GenAI4ELab/papercli-papers huggingface dataset:
Summarize the Past to Predict the Future: Natural Language Descriptions of Context Boost Multimodal Object Interaction Anticipation
Authors: Razvan-George Pasca; Alexey Gavryushin; Muhammad Hamza; Yen-Ling Kuo; Kaichun Mo; Luc Van Gool; Otmar Hilliges; Xi Wang
Venue: CVPR 2024 (main)

Abstract: We study object interaction anticipation in egocentric videos. This task requires an understanding of the spatio-temporal context formed by past actions on objects coined "action context". We propose TransFusion a multimodal transformer-based architecture for short-term object interaction anticipation. Our method exploits the representational power of language by summarizing the action context textually after leveraging pre-trained vision-language foundation models to extract the action context from past video frames. The summarized action context and the last observed video frame are processed by the multimodal fusion module to forecast the next object interaction. Experiments on the Ego4D next active object interaction dataset show the effectiveness of our multimodal fusion model and highlight the benefits of using the power of foundation models and language-based context summaries in a task where vision may appear to suffice. Our novel approach outperforms all state-of-the-art methods on both versions of the Ego4D dataset.
Transfer CLIP for Generalizable Image Denoising
Authors: Jun Cheng; Dong Liang; Shan Tan
Venue: CVPR 2024 (main)

Abstract: Image denoising is a fundamental task in computer vision. While prevailing deep learning-based supervised and self-supervised methods have excelled in eliminating in-distribution noise their susceptibility to out-of-distribution (OOD) noise remains a significant challenge. The recent emergence of contrastive language-image pre-training (CLIP) model has showcased exceptional capabilities in open-world image recognition and segmentation. Yet the potential for leveraging CLIP to enhance the robustness of low-level tasks remains largely unexplored. This paper uncovers that certain dense features extracted from the frozen ResNet image encoder of CLIP exhibit distortion-invariant and content-related properties which are highly desirable for generalizable denoising. Leveraging these properties we devise an asymmetrical encoder-decoder denoising network which incorporates dense features including the noisy image and its multi-scale features from the frozen ResNet encoder of CLIP into a learnable image decoder to achieve generalizable denoising. The progressive feature augmentation strategy is further proposed to mitigate feature overfitting and improve the robustness of the learnable decoder. Extensive experiments and comparisons conducted across diverse OOD noises including synthetic noise real-world sRGB noise and low-dose CT image noise demonstrate the superior generalization ability of our method.
Smooth Diffusion: Crafting Smooth Latent Spaces in Diffusion Models
Authors: Jiayi Guo; Xingqian Xu; Yifan Pu; Zanlin Ni; Chaofei Wang; Manushree Vasu; Shiji Song; Gao Huang; Humphrey Shi
Venue: CVPR 2024 (main)

Abstract: Recently diffusion models have made remarkable progress in text-to-image (T2I) generation synthesizing images with high fidelity and diverse contents. Despite this advancement latent space smoothness within diffusion models remains largely unexplored. Smooth latent spaces ensure that a perturbation on an input latent corresponds to a steady change in the output image. This property proves beneficial in downstream tasks including image interpolation inversion and editing. In this work we expose the non-smoothness of diffusion latent spaces by observing noticeable visual fluctuations resulting from minor latent variations. To tackle this issue we propose Smooth Diffusion a new category of diffusion models that can be simultaneously high-performing and smooth. Specifically we introduce Step-wise Variation Regularization to enforce the proportion between the variations of an arbitrary input latent and that of the output image is a constant at any diffusion training step. In addition we devise an interpolation standard deviation (ISTD) metric to effectively assess the latent space smoothness of a diffusion model. Extensive quantitative and qualitative experiments demonstrate that Smooth Diffusion stands out as a more desirable solution not only in T2I generation but also across various downstream tasks. Smooth Diffusion is implemented as a plug-and-play Smooth-LoRA to work with various community models. Code is available at https://github.com/SHI-Labs/Smooth-Diffusion.
Towards CLIP-driven Language-free 3D Visual Grounding via 2D-3D Relational Enhancement and Consistency
Authors: Yuqi Zhang; Han Luo; Yinjie Lei
Venue: CVPR 2024 (main)

