CVPR 2024 Papers

This paper proposes an image-vector dual diffusion model for generative layout design. Distinct from prior efforts that mostly ignore element-level visual information our approach integrates the power of a pre-trained large image diffusion model to guide layout composition in a vector diffusion model by providing enhanced salient region understanding and high-level inter-element relationship reasoning. Our proposed model simultaneously operates in two domains: it generates the overall design appearance in the image domain while optimizing the size and position of each design element in the vector domain. The proposed method achieves the state-of-the-art results on several datasets and enables new layout design applications.

In-context prompting in large language models (LLMs) has become a prevalent approach to improve zero-shot capabilities but this idea is less explored in the vision domain. Existing visual prompting methods focus on referring segmentation to segment the most relevant object falling short of addressing many generic vision tasks like open-set segmentation and detection. In this paper we introduce a universal visual in-context prompting framework for both tasks as shown in Fig.1. In particular we build on top of an encoder-decoder architecture and develop a versatile prompt encoder to support a variety of prompts like strokes boxes and points. We further enhance it to take an arbitrary number of reference image segments as the context. Our extensive explorations show that the proposed visual in-context prompting elicits extraordinary referring and generic segmentation capabilities to refer and detect yielding competitive performance to close-set in-domain datasets and showing promising results on many open-set segmentation datasets. By joint training on COCO and SA-1B DINOv achieves 57.7 PQ on COCO and 23.2 PQ on ADE20K. Code will be available at https://github.com/UX-Decoder/DINOv

Existing automatic captioning methods for visual content face challenges such as lack of detail content hallucination and poor instruction following. In this work we propose VisualFactChecker (VFC) a flexible training-free pipeline that generates high-fidelity and detailed captions for both 2D images and 3D objects. VFC consists of three steps: 1) proposal where image-to-text captioning models propose multiple initial captions; 2) verification where a large language model (LLM) utilizes tools such as object detection and VQA models to fact-check proposed captions; 3) captioning where an LLM generates the final caption by summarizing caption proposals and the fact check verification results. In this step VFC can flexibly generate captions in various styles following complex instructions. We conduct comprehensive captioning evaluations using four metrics: 1) CLIP-Score for image-text similarity; 2) CLIP-Image-Score for measuring the image-image similarity between the original and the reconstructed image generated by a text-to-image model using the caption. 3) human study on Amazon Mechanical Turk; 4) GPT-4V for fine-grained evaluation. Evaluation results show that VFC outperforms state-of-the-art open-sourced captioning methods for 2D images on the COCO dataset and 3D assets on the Objaverse dataset. Our study demonstrates that by combining open-source models into a pipeline we can attain captioning capability comparable to proprietary models such as GPT-4V despite being over 10x smaller in model size.

While existing large vision-language multimodal models focus on whole image understanding there is a prominent gap in achieving region-specific comprehension. Current approaches that use textual coordinates or spatial encodings often fail to provide a user-friendly interface for visual prompting. To address this challenge we introduce a novel multimodal model capable of decoding arbitrary (free-form) visual prompts. This allows users to intuitively mark images and interact with the model using natural cues like a "red bounding box" or "pointed arrow'". Our simple design directly overlays visual markers onto the RGB image eliminating the need for complex region encodings yet achieves state-of-the-art performance on region-understanding tasks like Visual7W PointQA and Visual Commonsense Reasoning benchmark. Furthermore we present ViP-Bench a comprehensive benchmark to assess the capability of models in understanding visual prompts across multiple dimensions enabling future research in this domain. Code data and model are publicly available.

In this paper we propose a novel Visual Reference Prompt (VRP) encoder that empowers the Segment Anything Model (SAM) to utilize annotated reference images as prompts for segmentation creating the VRP-SAM model. In essence VRP-SAM can utilize annotated reference images to comprehend specific objects and perform segmentation of specific objects in target image. It is note that the VRP encoder can support a variety of annotation formats for reference images including point box scribble and mask. VRP-SAM achieves a breakthrough within the SAM framework by extending its versatility and applicability while preserving SAM's inherent strengths thus enhancing user-friendliness. To enhance the generalization ability of VRP-SAM the VRP encoder adopts a meta-learning strategy. To validate the effectiveness of VRP-SAM we conducted extensive empirical studies on the Pascal and COCO datasets. Remarkably VRP-SAM achieved state-of-the-art performance in visual reference segmentation with minimal learnable parameters. Furthermore VRP-SAM demonstrates strong generalization capabilities allowing it to perform segmentation of unseen objects and enabling cross-domain segmentation. The source code and models will be available at https://github.com/syp2ysy/VRP-SAM

