes due to many false positives. We propose DAVE a low-shot counter based on a detect-and-verify paradigm that avoids the aforementioned issues by first generating a high-recall detection set and then verifying the detections to identify and remove the outliers.This jointly increases the recall and precision leading to accurate counts. DAVE outperforms the top density-based counters by ?20% in the total count MAE it outperforms the most recent detection-based counter by ?20% in detection quality and sets a new state-of-the-art in zero-shot as well as text-prompt-based counting.
Ranni: Taming Text-to-Image Diffusion for Accurate Instruction Following
Authors: Biao Gong, Di Chen, Yujun Shen, Yu Liu, Jingren Zhou Yutong Feng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Existing text-to-image (T2I) diffusion models usually struggle in interpreting complex prompts especially those with quantity object-attribute binding and multi-subject descriptions. In this work we introduce a semantic panel as the middleware in decoding texts to images supporting the generator to better follow instructions. The panel is obtained through arranging the visual concepts parsed from the input text by the aid of large language models and then injected into the denoising network as a detailed control signal to complement the text condition. To facilitate text-to-panel learning we come up with a carefully designed semantic formatting protocol accompanied by a fully-automatic data preparation pipeline. Thanks to such a design our approach which we call Ranni manages to enhance a pre-trained T2I generator regarding its textual controllability. More importantly the introduction of the generative middleware brings a more convenient form of interaction (i.e. directly adjusting the elements in the panel or using language instructions) and further allows users to finely customize their generation based on which we develop a practical system and showcase its potential in continuous generation and chatting-based editing.
Relaxed Contrastive Learning for Federated Learning
Authors: Jinkyu Kim, Geeho Kim, Bohyung Han Seonguk Seo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We propose a novel contrastive learning framework to effectively address the challenges of data heterogeneity in federated learning. We first analyze the inconsistency of gradient updates across clients during local training and establish its dependence on the distribution of feature representations leading to the derivation of the supervised contrastive learning (SCL) objective to mitigate local deviations. In addition we show that a naive integration of SCL into federated learning incurs representation collapse resulting in slow convergence and limited performance gains. To address this issue we introduce a relaxed contrastive learning loss that imposes a divergence penalty on excessively similar sample pairs within each class. This strategy prevents collapsed representations and enhances feature transferability facilitating collaborative training and leading to significant performance improvements. Our framework outperforms all existing federated learning approaches by significant margins on the standard benchmarks as demonstrated by extensive experimental results. The source code is available at our project page(https://github.com/skynbe/FedRCL).
Direct2.5: Diverse Text-to-3D Generation via Multi-view 2.5D Diffusion
Authors: Jingyang Zhang, Shiwei Li, Tian Fang, David McKinnon, Yanghai Tsin, Long Quan, Xun Cao, Yao Yao Yuanxun Lu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent advances in generative AI have unveiled significant potential for the creation of 3D content. However current methods either apply a pre-trained 2D diffusion model with the time-consuming score distillation sampling (SDS) or a direct 3D diffusion model trained on limited 3D data losing generation diversity. In this work we approach the problem by employing a multi-view 2.5D diffusion fine-tuned from a pre-trained 2D diffusion model. The multi-view 2.5D diffusion directly models the structural distribution of 3D data while still maintaining the strong generalization ability of the original 2D diffusion model filling the gap between 2D diffusion-based and direct 3D diffusion-based methods for 3D content generation. During inference multi-view normal maps are generated using the 2.5D diffusion and a novel differentiable rasterization scheme is introduced to fuse the almost consistent multi-view normal maps into a consistent 3D model. We further design a normal-conditioned multi-view image generation module for fast appearance generation given the 3D geometry. Our method is a one-pass diffusion process and does not require any SDS optimization as post-processing. We demonstrate through extensive experiments that our direct 2.5D generation with the specially-designed fusion scheme can achieve diverse mode-seeking-free and high-fidelity 3D content generation in only 10 seconds.
