GenAI4ELab/papercli-papers huggingface dataset:
MMA-Diffusion: MultiModal Attack on Diffusion Models
Authors: Yijun Yang; Ruiyuan Gao; Xiaosen Wang; Tsung-Yi Ho; Nan Xu; Qiang Xu
Venue: CVPR 2024 (main)

Abstract: In recent years Text-to-Image (T2I) models have seen remarkable advancements gaining widespread adoption. However this progress has inadvertently opened avenues for potential misuse particularly in generating inappropriate or Not-Safe-For-Work (NSFW) content. Our work introduces MMA-Diffusion a framework that presents a significant and realistic threat to the security of T2I models by effectively circumventing current defensive measures in both open-source models and commercial online services. Unlike previous approaches MMA-Diffusion leverages both textual and visual modalities to bypass safeguards like prompt filters and post-hoc safety checkers thus exposing and highlighting the vulnerabilities in existing defense mechanisms. Our codes are available at https://github.com/cure-lab/MMA-Diffusion.
Benchmarking Audio Visual Segmentation for Long-Untrimmed Videos
Authors: Chen Liu; Peike Patrick Li; Qingtao Yu; Hongwei Sheng; Dadong Wang; Lincheng Li; Xin Yu
Venue: CVPR 2024 (main)

Abstract: Existing audio-visual segmentation datasets typically focus on short-trimmed videos with only one pixel-map annotation for a per-second video clip. In contrast for untrimmed videos the sound duration start- and end-sounding time positions and visual deformation of audible objects vary significantly. Therefore we observed that current AVS models trained on trimmed videos might struggle to segment sounding objects in long videos. To investigate the feasibility of grounding audible objects in videos along both temporal and spatial dimensions we introduce the Long-Untrimmed Audio-Visual Segmentation dataset (LU-AVS) which includes precise frame-level annotations of sounding emission times and provides exhaustive mask annotations for all frames. Considering that pixel-level annotations are difficult to achieve in some complex scenes we also provide the bounding boxes to indicate the sounding regions. Specifically LU-AVS contains 10M mask annotations across 6.6K videos and 11M bounding box annotations across 7K videos. Compared with the existing datasets LU-AVS videos are on average 4 8 times longer with the silent duration being 3 15 times greater. Furthermore we try our best to adapt some baseline models that were originally designed for audio-visual-relevant tasks to examine the challenges of our newly curated LU-AVS. Through comprehensive evaluation we demonstrate the challenges of LU-AVS compared to the ones containing trimmed videos. Therefore LU-AVS provides an ideal yet challenging platform for evaluating audio-visual segmentation and localization on untrimmed long videos. The dataset is publicly available at: https://yenanliu.github.io/LU-AVS/.
EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation
Authors: Md Mostafijur Rahman; Mustafa Munir; Radu Marculescu
Venue: CVPR 2024 (main)

Abstract: An efficient and effective decoding mechanism is crucial in medical image segmentation especially in scenarios with limited computational resources. However these decoding mechanisms usually come with high computational costs. To address this concern we introduce EMCAD a new efficient multi-scale convolutional attention decoder designed to optimize both performance and computational efficiency. EMCAD leverages a unique multi-scale depth-wise convolution block significantly enhancing feature maps through multi-scale convolutions. EMCAD also employs channel spatial and grouped (large-kernel) gated attention mechanisms which are highly effective at capturing intricate spatial relationships while focusing on salient regions. By employing group and depth-wise convolution EMCAD is very efficient and scales well (e.g. only 1.91M parameters and 0.381G FLOPs are needed when using a standard encoder). Our rigorous evaluations across 12 datasets that belong to six medical image segmentation tasks reveal that EMCAD achieves state-of-the-art (SOTA) performance with 79.4% and 80.3% reduction in #Params and #FLOPs respectively. Moreover EMCAD's adaptability to different encoders and versatility across segmentation tasks further establish EMCAD as a promising tool advancing the field towards more efficient and accurate medical image analysis. Our implementation is available at https://github.com/SLDGroup/EMCAD.
VTQA: Visual Text Question Answering via Entity Alignment and Cross-Media Reasoning
Authors: Kang Chen; Xiangqian Wu
Venue: CVPR 2024 (main)