Recent innovations on text-to-3D generation have featured Score Distillation Sampling (SDS) which enables the zero-shot learning of implicit 3D models (NeRF) by directly distilling prior knowledge from 2D diffusion models. However current SDS-based models still struggle with intricate text prompts and commonly result in distorted 3D models with unrealistic textures or cross-view inconsistency issues. In this work we introduce a novel Visual Prompt-guided text-to-3D diffusion model (VP3D) that explicitly unleashes the visual appearance knowledge in 2D visual prompt to boost text-to-3D generation. Instead of solely supervising SDS with text prompt VP3D first capitalizes on 2D diffusion model to generate a high-quality image from input text which subsequently acts as visual prompt to strengthen SDS optimization with explicit visual appearance. Meanwhile we couple the SDS optimization with additional differentiable reward function that encourages rendering images of 3D models to better visually align with 2D visual prompt and semantically match with text prompt. Through extensive experiments we show that the 2D Visual Prompt in our VP3D significantly eases the learning of visual appearance of 3D models and thus leads to higher visual fidelity with more detailed textures. It is also appealing in view that when replacing the self-generating visual prompt with a given reference image VP3D is able to trigger a new task of stylized text-to-3D generation. Our project page is available at https://vp3d-cvpr24.github.io.

Recent advances in large video-language models have displayed promising outcomes in video comprehension. Current approaches straightforwardly convert video into language tokens and employ large language models for multi-modal tasks. However this method often leads to the generation of irrelevant content commonly known as "hallucination" as the length of the text increases and the impact of the video diminishes. To address this problem we propose Vista-LLaMA a novel framework that maintains the consistent distance between all visual tokens and any language tokens irrespective of the generated text length. Vista-LLaMA omits relative position encoding when determining attention weights between visual and text tokens retaining the position encoding for text and text tokens. This amplifies the effect of visual tokens on text generation especially when the relative distance is longer between visual and text tokens. The proposed attention mechanism significantly reduces the chance of producing irrelevant text related to the video content. Furthermore we present a sequential visual projector that projects the current video frame into tokens of language space with the assistance of the previous frame. This approach not only captures the temporal relationship within the video but also allows less visual tokens to encompass the entire video. Our approach significantly outperforms various previous methods (e.g. Video-ChatGPT MovieChat) on four challenging open-ended video question answering benchmarks. We reach an accuracy of 60.7 on the zero-shot NExT-QA and 60.5 on the zero-shot MSRVTT-QA setting a new state-of-the-art performance.

Visual language models (VLMs) rapidly progressed with the recent success of large language models. There have been growing efforts on visual instruction tuning to extend the LLM with visual inputs but lacks an in-depth study of the visual language pre-training process where the model learns to perform joint modeling on both modalities. In this work we examine the design options for VLM pre-training by augmenting LLM towards VLM through step-by-step con- trollable comparisons. We introduce three main findings: (1) freezing LLMs during pre-training can achieve decent zero-shot performance but lack in-context learning capabil- ity which requires unfreezing the LLM; (2) interleaved pre- training data is beneficial whereas image-text pairs alone are not optimal; (3) re-blending text-only instruction data to image-text data during instruction fine-tuning not only remedies the degradation of text-only tasks but also boosts VLM task accuracy. With an enhanced pre-training recipe we build VILA a Visual Language model family that consis- tently outperforms the state-of-the-art models e.g. LLaVA- 1.5 across main benchmarks without bells and whistles. Multi-modal pre-training also helps unveil appealing prop- erties of VILA including multi-image reasoning enhanced in-context learning and better world knowledge. VILA is also deployable on Jetson Orin for on-device VLM.

Structure-from-motion (SfM) is a long-standing problem in the computer vision community which aims to reconstruct the camera poses and 3D structure of a scene from a set of unconstrained 2D images. Classical frameworks solve this problem in an incremental manner by detecting and matching keypoints registering images triangulating 3D points and conducting bundle adjustment. Recent research efforts have predominantly revolved around harnessing the power of deep learning techniques to enhance specific elements (e.g. keypoint matching) but are still based on the original non-differentiable pipeline. Instead we propose a new deep SfM pipeline where each component is fully differentiable and thus can be trained in an end-to-end manner. To this end we introduce new mechanisms and simplifications. First we build on recent advances in deep 2D point tracking to extract reliable pixel-accurate tracks which eliminates the need for chaining pairwise matches. Furthermore we recover all cameras simultaneously based on the image and track features instead of gradually registering cameras. Finally we optimise the cameras and triangulate 3D points via a differentiable bundle adjustment layer. We attain state-of-the-art performance on three popular datasets CO3D IMC Phototourism and ETH3D.