Efficient LoFTR: Semi-Dense Local Feature Matching with Sparse-Like Speed
Authors: Xingyi He, Sida Peng, Dongli Tan, Xiaowei Zhou Yifan Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present a novel method for efficiently producing semi-dense matches across images. Previous detector-free matcher LoFTR has shown remarkable matching capability in handling large-viewpoint change and texture-poor scenarios but suffers from low efficiency. We revisit its design choices and derive multiple improvements for both efficiency and accuracy. One key observation is that performing the transformer over the entire feature map is redundant due to shared local information therefore we propose an aggregated attention mechanism with adaptive token selection for efficiency. Furthermore we find spatial variance exists in LoFTR's fine correlation module which is adverse to matching accuracy. A novel two-stage correlation layer is proposed to achieve accurate subpixel correspondences for accuracy improvement. Our efficiency optimized model is ~ 2.5xfaster than LoFTR which can even surpass state-of-the-art efficient sparse matching pipeline SuperPoint + LightGlue. Moreover extensive experiments show that our method can achieve higher accuracy compared with competitive semi-dense matchers with considerable efficiency benefits. This opens up exciting prospects for large-scale or latency-sensitive applications such as image retrieval and 3D reconstruction. Project page: https://zju3dv.github.io/efficientloftr/.
Contextual Augmented Global Contrast for Multimodal Intent Recognition
Authors: Zhiwen Xie, Mang Ye, Huyin Zhang Kaili Sun
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Multimodal intent recognition (MIR) aims to perceive the human intent polarity via language visual and acoustic modalities. The inherent intent ambiguity makes it challenging to recognize in multimodal scenarios. Existing MIR methods tend to model the individual video independently ignoring global contextual information across videos. This learning manner inevitably introduces perception biases exacerbated by the inconsistencies of the multimodal representation amplifying the intent uncertainty. This challenge motivates us to explore effective global context modeling. Thus we propose a context-augmented global contrast (CAGC) method to capture rich global context features by mining both intra-and cross-video context interactions for MIR. Concretely we design a context-augmented transformer module to extract global context dependencies across videos. To further alleviate error accumulation and interference we develop a cross-video bank that retrieves effective video sources by considering both intentional tendency and video similarity. Furthermore we introduce a global context-guided contrastive learning scheme designed to mitigate inconsistencies arising from global context and individual modalities in different feature spaces. This scheme incorporates global cues as the supervision to capture robust the multimodal intent representation. Experiments demonstrate CAGC obtains superior performance than state-of-the-art MIR methods. We also generalize our approach to a closely related task multimodal sentiment analysis achieving the comparable performance.
Pre-trained Model Guided Fine-Tuning for Zero-Shot Adversarial Robustness
Authors: Jie Zhang, Zheng Yuan, Shiguang Shan Sibo Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Large-scale pre-trained vision-language models like CLIP have demonstrated impressive performance across various tasks and exhibit remarkable zero-shot generalization capability while they are also vulnerable to imperceptible adversarial examples. Existing works typically employ adversarial training (fine-tuning) as a defense method against adversarial examples. However direct application to the CLIP model may result in overfitting compromising the model's capacity for generalization. In this paper we propose Pre-trained Model Guided Adversarial Fine-Tuning (PMG-AFT) method which leverages supervision from the original pre-trained model by carefully designing an auxiliary branch to enhance the model's zero-shot adversarial robustness. Specifically PMG-AFT minimizes the distance between the features of adversarial examples in the target model and those in the pre-trained model aiming to preserve the generalization features already captured by the pre-trained model. Extensive Experiments on 15 zero-shot datasets demonstrate that PMG-AFT significantly outperforms the state-of-the-art method improving the top-1 robust accuracy by an average of 4.99%. Furthermore our approach consistently improves clean accuracy by an average of 8.72%.