Abstract: 3D visual grounding plays a crucial role in scene understanding with extensive applications in AR/VR. Despite the significant progress made in recent methods the requirement of dense textual descriptions for each individual object which is time-consuming and costly hinders their scalability. To mitigate reliance on text annotations during training researchers have explored language-free training paradigms in the 2D field via explicit text generation or implicit feature substitution. Nevertheless unlike 2D images the complexity of spatial relations in 3D coupled with the absence of robust 3D visual language pre-trained models makes it challenging to directly transfer previous strategies. To tackle the above issues in this paper we introduce a language-free training framework for 3D visual grounding. By utilizing the visual-language joint embedding in 2D large cross-modality model as a bridge we can expediently produce the pseudo-language features by leveraging the features of 2D images which are equivalent to that of real textual descriptions. We further develop a relation injection scheme with a Neighboring Relation-aware Modeling module and a Cross-modality Relation Consistency module aiming to enhance and preserve the complex relationships between the 2D and 3D embedding space. Extensive experiments demonstrate that our proposed language-free 3D visual grounding approach can obtain promising performance across three widely used datasets --ScanRefer Nr3D and Sr3D. Our codes are available at https://github.com/xibi777/3DLFVG
Optimal Transport Aggregation for Visual Place Recognition
Authors: Sergio Izquierdo; Javier Civera
Venue: CVPR 2024 (main)

Abstract: The task of Visual Place Recognition (VPR) aims to match a query image against references from an extensive database of images from different places relying solely on visual cues. State-of-the-art pipelines focus on the aggregation of features extracted from a deep backbone in order to form a global descriptor for each image. In this context we introduce SALAD (Sinkhorn Algorithm for Locally Aggregated Descriptors) which reformulates NetVLAD's soft-assignment of local features to clusters as an optimal transport problem. In SALAD we consider both feature-to-cluster and cluster-to-feature relations and we also introduce a dustbin cluster designed to selectively discard features deemed non-informative enhancing the overall descriptor quality. Additionally we leverage and fine-tune DINOv2 as a backbone which provides enhanced description power for the local features and dramatically reduces the required training time. As a result our single-stage method not only surpasses single-stage baselines in public VPR datasets but also surpasses two-stage methods that add a re-ranking with significantly higher cost.
FlowIE: Efficient Image Enhancement via Rectified Flow
Authors: Yixuan Zhu; Wenliang Zhao; Ao Li; Yansong Tang; Jie Zhou; Jiwen Lu
Venue: CVPR 2024 (main)

Abstract: Image enhancement holds extensive applications in real-world scenarios due to complex environments and limitations of imaging devices. Conventional methods are often constrained by their tailored models resulting in diminished robustness when confronted with challenging degradation conditions. In response we propose FlowIE a simple yet highly effective flow-based image enhancement framework that estimates straight-line paths from an elementary distribution to high-quality images. Unlike previous diffusion-based methods that suffer from long-time inference FlowIE constructs a linear many-to-one transport mapping via conditioned rectified flow. The rectification straightens the trajectories of probability transfer accelerating inference by an order of magnitude. This design enables our FlowIE to fully exploit rich knowledge in the pre-trained diffusion model rendering it well-suited for various real-world applications. Moreover we devise a faster inference algorithm inspired by Lagrange's Mean Value Theorem harnessing midpoint tangent direction to optimize path estimation ultimately yielding visually superior results. Thanks to these designs our FlowIE adeptly manages a diverse range of enhancement tasks within a concise sequence of fewer than 5 steps. Our contributions are rigorously validated through comprehensive experiments on synthetic and real-world datasets unveiling the compelling efficacy and efficiency of our proposed FlowIE.
Aligning and Prompting Everything All at Once for Universal Visual Perception
Authors: Yunhang Shen; Chaoyou Fu; Peixian Chen; Mengdan Zhang; Ke Li; Xing Sun; Yunsheng Wu; Shaohui Lin; Rongrong Ji
Venue: CVPR 2024 (main)