Abstract: Achieving the optimal form of Visual Question Answering mandates a profound grasp of understanding grounding and reasoning within the intersecting domains of vision and language. Traditional VQA benchmarks have predominantly focused on simplistic tasks such as counting visual attributes and object detection which do not necessitate intricate cross-modal information understanding and inference. Motivated by the need for a more comprehensive evaluation we introduce a novel dataset comprising 23781 questions derived from 10124 image-text pairs. Specifically the task of this dataset requires the model to align multimedia representations of the same entity to implement multi-hop reasoning between image and text and finally use natural language to answer the question. Furthermore we evaluate this VTQA dataset comparing the performance of both state-of-the-art VQA models and our proposed baseline model the Key Entity Cross-Media Reasoning Network (KECMRN). The VTQA task poses formidable challenges for traditional VQA models underscoring its intrinsic complexity. Conversely KECMRN exhibits a modest improvement signifying its potential in multimedia entity alignment and multi-step reasoning. Our analysis underscores the diversity difficulty and scale of the VTQA task compared to previous multimodal QA datasets. In conclusion we anticipate that this dataset will serve as a pivotal resource for advancing and evaluating models proficient in multimedia entity alignment multi-step reasoning and open-ended answer generation. Our dataset and code is available at https://visual-text-qa.github.io/.
QN-Mixer: A Quasi-Newton MLP-Mixer Model for Sparse-View CT Reconstruction
Authors: Ishak Ayad; Nicolas Larue; Mai K. Nguyen
Venue: CVPR 2024 (main)

Abstract: Inverse problems span across diverse fields. In medical contexts computed tomography (CT) plays a crucial role in reconstructing a patient's internal structure presenting challenges due to artifacts caused by inherently ill-posed inverse problems. Previous research advanced image quality via post-processing and deep unrolling algorithms but faces challenges such as extended convergence times with ultra-sparse data. Despite enhancements resulting images often show significant artifacts limiting their effectiveness for real-world diagnostic applications. We aim to explore deep second-order unrolling algorithms for solving imaging inverse problems emphasizing their faster convergence and lower time complexity compared to common first-order methods like gradient descent. In this paper we introduce QN-Mixer an algorithm based on the quasi-Newton approach. We use learned parameters through the BFGS algorithm and introduce Incept-Mixer an efficient neural architecture that serves as a non-local regularization term capturing long-range dependencies within images. To address the computational demands typically associated with quasi-Newton algorithms that require full Hessian matrix computations we present a memory-efficient alternative. Our approach intelligently downsamples gradient information significantly reducing computational requirements while maintaining performance. The approach is validated through experiments on the sparse-view CT problem involving various datasets and scanning protocols and is compared with post-processing and deep unrolling state-of-the-art approaches. Our method outperforms existing approaches and achieves state-of-the-art performance in terms of SSIM and PSNR all while reducing the number of unrolling iterations required.
Learning CNN on ViT: A Hybrid Model to Explicitly Class-specific Boundaries for Domain Adaptation
Authors: Ba Hung Ngo; Nhat-Tuong Do-Tran; Tuan-Ngoc Nguyen; Hae-Gon Jeon; Tae Jong Choi
Venue: CVPR 2024 (main)