MatFuse: Controllable Material Generation with Diffusion Models
Authors: Renato Sortino, Simone Palazzo, Concetto Spampinato Giuseppe Vecchio
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Creating high-quality materials in computer graphics is a challenging and time-consuming task which requires great expertise. To simplify this process we introduce MatFuse a unified approach that harnesses the generative power of diffusion models for creation and editing of 3D materials. Our method integrates multiple sources of conditioning including color palettes sketches text and pictures enhancing creative possibilities and granting fine-grained control over material synthesis. Additionally MatFuse enables map-level material editing capabilities through latent manipulation by means of a multi-encoder compression model which learns a disentangled latent representation for each map. We demonstrate the effectiveness of MatFuse under multiple conditioning settings and explore the potential of material editing. Finally we assess the quality of the generated materials both quantitatively in terms of CLIP-IQA and FID scores and qualitatively by conducting a user study. Source code for training MatFuse and supplemental materials are publicly available at https://gvecchio.com/matfuse.
CoGS: Controllable Gaussian Splatting
Authors: Joel Julin, Zoltán A. Milacski, Koichiro Niinuma, László A. Jeni Heng Yu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Capturing and re-animating the 3D structure of articulated objects present significant barriers. On one hand methods requiring extensively calibrated multi-view setups are prohibitively complex and resource-intensive limiting their practical applicability. On the other hand while single-camera Neural Radiance Fields (NeRFs) offer a more streamlined approach they have excessive training and rendering costs. 3D Gaussian Splatting would be a suitable alternative but for two reasons. Firstly existing methods for 3D dynamic Gaussians require synchronized multi-view cameras and secondly the lack of controllability in dynamic scenarios. We present CoGS a method for Controllable Gaussian Splatting that enables the direct manipulation of scene elements offering real-time control of dynamic scenes without the prerequisite of pre-computing control signals. We evaluated CoGS using both synthetic and real-world datasets that include dynamic objects that differ in degree of difficulty. In our evaluations CoGS consistently outperformed existing dynamic and controllable neural representations in terms of visual fidelity.
Partial-to-Partial Shape Matching with Geometric Consistency
Authors: Maolin Gao, Paul Roetzer, Marvin Eisenberger, Daniel Cremers, Florian Bernard Viktoria Ehm
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Finding correspondences between 3D shapes is an important and long-standing problem in computer vision graphics and beyond. A prominent challenge are partial-to-partial shape matching settings which occur when the shapes to match are only observed incompletely (e.g. from 3D scanning). Although partial-to-partial matching is a highly relevant setting in practice it is rarely explored. Our work bridges the gap between existing (rather artificial) 3D full shape matching and partial-to-partial real-world settings by exploiting geometric consistency as a strong constraint. We demonstrate that it is indeed possible to solve this challenging problem in a variety of settings. For the first time we achieve geometric consistency for partial-to-partial matching which is realized by a novel integer non-linear program formalism building on triangle product spaces along with a new pruning algorithm based on linear integer programming. Further we generate a new inter-class dataset for partial-to-partial shape-matching. We show that our method outperforms current SOTA methods on both an established intra-class dataset and our novel inter-class dataset.