Humans possess a remarkable ability to integrate auditory and visual information enabling a deeper understanding of the surrounding environment. This early fusion of audio and visual cues demonstrated through cognitive psychology and neuroscience research offers promising potential for developing multimodal perception models. However training early fusion architectures poses significant challenges as the increased model expressivity requires robust learning frameworks to harness their enhanced capabilities. In this paper we address this challenge by leveraging the masked reconstruction framework previously successful in unimodal settings to train audio-visual encoders with early fusion. Additionally we propose an attention-based fusion module that captures interactions between local audio and visual representations enhancing the model's ability to capture fine-grained interactions. While effective this procedure can become computationally intractable as the number of local representations increases. Thus to address the computational complexity we propose an alternative procedure that factorizes the local representations before representing audio-visual interactions. Extensive evaluations on a variety of datasets demonstrate the superiority of our approach in audio-event classification visual sound localization sound separation and audio-visual segmentation. These contributions enable the efficient training of deeply integrated audio-visual models and significantly advance the usefulness of early fusion architectures.

Audio-visual segmentation (AVS) is a challenging task that involves accurately segmenting sounding objects based on audio-visual cues. The effectiveness of audio-visual learning critically depends on achieving accurate cross-modal alignment between sound and visual objects. Successful audio-visual learning requires two essential components: 1) a challenging dataset with high-quality pixel-level multi-class annotated images associated with audio files and 2) a model that can establish strong links between audio information and its corresponding visual object. However these requirements are only partially addressed by current methods with training sets containing biased audio-visual data and models that generalise poorly beyond this biased training set. In this work we propose a new cost-effective strategy to build challenging and relatively unbiased high-quality audio-visual segmentation benchmarks. We also propose a new informative sample mining method for audio-visual supervised contrastive learning to leverage discriminative contrastive samples to enforce cross-modal understanding. We show empirical results that demonstrate the effectiveness of our benchmark. Furthermore experiments conducted on existing AVS datasets and on our new benchmark show that our method achieves state-of-the-art (SOTA) segmentation accuracy.

Due to the depth degradation effect in residual connections many efficient Vision Transformers models that rely on stacking layers for information exchange often fail to form sufficient information mixing leading to unnatural visual perception. To address this issue in this paper we propose Aggregated Attention a biomimetic design-based token mixer that simulates biological foveal vision and continuous eye movement while enabling each token on the feature map to have a global perception. Furthermore we incorporate learnable tokens that interact with conventional queries and keys which further diversifies the generation of affinity matrices beyond merely relying on the similarity between queries and keys. Our approach does not rely on stacking for information exchange thus effectively avoiding depth degradation and achieving natural visual perception. Additionally we propose Convolutional GLU a channel mixer that bridges the gap between GLU and SE mechanism which empowers each token to have channel attention based on its nearest neighbor image features enhancing local modeling capability and model robustness. We combine aggregated attention and convolutional GLU to create a new visual backbone called TransNeXt. Extensive experiments demonstrate that our TransNeXt achieves state-of-the-art performance across multiple model sizes. At a resolution of 224^2 TransNeXt-Tiny attains an ImageNet accuracy of 84.0% surpassing ConvNeXt-B with 69% fewer parameters. Our TransNeXt-Base achieves an ImageNet accuracy of 86.2% and an ImageNet-A accuracy of 61.6% at a resolution of 384^2 a COCO object detection mAP of 57.1 and an ADE20K semantic segmentation mIoU of 54.7.

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

In recent years the thriving development of research related to egocentric videos has provided a unique perspective for the study of conversational interactions where both visual and audio signals play a crucial role. While most prior work focus on learning about behaviors that directly involve the camera wearer we introduce the Ego-Exocentric Conversational Graph Prediction problem marking the first attempt to infer exocentric conversational interactions from egocentric videos. We propose a unified multi-modal framework---Audio-Visual Conversational Attention (AV-CONV) for the joint prediction of conversation behaviors---speaking and listening---for both the camera wearer as well as all other social partners present in the egocentric video. Specifically we adopt the self-attention mechanism to model the representations across-time across-subjects and across-modalities. To validate our method we conduct experiments on a challenging egocentric video dataset that includes multi-speaker and multi-conversation scenarios. Our results demonstrate the superior performance of our method compared to a series of baselines. We also present detailed ablation studies to assess the contribution of each component in our model. Check our \href https://vjwq.github.io/AV-CONV/ Project Page .

Diverse actions give rise to rich audio-visual signals in long videos. Recent works showcase that the two modalities of audio and video exhibit different temporal extents of events and distinct labels. We address the interplay between the two modalities in long videos by explicitly modelling the temporal extents of audio and visual events. We propose the Time Interval Machine (TIM) where a modality-specific time interval poses as a query to a transformer encoder that ingests a long video input. The encoder then attends to the specified interval as well as the surrounding context in both modalities in order to recognise the ongoing action. We test TIM on three long audio-visual video datasets: EPIC-KITCHENS Perception Test and AVE reporting state-of-the-art (SOTA) for recognition. On EPIC-KITCHENS we beat previous SOTA that utilises LLMs and significantly larger pre-training by 2.9% top-1 action recognition accuracy. Additionally we show that TIM can be adapted for action detection using dense multi-scale interval queries outperforming SOTA on EPIC-KITCHENS-100 for most metrics and showing strong performance on the Perception Test. Our ablations show the critical role of integrating the two modalities and modelling their time intervals in achieving this performance. Code and models at: https://github.com/JacobChalk/TIM.