Abstract: Most domain adaptation (DA) methods are based on either a convolutional neural networks (CNNs) or a vision transformers (ViTs). They align the distribution differences between domains as encoders without considering their unique characteristics. For instance ViT excels in accuracy due to its superior ability to capture global representations while CNN has an advantage in capturing local representations. This fact has led us to design a hybrid method to fully take advantage of both ViT and CNN called Explicitly Class-specific Boundaries (ECB). ECB learns CNN on ViT to combine their distinct strengths. In particular we leverage ViT's properties to explicitly find class-specific decision boundaries by maximizing the discrepancy between the outputs of the two classifiers to detect target samples far from the source support. In contrast the CNN encoder clusters target features based on the previously defined class-specific boundaries by minimizing the discrepancy between the probabilities of the two classifiers. Finally ViT and CNN mutually exchange knowledge to improve the quality of pseudo labels and reduce the knowledge discrepancies of these models. Compared to conventional DA methods our ECB achieves superior performance which verifies its effectiveness in this hybrid model. The project website can be found https://dotrannhattuong.github.io/ECB/website/.
A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense Captions
Authors: Jack Urbanek; Florian Bordes; Pietro Astolfi; Mary Williamson; Vasu Sharma; Adriana Romero-Soriano
Venue: CVPR 2024 (main)

Abstract: Curation methods for massive vision-language datasets trade off between dataset size and quality. However even the highest quality of available curated captions are far too short to capture the rich visual detail in an image. To show the value of dense and highly-aligned image-text pairs we collect the Densely Captioned Images (DCI) dataset containing 8012 natural images human-annotated with mask-aligned descriptions averaging above 1000 words each. With precise and reliable captions associated with specific parts of an image we can evaluate vision-language models' (VLMs) understanding of image content with a novel task that matches each caption with its corresponding subcrop. As current models are often limited to 77 text tokens we also introduce a summarized version (sDCI) in which each caption length is limited. We show that modern techniques that make progress on standard benchmarks do not correspond with significant improvement on our sDCI based benchmark. Lastly we finetune CLIP using sDCI and show significant improvements over the baseline despite a small training set. By releasing the first human annotated dense image captioning dataset we hope to enable the development of new benchmarks or fine-tuning recipes for the next generation of VLMs to come.
HanDiffuser: Text-to-Image Generation With Realistic Hand Appearances
Authors: Supreeth Narasimhaswamy; Uttaran Bhattacharya; Xiang Chen; Ishita Dasgupta; Saayan Mitra; Minh Hoai
Venue: CVPR 2024 (main)

Abstract: Text-to-image generative models can generate high-quality humans but realism is lost when generating hands. Common artifacts include irregular hand poses shapes incorrect numbers of fingers and physically implausible finger orientations. To generate images with realistic hands we propose a novel diffusion-based architecture called HanDiffuser that achieves realism by injecting hand embeddings in the generative process. HanDiffuser consists of two components: a Text-to-Hand-Params diffusion model to generate SMPL-Body and MANO-Hand parameters from input text prompts and a Text-Guided Hand-Params-to-Image diffusion model to synthesize images by conditioning on the prompts and hand parameters generated by the previous component. We incorporate multiple aspects of hand representation including 3D shapes and joint-level finger positions orientations and articulations for robust learning and reliable performance during inference. We conduct extensive quantitative and qualitative experiments and perform user studies to demonstrate the efficacy of our method in generating images with high-quality hands.
Infinigen Indoors: Photorealistic Indoor Scenes using Procedural Generation
Authors: Alexander Raistrick; Lingjie Mei; Karhan Kayan; David Yan; Yiming Zuo; Beining Han; Hongyu Wen; Meenal Parakh; Stamatis Alexandropoulos; Lahav Lipson; Zeyu Ma; Jia Deng
Venue: CVPR 2024 (main)

Abstract: We introduce Infinigen Indoors a Blender-based procedural generator of photorealistic indoor scenes. It builds upon the existing Infinigen system which focuses on natural scenes but expands its coverage to indoor scenes by introducing a diverse library of procedural indoor assets including furniture architecture elements appliances and other day-to-day objects. It also introduces a constraint-based arrangement system which consists of a domain-specific language for expressing diverse constraints on scene composition and a solver that generates scene compositions that maximally satisfy the constraints. We provide an export tool that allows the generated 3D objects and scenes to be directly used for training embodied agents in real-time simulators such as Omniverse and Unreal. Infinigen Indoors is open-sourced under the BSD license. Please visit infinigen.org for code and videos.
MART: Masked Affective RepresenTation Learning via Masked Temporal Distribution Distillation
Authors: Zhicheng Zhang; Pancheng Zhao; Eunil Park; Jufeng Yang
Venue: CVPR 2024 (main)