Descriptor and Word Soups: Overcoming the Parameter Efficiency Accuracy Tradeoff for Out-of-Distribution Few-shot Learning
Authors: Theodoros Tsiligkaridis, Brian Kulis Christopher Liao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Over the past year a large body of multimodal research has emerged around zero-shot evaluation using GPT descriptors. These studies boost the zero-shot accuracy of pretrained VL models with an ensemble of label-specific text generated by GPT. A recent study WaffleCLIP demonstrated that similar zero-shot accuracy can be achieved with an ensemble of random descriptors. However both zero-shot methods are un-trainable and consequently sub-optimal when some few-shot out-of-distribution (OOD) training data is available. Inspired by these prior works we present two more flexible methods called descriptor and word soups which do not require an LLM at test time and can leverage training data to increase OOD target accuracy. Descriptor soup greedily selects a small set of textual descriptors using generic few-shot training data then calculates robust class embeddings using the selected descriptors. Word soup greedily assembles a chain of words in a similar manner. Compared to existing few-shot soft prompt tuning methods word soup requires fewer parameters by construction and less GPU memory since it does not require backpropagation. Both soups outperform current published few-shot methods even when combined with SoTA zero-shot methods on cross-dataset and domain generalization benchmarks. Compared with SoTA prompt and descriptor ensembling methods such as ProDA and WaffleCLIP word soup achieves higher OOD accuracy with fewer ensemble members. Please checkout our code: https://github.com/Chris210634/word_soups
Harnessing the Power of MLLMs for Transferable Text-to-Image Person ReID
Authors: Changxing Ding, Jiayu Jiang, Fei Wang, Yibing Zhan, Dapeng Tao Wentan Tan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Text-to-image person re-identification (ReID) retrieves pedestrian images according to textual descriptions. Manually annotating textual descriptions is time-consuming restricting the scale of existing datasets and therefore the generalization ability of ReID models. As a result we study the transferable text-to-image ReID problem where we train a model on our proposed large-scale database and directly deploy it to various datasets for evaluation. We obtain substantial training data via Multi-modal Large Language Models (MLLMs). Moreover we identify and address two key challenges in utilizing the obtained textual descriptions. First an MLLM tends to generate descriptions with similar structures causing the model to overfit specific sentence patterns. Thus we propose a novel method that uses MLLMs to caption images according to various templates. These templates are obtained using a multi-turn dialogue with a Large Language Model (LLM). Therefore we can build a large-scale dataset with diverse textual descriptions. Second an MLLM may produce incorrect descriptions. Hence we introduce a novel method that automatically identifies words in a description that do not correspond with the image. This method is based on the similarity between one text and all patch token embeddings in the image. Then we mask these words with a larger probability in the subsequent training epoch alleviating the impact of noisy textual descriptions. The experimental results demonstrate that our methods significantly boost the direct transfer text-to-image ReID performance. Benefiting from the pre-trained model weights we also achieve state-of-the-art performance in the traditional evaluation settings.
360+x: A Panoptic Multi-modal Scene Understanding Dataset
Authors: Yuqi Hou, Chenyuan Qu, Irene Testini, Xiaohan Hong, Jianbo Jiao Hao Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Human perception of the world is shaped by a multitude of viewpoints and modalities. While many existing datasets focus on scene understanding from a certain perspective (e.g. egocentric or third-person views) our dataset offers a panoptic perspective (i.e. multiple viewpoints with multiple data modalities). Specifically we encapsulate third-person panoramic and front views as well as egocentric monocular/binocular views with rich modalities including video multi-channel audio directional binaural delay location data and textual scene descriptions within each scene captured presenting comprehensive observation of the world. To the best of our knowledge this is the first database that covers multiple viewpoints with multiple data modalities to mimic how daily information is accessed in the real world. Through our benchmark analysis we presented 5 different scene understanding tasks on the proposed 360+x dataset to evaluate the impact and benefit of each data modality and perspective in panoptic scene understanding. We hope this unique dataset could broaden the scope of comprehensive scene understanding and encourage the community to approach these problems from more diverse perspectives.
Weakly Supervised Video Individual Counting
Authors: Guorong Li, Yuankai Qi, Ziheng Yan, Zhenjun Han, Anton van den Hengel, Ming-Hsuan Yang, Qingming Huang Xinyan Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Video Individual Counting (VIC) aims to predict the number of unique individuals in a single video. Existing methods learn representations based on trajectory labels for individuals which are annotation-expensive. To provide a more realistic reflection of the underlying practical challenge we introduce a weakly supervised VIC task wherein trajectory labels are not provided. Instead two types of labels are provided to indicate traffic entering the field of view (inflow) and leaving the field view (outflow). We also propose the first solution as a baseline that formulates the task as a weakly supervised contrastive learning problem under group-level matching. In doing so we devise an end-to-end trainable soft contrastive loss to drive the network to distinguish inflow outflow and the remaining. To facilitate future study in this direction we generate annotations from the existing VIC datasets SenseCrowd and CroHD and also build a new dataset UAVVIC. Extensive results show that our baseline weakly supervised method outperforms supervised methods and thus little information is lost in the transition to the more practically relevant weakly supervised task. The code and trained model can be found at CGNet.