Abstract: Vision foundation models have been explored recently to build general-purpose vision systems. However predominant paradigms driven by casting instance-level tasks as an object-word alignment bring heavy cross-modality interaction which is not effective in prompting object detection and visual grounding. Another line of work that focuses on pixel-level tasks often encounters a large annotation gap of things and stuff and suffers from mutual interference between foreground-object and background-class segmentation. In stark contrast to the prevailing methods we present APE a universal visual perception model for aligning and prompting everything all at once in an image to perform diverse tasks i.e. detection segmentation and grounding as an instance-level sentence-object matching paradigm. Specifically APE advances the convergence of detection and grounding by reformulating language-guided grounding as open-vocabulary detection which efficiently scales up model prompting to thousands of category vocabularies and region descriptions while maintaining the effectiveness of cross-modality fusion. To bridge the granularity gap of different pixel-level tasks APE equalizes semantic and panoptic segmentation to proxy instance learning by considering any isolated regions as individual instances. APE aligns vision and language representation on broad data with natural and challenging characteristics all at once without task-specific fine-tuning. The extensive experiments on over 160 datasets demonstrate that with only one-suit of weights APE outperforms (or is on par with) the state-of-the-art models proving that an effective yet universal perception for anything aligning and prompting is indeed feasible. Codes and trained models are released at https://github.com/shenyunhang/APE.
Correlation-Decoupled Knowledge Distillation for Multimodal Sentiment Analysis with Incomplete Modalities
Authors: Mingcheng Li; Dingkang Yang; Xiao Zhao; Shuaibing Wang; Yan Wang; Kun Yang; Mingyang Sun; Dongliang Kou; Ziyun Qian; Lihua Zhang
Venue: CVPR 2024 (main)

Abstract: Multimodal sentiment analysis (MSA) aims to understand human sentiment through multimodal data. Most MSA efforts are based on the assumption of modality completeness. However in real-world applications some practical factors cause uncertain modality missingness which drastically degrades the model's performance. To this end we propose a Correlation-decoupled Knowledge Distillation (CorrKD) framework for the MSA task under uncertain missing modalities. Specifically we present a sample-level contrastive distillation mechanism that transfers comprehensive knowledge containing cross-sample correlations to reconstruct missing semantics. Moreover a category-guided prototype distillation mechanism is introduced to capture cross-category correlations using category prototypes to align feature distributions and generate favorable joint representations. Eventually we design a response-disentangled consistency distillation strategy to optimize the sentiment decision boundaries of the student network through response disentanglement and mutual information maximization. Comprehensive experiments on three datasets indicate that our framework can achieve favorable improvements compared with several baselines.
Revisiting Adversarial Training at Scale
Authors: Zeyu Wang; Xianhang Li; Hongru Zhu; Cihang Xie
Venue: CVPR 2024 (main)

Abstract: The machine learning community has witnessed a drastic change in the training pipeline pivoted by those "foundation models" with unprecedented scales. However the field of adversarial training is lagging behind predominantly centered around small model sizes like ResNet-50 and tiny and low-resolution datasets like CIFAR-10. To bridge this transformation gap this paper provides a modern re-examination with adversarial training investigating its potential benefits when applied at scale. Additionally we introduce an efficient and effective training strategy to enable adversarial training with giant models and web-scale data at an affordable computing cost. We denote this newly introduced framework as AdvXL. Empirical results demonstrate that AdvXL establishes new state-of-the-art robust accuracy records under AutoAttack on ImageNet-1K. For example by training on DataComp-1B dataset our AdvXL empowers a vanilla ViT-g model to substantially surpass the previous records of l_ infinity - l_ 2 - and l_ 1 -robust accuracy by margins of 11.4% 14.2% and 12.9% respectively. This achievement posits AdvXL as a pioneering approach charting a new trajectory for the efficient training of robust visual representations at significantly larger scales. Our code is available at https://github.com/UCSC-VLAA/AdvXL.
Towards Fairness-Aware Adversarial Learning
Authors: Yanghao Zhang; Tianle Zhang; Ronghui Mu; Xiaowei Huang; Wenjie Ruan
Venue: CVPR 2024 (main)