Abstract: Limited training data is a long-standing problem for video emotion analysis (VEA). Existing works leverage the power of large-scale image datasets for transferring while failing to extract the temporal correlation of affective cues in the video. Inspired by psychology research and empirical theory we verify that the degree of emotion may vary in different segments of the video thus introducing the sentiment complementary and emotion intrinsic among temporal segments. We propose an MAE-style method for learning robust affective representation of videos via masking termed MART. First we extract the affective cues of the lexicon and verify the extracted one by computing its matching score with video content in terms of sentiment and emotion scores alongside the temporal dimension. Then with the verified cues we propose masked affective modeling to recover temporal emotion distribution. We present temporal affective complementary learning that pulls the complementary part and pushes the intrinsic one of masked multimodal features where the constraint is set with cross-modal attention among features to mask the video and recover the degree of emotion among segments. Extensive experiments on five benchmarks show the superiority of our method in video sentiment analysis video emotion recognition multimodal sentiment analysis and multimodal emotion recognition.
MTLoRA: Low-Rank Adaptation Approach for Efficient Multi-Task Learning
Authors: Ahmed Agiza; Marina Neseem; Sherief Reda
Venue: CVPR 2024 (main)

Abstract: Adapting models pre-trained on large-scale datasets to a variety of downstream tasks is a common strategy in deep learning. Consequently parameter-efficient fine-tuning methods have emerged as a promising way to adapt pre-trained models to different tasks while training only a minimal number of parameters. While most of these methods are designed for single-task adaptation parameter-efficient training in Multi-Task Learning (MTL) architectures is still unexplored. In this paper we introduce MTLoRA a novel framework for parameter-efficient training of MTL models. MTLoRA employs Task-Agnostic and Task-Specific Low-Rank Adaptation modules which effectively disentangle the parameter space in MTL fine-tuning thereby enabling the model to adeptly handle both task specialization and interaction within MTL contexts. We applied MTLoRA to hierarchical-transformer-based MTL architectures adapting them to multiple downstream dense prediction tasks. Our extensive experiments on the PASCAL dataset show that MTLoRA achieves higher accuracy on downstream tasks compared to fully fine-tuning the MTL model while reducing the number of trainable parameters by 3.6x. Furthermore MTLoRA establishes a Pareto-optimal trade-off between the number of trainable parameters and the accuracy of the downstream tasks outperforming current state-of-the-art parameter-efficient training methods in both accuracy and efficiency.
Hierarchical Patch Diffusion Models for High-Resolution Video Generation
Authors: Ivan Skorokhodov; Willi Menapace; Aliaksandr Siarohin; Sergey Tulyakov
Venue: CVPR 2024 (main)

Abstract: Diffusion models have demonstrated remarkable performance in image and video synthesis. However scaling them to high-resolution inputs is challenging and requires restructuring the diffusion pipeline into multiple independent components limiting scalability and complicating downstream applications. In this work we study patch diffusion models (PDMs) -- a diffusion paradigm which models the distribution of patches rather than whole inputs keeping up to 0.7% of the original pixels. This makes it very efficient during training and unlocks end-to-end optimization on high-resolution videos. We improve PDMs in two principled ways. First to enforce consistency between patches we develop deep context fusion -- an architectural technique that propagates the context information from low-scale to high-scale patches in a hierarchical manner. Second to accelerate training and inference we propose adaptive computation which allocates more network capacity and computation towards coarse image details. The resulting model sets a new state-of-the-art FVD score of 66.32 and Inception Score of 87.68 in class-conditional video generation on UCF-101 256x256 surpassing recent methods by more than 100%. Then we show that it can be rapidly fine-tuned from a base 36x64 low-resolution generator for high-resolution 64x288x512 text-to-video synthesis. To the best of our knowledge our model is the first diffusion-based architecture which is trained on such high resolutions entirely end-to-end. Project webpage: https://snap-research.github.io/hpdm.
Motion Blur Decomposition with Cross-shutter Guidance
Authors: Xiang Ji; Haiyang Jiang; Yinqiang Zheng
Venue: CVPR 2024 (main)