Gaussian Shading: Provable Performance-Lossless Image Watermarking for Diffusion Models
Authors: Kai Zeng, Kejiang Chen, Han Fang, Weiming Zhang, Nenghai Yu Zijin Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Ethical concerns surrounding copyright protection and inappropriate content generation pose challenges for the practical implementation of diffusion models. One effective solution involves watermarking the generated images. However existing methods often compromise the model performance or require additional training which is undesirable for operators and users. To address this issue we propose Gaussian Shading a diffusion model watermarking technique that is both performance-lossless and training-free while serving the dual purpose of copyright protection and tracing of offending content. Our watermark embedding is free of model parameter modifications and thus is plug-and-play. We map the watermark to latent representations following a standard Gaussian distribution which is indistinguishable from latent representations obtained from the non-watermarked diffusion model. Therefore we can achieve watermark embedding with lossless performance for which we also provide theoretical proof. Furthermore since the watermark is intricately linked with image semantics it exhibits resilience to lossy processing and erasure attempts. The watermark can be extracted by Denoising Diffusion Implicit Models (DDIM) inversion and inverse sampling. We evaluate Gaussian Shading on multiple versions of Stable Diffusion and the results demonstrate that Gaussian Shading not only is performance-lossless but also outperforms existing methods in terms of robustness.
Generalized Event Cameras
Authors: Matthew Dutson, Andrei Ardelean, Claudio Bruschini, Edoardo Charbon, Mohit Gupta Varun Sundar
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Event cameras capture the world at high time resolution and with minimal bandwidth requirements. However event streams which only encode changes in brightness do not contain sufficient scene information to support a wide variety of downstream tasks. In this work we design generalized event cameras that inherently preserve scene intensity in a bandwidth-efficient manner. We generalize event cameras in terms of when an event is generated and what information is transmitted. To implement our designs we turn to single-photon sensors that provide digital access to individual photon detections; this modality gives us the flexibility to realize a rich space of generalized event cameras. Our single-photon event cameras are capable of high-speed high-fidelity imaging at low readout rates. Consequently these event cameras can support plug-and-play downstream inference without capturing new event datasets or designing specialized event-vision models. As a practical implication our designs which involve lightweight and near-sensor-compatible computations provide a way to use single-photon sensors without exorbitant bandwidth costs.
3D Neural Edge Reconstruction
Authors: Songyou Peng, Zehao Yu, Shaohui Liu, Rémi Pautrat, Xiaochuan Yin, Marc Pollefeys Lei Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Real-world objects and environments are predominantly composed of edge features including straight lines and curves. Such edges are crucial elements for various applications such as CAD modeling surface meshing lane mapping etc. However existing traditional methods only prioritize lines over curves for simplicity in geometric modeling. To this end we introduce EMAP a new method for learning 3D edge representations with a focus on both lines and curves. Our method implicitly encodes 3D edge distance and direction in Unsigned Distance Functions (UDF) from multi-view edge maps. On top of this neural representation we propose an edge extraction algorithm that robustly abstracts parametric 3D edges from the inferred edge points and their directions. Comprehensive evaluations demonstrate that our method achieves better 3D edge reconstruction on multiple challenging datasets. We further show that our learned UDF field enhances neural surface reconstruction by capturing more details.
DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks
Authors: Dezhi Peng, Chongyu Liu, Peirong Zhang, Lianwen Jin Jiaxin Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Document image restoration is a crucial aspect of Document AI systems as the quality of document images significantly influences the overall performance. Prevailing methods address distinct restoration tasks independently leading to intricate systems and the incapability to harness the potential synergies of multi-task learning. To overcome this challenge we propose DocRes a generalist model that unifies five document image restoration tasks including dewarping deshadowing appearance enhancement deblurring and binarization. To instruct DocRes to perform various restoration tasks we propose a novel visual prompt approach called Dynamic Task-Specific Prompt (DTSPrompt). The DTSPrompt for different tasks comprises distinct prior features which are additional characteristics extracted from the input image. Beyond its role as a cue for task-specific execution DTSPrompt can also serve as supplementary information to enhance the model's performance. Moreover DTSPrompt is more flexible than prior visual prompt approaches as it can be seamlessly applied and adapted to inputs with high and variable resolutions. Experimental results demonstrate that DocRes achieves competitive or superior performance compared to existing state-of-the-art task-specific models. This underscores the potential of DocRes across a broader spectrum of document image restoration tasks. The source code is publicly available at https://github.com/ZZZHANG-jx/DocRes.
Honeybee: Locality-enhanced Projector for Multimodal LLM
Authors: Wooyoung Kang, Jonghwan Mun, Byungseok Roh Junbum Cha
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In Multimodal Large Language Models (MLLMs) a visual projector plays a crucial role in bridging pre-trained vision encoders with LLMs enabling profound visual understanding while harnessing the LLMs' robust capabilities. Despite the importance of the visual projector it has been relatively less explored. In this study we first identify two essential projector properties: (i) flexibility in managing the number of visual tokens crucial for MLLMs' overall efficiency and (ii) preservation of local context from visual features vital for spatial understanding. Based on these findings we propose a novel projector design that is both flexible and locality-enhanced effectively satisfying the two desirable properties. Additionally we present comprehensive strategies to effectively utilize multiple and multifaceted instruction datasets. Through extensive experiments we examine the impact of individual design choices. Finally our proposed MLLM Honeybee remarkably outperforms previous state-of-the-art methods across various benchmarks including MME MMBench SEED-Bench and LLaVA-Bench achieving significantly higher efficiency. Code and models are available at https://github.com/kakaobrain/honeybee.
Learned Trajectory Embedding for Subspace Clustering
Authors: Carl Olsson, Christopher Zach Yaroslava Lochman
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Clustering multiple motions from observed point trajectories is a fundamental task in understanding dynamic scenes. Most motion models require multiple tracks to estimate their parameters hence identifying clusters when multiple motions are observed is a very challenging task. This is even aggravated for high-dimensional motion models. The starting point of our work is that this high-dimensionality of motion model can actually be leveraged to our advantage as sufficiently long trajectories identify the underlying motion uniquely in practice. Consequently we propose to learn a mapping from trajectories to embedding vectors that represent the generating motion. The obtained trajectory embeddings are useful for clustering multiple observed motions but are also trained to contain sufficient information to recover the parameters of the underlying motion by utilizing a geometric loss. We therefore are able to use only weak supervision from given motion segmentation to train this mapping. The entire algorithm consisting of trajectory embedding clustering and motion parameter estimation is highly efficient. We conduct experiments on the Hopkins155 Hopkins12 and KT3DMoSeg datasets and show state-of-the-art performance of our proposed method for trajectory-based motion segmentation on full sequences and its competitiveness on the occluded sequences. Project page: https://ylochman.github.io/trajectory-embedding.
Training Vision Transformers for Semi-Supervised Semantic Segmentation
Authors: Li Jiang, Bernt Schiele Xinting Hu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We present S4Former a novel approach to training Vision Transformers for Semi-Supervised Semantic Segmentation (S4). At its core S4Former employs a Vision Transformer within a classic teacher-student framework and then leverages three novel technical ingredients: PatchShuffle as a parameter-free perturbation technique Patch-Adaptive Self-Attention (PASA) as a fine-grained feature modulation method and the innovative Negative Class Ranking (NCR) regularization loss. Based on these regularization modules aligned with Transformer-specific characteristics across the image input feature and output dimensions S4Former exploits the Transformer's ability to capture and differentiate consistent global contextual information in unlabeled images. Overall S4Former not only defines a new state of the art in S4 but also maintains a streamlined and scalable architecture. Being readily compatible with existing frameworks S4Former achieves strong improvements (up to 4.9%) on benchmarks like Pascal VOC 2012 COCO and Cityscapes with varying numbers of labeled data. The code is at https://github.com/JoyHuYY1412/S4Former.
