CVPR2024/CVPR2024-papers-abstract-index huggingface dataset:
We propose NeRFiller an approach that completes missing portions of a 3D capture via generative 3D inpainting using off-the-shelf 2D visual generative models. Often parts of a captured 3D scene or object are missing due to mesh reconstruction failures or a lack of observations (e.g. contact regions such as the bottom of objects or hard-to-reach areas). We approach this challenging 3D inpainting problem by leveraging a 2D inpainting diffusion model. We identify a surprising behavior of these models where they generate more 3D consistent inpaints when images form a 2x2 grid and show how to generalize this behavior to more than four images. We then present an iterative framework to distill these inpainted regions into a single consistent 3D scene. In contrast to related works we focus on completing scenes rather than deleting foreground objects and our approach does not require tight 2D object masks or text. We compare our approach to relevant baselines adapted to our setting on a variety of scenes where NeRFiller creates the most 3D consistent and plausible scene completions. Our project page is at https://ethanweber.me/nerfiller/.
The burgeoning field of Multimodal Large Language Models (MLLMs) has exhibited remarkable performance in diverse tasks such as captioning commonsense reasoning and visual scene understanding. However the deployment of these large-scale MLLMs on client devices is hindered by their extensive model parameters leading to a notable decline in generalization capabilities when these models are compressed for device deployment. Addressing this challenge we introduce a Cloud-Device Collaborative Continual Adaptation framework designed to enhance the performance of compressed device-deployed MLLMs by leveraging the robust capabilities of cloud-based larger-scale MLLMs. Our framework is structured into three key components: a device-to-cloud uplink for efficient data transmission cloud-based knowledge adaptation and an optimized cloud-to-device downlink for model deployment. In the uplink phase we employ an Uncertainty-guided Token Sampling (UTS) strategy to effectively filter out-of-distribution tokens thereby reducing transmission costs and improving training efficiency. On the cloud side we propose Adapter-based Knowledge Distillation (AKD) method to transfer refined knowledge from large-scale to compressed pocket-size MLLMs.
On the cloud side we propose Adapter-based Knowledge Distillation (AKD) method to transfer refined knowledge from large-scale to compressed pocket-size MLLMs. Furthermore we propose a Dynamic Weight update Compression (DWC) strategy for the downlink which adaptively selects and quantizes updated weight parameters enhancing transmission efficiency and reducing the representational disparity between cloud and device models. Extensive experiments on several multimodal benchmarks demonstrate the superiority of our proposed framework over prior Knowledge Distillation and device-cloud collaboration methods. Notably we also validate the feasibility of our approach to real-world experiments.
DETR is a novel end-to-end transformer architecture object detector which significantly outperforms classic detectors when scaling up. In this paper we focus on the compression of DETR with knowledge distillation. While knowledge distillation has been well-studied in classic detectors there is a lack of researches on how to make it work effectively on DETR. We first provide experimental and theoretical analysis to point out that the main challenge in DETR distillation is the lack of consistent distillation points. Distillation points refer to the corresponding inputs of the predictions for student to mimic which have different formulations in CNN detector and DETR and reliable distillation requires sufficient distillation points which are consistent between teacher and student. Based on this observation we propose the first general knowledge distillation paradigm for DETR(KD-DETR) with consistent distillation points sampling for both homogeneous and heterogeneous distillation. Specifically we decouple detection and distillation tasks by introducing a set of specialized object queries to construct distillation points for DETR. We further propose a general-to-specific distillation points sampling strategy to explore the extensibility of KD-DETR. Extensive experiments validate the effectiveness and generalization of KD-DETR.
We further propose a general-to-specific distillation points sampling strategy to explore the extensibility of KD-DETR. Extensive experiments validate the effectiveness and generalization of KD-DETR. For both single-scale DAB-DETR and multis-scale Deformable DETR and DINO KD-DETR boost the performance of student model with improvements of 2.6%-5.2%. We further extend KD-DETR to heterogeneous distillation and achieves 2.1% improvement by distilling the knowledge from DINO to Faster R-CNN with ResNet-50 which is comparable with homogeneous distillation methods.
Keypoints used for image matching often include an estimate of the feature scale and orientation. While recent work has demonstrated the advantages of using feature scales and orientations for relative pose estimation relatively little work has considered their use for absolute pose estimation. We introduce minimal solutions for absolute pose from two oriented feature correspondences in the general case or one scaled and oriented correspondence given a known vertical direction. Nowadays assuming a known direction is not particularly restrictive as modern consumer devices such as smartphones or drones are equipped with Inertial Measurement Units (IMU) that provide the gravity direction by default. Compared to traditional absolute pose methods requiring three point correspondences our solvers need a smaller minimal sample reducing the cost and complexity of robust estimation. Evaluations on large-scale and public real datasets demonstrate the advantage of our methods for fast and accurate localization in challenging conditions. Code is available at https://github.com/danini/absolute-pose-from-oriented-and-scaled-features .
Source-Free Domain Adaptation (SFDA) aims to adapt a source model for a target domain with only access to unlabeled target training data and the source model pretrained on a supervised source domain. Relying on pseudo labeling and/or auxiliary supervision conventional methods are inevitably error-prone. To mitigate this limitation in this work we for the first time explore the potentials of off-the-shelf vision-language (ViL) multimodal models (e.g. CLIP) with rich whilst heterogeneous knowledge. We find that directly applying the ViL model to the target domain in a zero-shot fashion is unsatisfactory as it is not specialized for this particular task but largely generic. To make it task specific we propose a novel Distilling multImodal Foundation mOdel (DIFO) approach. Specifically DIFO alternates between two steps during adaptation: (i) Customizing the ViL model by maximizing the mutual information with the target model in a prompt learning manner (ii) Distilling the knowledge of this customized ViL model to the target model. For more fine-grained and reliable distillation we further introduce two effective regularization terms namely most-likely category encouragement and predictive consistency.
For more fine-grained and reliable distillation we further introduce two effective regularization terms namely most-likely category encouragement and predictive consistency. Extensive experiments show that DIFO significantly outperforms the state-of-the-art alternatives. Code is here.
The capacity of existing human keypoint localization models is limited by keypoint priors provided by the training data. To alleviate this restriction and pursue more general model this work studies keypoint localization from a different perspective by reasoning locations based on keypiont clues in text descriptions. We propose LocLLM the first Large-Language Model (LLM) based keypoint localization model that takes images and text instructions as inputs and outputs the desired keypoint coordinates. LocLLM leverages the strong reasoning capability of LLM and clues of keypoint type location and relationship in textual descriptions for keypoint localization. To effectively tune LocLLM we construct localization-based instruction conversations to connect keypoint description with corresponding coordinates in input image and fine-tune the whole model in a parameter-efficient training pipeline. LocLLM shows remarkable performance on standard 2D/3D keypoint localization benchmarks. Moreover incorporating language clues into the localization makes LocLLM show superior flexibility and generalizable capability in cross dataset keypoint localization and even detecting novel type of keypoints unseen during training.
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.
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.
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/.
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.
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.
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.
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/.
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.
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.
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/.
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.
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.
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.
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.
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.