Abstract: Motion blur is a frequently observed image artifact especially under insufficient illumination where exposure time has to be prolonged so as to collect more photons for a bright enough image. Rather than simply removing such blurring effects recent researches have aimed at decomposing a blurry image into multiple sharp images with spatial and temporal coherence. Since motion blur decomposition itself is highly ambiguous priors from neighbouring frames or human annotation are usually needed for motion disambiguation. In this paper inspired by the complementary exposure characteristics of a global shutter (GS) camera and a rolling shutter (RS) camera we propose to utilize the ordered scanline-wise delay in a rolling shutter image to robustify motion decomposition of a single blurry image. To evaluate this novel dual imaging setting we construct a triaxial system to collect realistic data as well as a deep network architecture that explicitly addresses temporal and contextual information through reciprocal branches for cross-shutter motion blur decomposition. Experiment results have verified the effectiveness of our proposed algorithm as well as the validity of our dual imaging setting.
Scene-adaptive and Region-aware Multi-modal Prompt for Open Vocabulary Object Detection
Authors: Xiaowei Zhao; Xianglong Liu; Duorui Wang; Yajun Gao; Zhide Liu
Venue: CVPR 2024 (main)

Abstract: Open Vocabulary Object Detection (OVD) aims to detect objects from novel classes described by text inputs based on the generalization ability of trained classes. Existing methods mainly focus on transferring knowledge from large Vision and Language models (VLM) to detectors through knowledge distillation. However these approaches show weak ability in adapting to diverse classes and aligning between the image-level pre-training and region-level detection thereby impeding effective knowledge transfer. Motivated by the prompt tuning we propose scene-adaptive and region-aware multi-modal prompts to address these issues by effectively adapting class-aware knowledge from VLM to the detector at the region level. Specifically to enhance the adaptability to diverse classes we design a scene-adaptive prompt generator from a scene perspective to consider both the commonality and diversity of the class distributions and formulate a novel selection mechanism to facilitate the acquisition of common knowledge across all classes and specific insights relevant to each scene. Meanwhile to bridge the gap between the pre-trained model and the detector we present a region-aware multi-modal alignment module which employs the region prompt to incorporate the positional information for feature distillation and integrates textual prompts to align visual and linguistic representations. Extensive experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art models on the OV-COCO and OV-LVIS datasets surpassing the current method by 3.0% mAP and 4.6% APr .
MimicDiffusion: Purifying Adversarial Perturbation via Mimicking Clean Diffusion Model
Authors: Kaiyu Song; Hanjiang Lai; Yan Pan; Jian Yin
Venue: CVPR 2024 (main)

Abstract: Deep neural networks (DNNs) are vulnerable to adversarial perturbation where an imperceptible perturbation is added to the image that can fool the DNNs. Diffusion-based adversarial purification uses the diffusion model to generate a clean image against such adversarial attacks. Unfortunately the generative process of the diffusion model is also inevitably affected by adversarial perturbation since the diffusion model is also a deep neural network where its input has adversarial perturbation. In this work we propose MimicDiffusion a new diffusion-based adversarial purification technique that directly approximates the generative process of the diffusion model with the clean image as input. Concretely we analyze the differences between the guided terms using the clean image and the adversarial sample. After that we first implement MimicDiffusion based on Manhattan distance. Then we propose two guidance to purify the adversarial perturbation and approximate the clean diffusion model. Extensive experiments on three image datasets including CIFAR-10 CIFAR-100 and ImageNet with three classifier backbones including WideResNet-70-16 WideResNet-28-10 and ResNet-50 demonstrate that MimicDiffusion significantly performs better than the state-of-the-art baselines. On CIFAR-10 CIFAR-100 and ImageNet it achieves 92.67% 61.35% and 61.53% average robust accuracy which are 18.49% 13.23% and 17.64% higher respectively. The code is available at https://github.com/psky1111/MimicDiffusion.