Visual sound source localization poses a significant challenge in identifying the semantic region of each sounding source within a video. Existing self-supervised and weakly supervised source localization methods struggle to accurately distinguish the semantic regions of each sounding object particularly in multi-source mixtures. These methods often rely on audio-visual correspondence as guidance which can lead to substantial performance drops in complex multi-source localization scenarios. The lack of access to individual source sounds in multi-source mixtures during training exacerbates the difficulty of learning effective audio-visual correspondence for localization. To address this limitation in this paper we propose incorporating the text modality as an intermediate feature guide using tri-modal joint embedding models (e.g. AudioCLIP) to disentangle the semantic audio-visual source correspondence in multi-source mixtures. Our framework dubbed T-VSL begins by predicting the class of sounding entities in mixtures. Subsequently the textual representation of each sounding source is employed as guidance to disentangle fine-grained audio-visual source correspondence from multi-source mixtures leveraging the tri-modal AudioCLIP embedding. This approach enables our framework to handle a flexible number of sources and exhibits promising zero-shot transferability to unseen classes during test time. Extensive experiments conducted on the MUSIC VGGSound and VGGSound-Instruments datasets demonstrate significant performance improvements over state-of-the-art methods. Code is released at https://github.com/enyac-group/T-VSL/tree/main.

Computer vision tasks typically involve describing what is visible in an image (e.g. classification detection segmentation and captioning). We study a visual common sense task that requires understanding 'what is not visible'. Specifically given an image (e.g. of a living room) and a name of an object ("cushion") a vision system is asked to predict semantically-meaningful regions (masks or bounding boxes) in the image where that object could be placed or is likely be placed by humans (e.g. on the sofa). We call this task: Semantic Placement (SP) and believe that such common-sense visual understanding is critical for assitive robots (tidying a house) AR devices (automatically rendering an object in the user's space) and visually-grounded chatbots with common sense. Studying the invisible is hard. Datasets for image description are typically constructed by curating relevant images (e.g. via image search with object names) and asking humans to annotate the contents of the image; neither of those two steps are straightforward for objects not present in the image. We overcome this challenge by operating in the opposite direction: we start with an image of an object in context (which is easy to find online) and remove that object from the image via inpainting. This automated pipeline converts unstructured web data into a paired with/without object dataset. With this proposed data generation pipeline we collect a novel dataset containing 1.3M images across 9 object categories. We then train a SP prediction model called CLIP-UNet on our dataset. The CLIP-UNet outperforms existing VLMs and baselines that combine semantic priors with object detectors generalizes well to real-world and simulated images exhibits semantics-aware reasoning for object placement and enables downstream applications like tidying robots in indoor environments.

Video and audio content creation serves as the core technique for the movie industry and professional users. Recently existing diffusion-based methods tackle video and audio generation separately which hinders the technique transfer from academia to industry. In this work we aim at filling the gap with a carefully designed optimization-based framework for cross-visual-audio and joint-visual-audio generation. We observe the powerful generation ability of off-the-shelf video or audio generation models. Thus instead of training the giant models from scratch we propose to bridge the existing strong models with a shared latent representation space. Specifically we propose a multimodality latent aligner with the pre-trained ImageBind model. Our latent aligner shares a similar core as the classifier guidance that guides the diffusion denoising process during inference time. Through carefully designed optimization strategy and loss functions we show the superior performance of our method on joint video-audio generation visual-steered audio generation and audio-steered visual generation tasks. The project website can be found at \href https://yzxing87.github.io/Seeing-and-Hearing/ https://yzxing87.github.io/Seeing-and-Hearing/ .

Accuracy and computational efficiency are the most important metrics to Visual Inertial Navigation System (VINS). The existing VINS algorithms with either high accuracy or low computational complexity are difficult to provide the high precision localization in resource-constrained devices. To this end we propose a novel filter-based VINS framework named SchurVINS (SV) which could guarantee both high accuracy by building a complete residual model and low computational complexity with Schur complement. Technically we first formulate the full residual model where Gradient Hessian and observation covariance are explicitly modeled. Then Schur complement is employed to decompose the full model into ego-motion residual model and landmark residual model. Finally Extended Kalman Filter (EKF) update is implemented in these two models with high efficiency. Experiments on EuRoC and TUM-VI datasets show that our method notably outperforms state-of-the-art (SOTA) methods in both accuracy and computational complexity. The experimental code of SchurVINS is available at https://github.com/bytedance/SchurVINS.