HarmonyView: Harmonizing Consistency and Diversity in One-Image-to-3D
Authors: Byeongjun Park, Hyojun Go, Jin-Young Kim, Changick Kim Sangmin Woo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recent progress in single-image 3D generation highlights the importance of multi-view coherency leveraging 3D priors from large-scale diffusion models pretrained on Internet-scale images. However the aspect of novel-view diversity remains underexplored within the research landscape due to the ambiguity in converting a 2D image into 3D content where numerous potential shapes can emerge. Here we aim to address this research gap by simultaneously addressing both consistency and diversity. Yet striking a balance between these two aspects poses a considerable challenge due to their inherent trade-offs. This work introduces HarmonyView a simple yet effective diffusion sampling technique adept at decomposing two intricate aspects in single-image 3D generation: consistency and diversity. This approach paves the way for a more nuanced exploration of the two critical dimensions within the sampling process. Moreover we propose a new evaluation metric based on CLIP image and text encoders to comprehensively assess the diversity of the generated views which closely aligns with human evaluators' judgments. In experiments HarmonyView achieves a harmonious balance demonstrating a win-win scenario in both consistency and diversity.
DGC-GNN: Leveraging Geometry and Color Cues for Visual Descriptor-Free 2D-3D Matching
Authors: Juho Kannala, Daniel Barath Shuzhe Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Matching 2D keypoints in an image to a sparse 3D point cloud of the scene without requiring visual descriptors has garnered increased interest due to its low memory requirements inherent privacy preservation and reduced need for expensive 3D model maintenance compared to visual descriptor-based methods. However existing algorithms often compromise on performance resulting in a significant deterioration compared to their descriptor-based counterparts. In this paper we introduce DGC-GNN a novel algorithm that employs a global-to-local Graph Neural Network (GNN) that progressively exploits geometric and color cues to rep- resent keypoints thereby improving matching accuracy. Our procedure encodes both Euclidean and angular relations at a coarse level forming the geometric embedding to guide the point matching. We evaluate DGC-GNN on both indoor and outdoor datasets demonstrating that it not only doubles the accuracy of the state-of-the-art visual descriptor-free algorithm but also substantially narrows the performance gap between descriptor-based and descriptor free methods.
CuVLER: Enhanced Unsupervised Object Discoveries through Exhaustive Self-Supervised Transformers
Authors: Or Rubin, Sapir Gershov, Shlomi Laufer Shahaf Arica
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In this paper we introduce VoteCut an innovative method for unsupervised object discovery that leverages feature representations from multiple self-supervised models. VoteCut employs normalized-cut based graph partitioning clustering and a pixel voting approach. Additionally We present CuVLER (Cut-Vote-and-LEaRn) a zero-shot model trained using pseudo-labels generated by VoteCut and a novel soft target loss to refine segmentation accuracy. Through rigorous evaluations across multiple datasets and several unsupervised setups our methods demonstrate significant improvements in comparison to previous state-of-the-art models. Our ablation studies further highlight the contributions of each component revealing the robustness and efficacy of our approach. Collectively VoteCut and CuVLER pave the way for future advancements in image segmentation.
Quantifying Task Priority for Multi-Task Optimization
Authors: Kuk-Jin Yoon Wooseong Jeong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The goal of multi-task learning is to learn diverse tasks within a single unified network. As each task has its own unique objective function conflicts emerge during training resulting in negative transfer among them. Earlier research identified these conflicting gradients in shared parameters between tasks and attempted to realign them in the same direction. However we prove that such optimization strategies lead to sub-optimal Pareto solutions due to their inability to accurately determine the individual contributions of each parameter across various tasks. In this paper we propose the concept of task priority to evaluate parameter contributions across different tasks. To learn task priority we identify the type of connections related to links between parameters influenced by task-specific losses during backpropagation. The strength of connections is gauged by the magnitude of parameters to determine task priority. Based on these we present a new method named connection strength-based optimization for multi-task learning which consists of two phases. The first phase learns the task priority within the network while the second phase modifies the gradients while upholding this priority. This ultimately leads to finding new Pareto optimal solutions for multiple tasks. Through extensive experiments we show that our approach greatly enhances multi-task performance in comparison to earlier gradient manipulation methods.