Abstract: Although adversarial training (AT) has proven effective in enhancing the model's robustness the recently revealed issue of fairness in robustness has not been well addressed i.e. the robust accuracy varies significantly among different categories. In this paper instead of uniformly evaluating the model's average class performance we delve into the issue of robust fairness by considering the worst-case distribution across various classes. We propose a novel learning paradigm named Fairness-Aware Adversarial Learning (FAAL). As a generalization of conventional AT we re-define the problem of adversarial training as a min-max-max framework to ensure both robustness and fairness of the trained model. Specifically by taking advantage of distributional robust optimization our method aims to find the worst distribution among different categories and the solution is guaranteed to obtain the upper bound performance with high probability. In particular FAAL can fine-tune an unfair robust model to be fair within only two epochs without compromising the overall clean and robust accuracies. Extensive experiments on various image datasets validate the superior performance and efficiency of the proposed FAAL compared to other state-of-the-art methods.
LoSh: Long-Short Text Joint Prediction Network for Referring Video Object Segmentation
Authors: Linfeng Yuan; Miaojing Shi; Zijie Yue; Qijun Chen
Venue: CVPR 2024 (main)

Abstract: Referring video object segmentation (RVOS) aims to segment the target instance referred by a given text expression in a video clip. The text expression normally contains sophisticated description of the instance's appearance action and relation with others. It is therefore rather difficult for a RVOS model to capture all these attributes correspondingly in the video; in fact the model often favours more on the action- and relation-related visual attributes of the instance. This can end up with partial or even incorrect mask prediction of the target instance. We tackle this problem by taking a subject-centric short text expression from the original long text expression. The short one retains only the appearance-related information of the target instance so that we can use it to focus the model's attention on the instance's appearance. We let the model make joint predictions using both long and short text expressions; and insert a long-short cross-attention module to interact the joint features and a long-short predictions intersection loss to regulate the joint predictions. Besides the improvement on the linguistic part we also introduce a forward-backward visual consistency loss which utilizes optical flows to warp visual features between the annotated frames and their temporal neighbors for consistency. We build our method on top of two state of the art pipelines. Extensive experiments on A2D-Sentences Refer-YouTube-VOS JHMDB-Sentences and Refer-DAVIS17 show impressive improvements of our method. Code is available here.
MirageRoom: 3D Scene Segmentation with 2D Pre-trained Models by Mirage Projection
Authors: Haowen Sun; Yueqi Duan; Juncheng Yan; Yifan Liu; Jiwen Lu
Venue: CVPR 2024 (main)

Abstract: Nowadays leveraging 2D images and pre-trained models to guide 3D point cloud feature representation has shown a remarkable potential to boost the performance of 3D fundamental models. While some works rely on additional data such as 2D real-world images and their corresponding camera poses recent studies target at using point cloud exclusively by designing 3D-to-2D projection. However in the indoor scene scenario existing 3D-to-2D projection strategies suffer from severe occlusions and incoherence which fail to contain sufficient information for fine-grained point cloud segmentation task. In this paper we argue that the crux of the matter resides in the basic premise of existing projection strategies that the medium is homogeneous thereby projection rays propagate along straight lines and behind objects are occluded by front ones. Inspired by the phenomenon of mirage where the occluded objects are exposed by distorted light rays due to heterogeneous medium refraction rate we propose MirageRoom by designing parametric mirage projection with heterogeneous medium to obtain series of projected images with various distorted degrees. We further develop a masked reprojection module across 2D and 3D latent space to bridge the gap between pre-trained 2D backbone and 3D point-wise features. Both quantitative and qualitative experimental results on S3DIS and ScanNet V2 demonstrate the effectiveness of our method.
In2SET: Intra-Inter Similarity Exploiting Transformer for Dual-Camera Compressive Hyperspectral Imaging
Authors: Xin Wang; Lizhi Wang; Xiangtian Ma; Maoqing Zhang; Lin Zhu; Hua Huang
Venue: CVPR 2024 (main)