UnSAMFlow: Unsupervised Optical Flow Guided by Segment Anything Model
Authors: Lei Luo, Zhuo Hui, Can Pu, Xiaoyu Xiang, Rakesh Ranjan, Denis Demandolx Shuai Yuan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Traditional unsupervised optical flow methods are vulnerable to occlusions and motion boundaries due to lack of object-level information. Therefore we propose UnSAMFlow an unsupervised flow network that also leverages object information from the latest foundation model Segment Anything Model (SAM). We first include a self-supervised semantic augmentation module tailored to SAM masks. We also analyze the poor gradient landscapes of traditional smoothness losses and propose a new smoothness definition based on homography instead. A simple yet effective mask feature module has also been added to further aggregate features on the object level. With all these adaptations our method produces clear optical flow estimation with sharp boundaries around objects which outperforms state-of-the-art methods on both KITTI and Sintel datasets. Our method also generalizes well across domains and runs very efficiently.
Exploiting Inter-sample and Inter-feature Relations in Dataset Distillation
Authors: Wenbin Li, Tianyu Ding, Lei Wang, Hongguang Zhang, Kuihua Huang, Jing Huo, Yang Gao Wenxiao Deng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Dataset distillation has emerged as a promising approach in deep learning enabling efficient training with small synthetic datasets derived from larger real ones. Particularly distribution matching-based distillation methods attract attention thanks to its effectiveness and low computational cost. However these methods face two primary limitations: the dispersed feature distribution within the same class in synthetic datasets reducing class discrimination and an exclusive focus on mean feature consistency lacking precision and comprehensiveness. To address these challenges we introduce two novel constraints: a class centralization constraint and a covariance matching constraint. The class centralization constraint aims to enhance class discrimination by more closely clustering samples within classes. The covariance matching constraint seeks to achieve more accurate feature distribution matching between real and synthetic datasets through local feature covariance matrices particularly beneficial when sample sizes are much smaller than the number of features. Experiments demonstrate notable improvements with these constraints yielding performance boosts of up to 6.6% on CIFAR10 2.9% on SVHN 2.5% on CIFAR100 and 2.5% on TinyImageNet compared to the state-of-the-art relevant methods. In addition our method maintains robust performance in cross-architecture settings with a maximum performance drop of 1.7% on four architectures. Code is available at https://github.com/VincenDen/IID.
On the Scalability of Diffusion-based Text-to-Image Generation
Authors: Yang Zou, Ying Wang, Orchid Majumder, Yusheng Xie, R. Manmatha, Ashwin Swaminathan, Zhuowen Tu, Stefano Ermon, Stefano Soatto Hao Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Scaling up model and data size has been quite successful for the evolution of LLMs. However the scaling law for the diffusion based text-to-image (T2I) models is not fully explored. It is also unclear how to efficiently scale the model for better performance at reduced cost. The different training settings and expensive training cost make a fair model comparison extremely difficult. In this work we empirically study the scaling properties of diffusion based T2I models by performing extensive and rigours ablations on scaling both denoising backbones and training set including training scaled UNet and Transformer variants ranging from 0.4B to 4B parameters on datasets upto 600M images. For model scaling we find the location and amount of cross attention distinguishes the performance of existing UNet designs. And increasing the transformer blocks is more parameter-efficient for improving text-image alignment than increasing channel numbers. We then identify an efficient UNet variant which is 45% smaller and 28% faster than SDXL's UNet. On the data scaling side we show the quality and diversity of the training set matters more than simply dataset size. Increasing caption density and diversity improves text-image alignment performance and the learning efficiency. Finally we provide scaling functions to predict the text-image alignment performance as functions of the scale of model size compute and dataset size.