Abstract: Dual-camera compressive hyperspectral imaging (DCCHI) offers the capability to reconstruct 3D hyperspectral image (HSI) by fusing compressive and panchromatic (PAN) image which has shown great potential for snapshot hyperspectral imaging in practice. In this paper we introduce a novel DCCHI reconstruction network intra-inter similarity exploiting Transformer (In2SET). Our key insight is to make full use of the PAN image to assist the reconstruction. To this end we propose to use the intra-similarity within the PAN image as a proxy for approximating the intra-similarity in the original HSI thereby offering an enhanced content prior for more accurate HSI reconstruction. Furthermore we propose to use the inter-similarity to align the features between HSI and PAN images thereby maintaining semantic consistency between the two modalities during the reconstruction process. By integrating In2SET into a PAN-guided deep unrolling (PGDU) framework our method substantially enhances the spatial-spectral fidelity and detail of the reconstructed images providing a more comprehensive and accurate depiction of the scene. Experiments conducted on both real and simulated datasets demonstrate that our approach consistently outperforms existing state-of-the-art methods in terms of reconstruction quality and computational complexity. The code is available at https://github.com/2JONAS/In2SET.
Dual Prototype Attention for Unsupervised Video Object Segmentation
Authors: Suhwan Cho; Minhyeok Lee; Seunghoon Lee; Dogyoon Lee; Heeseung Choi; Ig-Jae Kim; Sangyoun Lee
Venue: CVPR 2024 (main)

Abstract: Unsupervised video object segmentation (VOS) aims to detect and segment the most salient object in videos. The primary techniques used in unsupervised VOS are 1) the collaboration of appearance and motion information; and 2) temporal fusion between different frames. This paper proposes two novel prototype-based attention mechanisms inter-modality attention (IMA) and inter-frame attention (IFA) to incorporate these techniques via dense propagation across different modalities and frames. IMA densely integrates context information from different modalities based on a mutual refinement. IFA injects global context of a video to the query frame enabling a full utilization of useful properties from multiple frames. Experimental results on public benchmark datasets demonstrate that our proposed approach outperforms all existing methods by a substantial margin. The proposed two components are also thoroughly validated via ablative study.
Look-Up Table Compression for Efficient Image Restoration
Authors: Yinglong Li; Jiacheng Li; Zhiwei Xiong
Venue: CVPR 2024 (main)

Abstract: Look-Up Table (LUT) has recently gained increasing attention for restoring High-Quality (HQ) images from Low-Quality (LQ) observations thanks to its high computational efficiency achieved through a "space for time" strategy of caching learned LQ-HQ pairs. However incorporating multiple LUTs for improved performance comes at the cost of a rapidly growing storage size which is ultimately restricted by the allocatable on-device cache size. In this work we propose a novel LUT compression framework to achieve a better trade-off between storage size and performance for LUT-based image restoration models. Based on the observation that most cached LQ image patches are distributed along the diagonal of a LUT we devise a Diagonal-First Compression (DFC) framework where diagonal LQ-HQ pairs are preserved and carefully re-indexed to maintain the representation capacity while non-diagonal pairs are aggressively subsampled to save storage. Extensive experiments on representative image restoration tasks demonstrate that our DFC framework significantly reduces the storage size of LUT-based models (including our new design) while maintaining their performance. For instance DFC saves up to 90% of storage at a negligible performance drop for x4 super-resolution. The source code is available on GitHub: https://github.com/leenas233/DFC.