GenAI4ELab/papercli-papers huggingface dataset:
Bidirectional Multi-Scale Implicit Neural Representations for Image Deraining
Authors: Xiang Chen; Jinshan Pan; Jiangxin Dong
Venue: CVPR 2024 (main)

Abstract: How to effectively explore multi-scale representations of rain streaks is important for image deraining. In contrast to existing Transformer-based methods that depend mostly on single-scale rain appearance we develop an end-to-end multi-scale Transformer that leverages the potentially useful features in various scales to facilitate high-quality image reconstruction. To better explore the common degradation representations from spatially-varying rain streaks we incorporate intra-scale implicit neural representations based on pixel coordinates with the degraded inputs in a closed-loop design enabling the learned features to facilitate rain removal and improve the robustness of the model in complex scenarios. To ensure richer collaborative representation from different scales we embed a simple yet effective inter-scale bidirectional feedback operation into our multi-scale Transformer by performing coarse-to-fine and fine-to-coarse information communication. Extensive experiments demonstrate that our approach named as NeRD-Rain performs favorably against the state-of-the-art ones on both synthetic and real-world benchmark datasets. The source code and trained models are available at https://github.com/cschenxiang/NeRD-Rain.
Frozen CLIP: A Strong Backbone for Weakly Supervised Semantic Segmentation
Authors: Bingfeng Zhang; Siyue Yu; Yunchao Wei; Yao Zhao; Jimin Xiao
Venue: CVPR 2024 (main)

Abstract: Weakly supervised semantic segmentation has witnessed great achievements with image-level labels. Several recent approaches use the CLIP model to generate pseudo labels for training an individual segmentation model while there is no attempt to apply the CLIP model as the backbone to directly segment objects with image-level labels. In this paper we propose WeCLIP a CLIP-based single-stage pipeline for weakly supervised semantic segmentation. Specifically the frozen CLIP model is applied as the backbone for semantic feature extraction and a new decoder is designed to interpret extracted semantic features for final prediction. Meanwhile we utilize the above frozen backbone to generate pseudo labels for training the decoder. Such labels cannot be optimized during training. We then propose a refinement module (RFM) to rectify them dynamically. Our architecture enforces the proposed decoder and RFM to benefit from each other to boost the final performance. Extensive experiments show that our approach significantly outperforms other approaches with less training cost. Additionally our WeCLIP also obtains promising results for fully supervised settings. The code is available at https://github.com/zbf1991/WeCLIP.
FedAS: Bridging Inconsistency in Personalized Federated Learning
Authors: Xiyuan Yang; Wenke Huang; Mang Ye
Venue: CVPR 2024 (main)

Abstract: Personalized Federated Learning (PFL) is primarily designed to provide customized models for each client to better fit the non-iid distributed client data which is a inherent challenge in Federated Learning. However current PFL methods suffer from inconsistencies in both intra-client and inter-client levels: 1) The intra-client inconsistency stems from the asynchronous update strategy for personalized and shared parameters. In PFL clients update their shared parameters to communicate and learn from others while keeping personalized parts unchanged leading to poor coordination between these two components. 2) The Inter-client inconsistency arises from "stragglers" - inactive clients that communicate and train with the server less frequently. This results in their under-trained personalized models and impedes the collaborative training stage for other clients. In this paper we present a novel PFL framework named FedAS which uses Federated Parameter-Alignment and Client-Synchronization to overcome above challenges. Initially we enhance the localization of global parameters by infusing them with local insights. We make the shared parts learn from previous model thereby increasing their local relevance and reducing the impact of parameter inconsistency. Furthermore we design a robust aggregation method to mitigate the impact of stragglers by preventing the incorporation of their under-trained knowledge into aggregated model. Experimental results on Cifar10 and Cifar100 validate the effectiveness of our FedAS in achieving better performance and robustness against data heterogeneity.
LAFS: Landmark-based Facial Self-supervised Learning for Face Recognition
Authors: Zhonglin Sun; Chen Feng; Ioannis Patras; Georgios Tzimiropoulos
Venue: CVPR 2024 (main)

Abstract: In this work we focus on learning facial representations that can be adapted to train effective face recognition models particularly in the absence of labels. Firstly compared with existing labelled face datasets a vastly larger magnitude of unlabeled faces exists in the real world. We explore the learning strategy of these unlabeled facial images through self-supervised pretraining to transfer generalized face recognition performance. Moreover motivated by one recent finding that is the face saliency area is critical for face recognition in contrast to utilizing random cropped blocks of images for constructing augmentations in pretraining we utilize patches localized by extracted facial landmarks. This enables our method - namely Landmark-based Facial Self-supervised learning (LAFS) to learn key representation that is more critical for face recognition. We also incorporate two landmark-specific augmentations which introduce more diversity of landmark information to further regularize the learning. With learned landmark-based facial representations we further adapt the representation for face recognition with regularization mitigating variations in landmark positions. Our method achieves significant improvement over the state-of-the-art on multiple face recognition benchmarks especially on more challenging few-shot scenarios. The code is available at https://github.com/szlbiubiubiu/LAFS_CVPR2024
SED: A Simple Encoder-Decoder for Open-Vocabulary Semantic Segmentation
Authors: Bin Xie; Jiale Cao; Jin Xie; Fahad Shahbaz Khan; Yanwei Pang
Venue: CVPR 2024 (main)

Abstract: Open-vocabulary semantic segmentation strives to distinguish pixels into different semantic groups from an open set of categories. Most existing methods explore utilizing pre-trained vision-language models in which the key is to adopt the image-level model for pixel-level segmentation task. In this paper we propose a simple encoder-decoder named SED for open-vocabulary semantic segmentation which comprises a hierarchical encoder-based cost map generation and a gradual fusion decoder with category early rejection. The hierarchical encoder-based cost map generation employs hierarchical backbone instead of plain transformer to predict pixel-level image-text cost map. Compared to plain transformer hierarchical backbone better captures local spatial information and has linear computational complexity with respect to input size. Our gradual fusion decoder employs a top-down structure to combine cost map and the feature maps of different backbone levels for segmentation. To accelerate inference speed we introduce a category early rejection scheme in the decoder that rejects many no-existing categories at the early layer of decoder resulting in at most 4.7 times acceleration without accuracy degradation. Experiments are performed on multiple open-vocabulary semantic segmentation datasets which demonstrates the efficacy of our SED method. When using ConvNeXt-B our SED method achieves mIoU score of 31.6% on ADE20K with 150 categories at 82 millisecond (ms) per image on a single A6000. Our source code is available at https://github.com/xb534/SED.
GPLD3D: Latent Diffusion of 3D Shape Generative Models by Enforcing Geometric and Physical Priors
Authors: Yuan Dong; Qi Zuo; Xiaodong Gu; Weihao Yuan; Zhengyi Zhao; Zilong Dong; Liefeng Bo; Qixing Huang
Venue: CVPR 2024 (main)

Abstract: State-of-the-art man-made shape generative models usually adopt established generative models under a suitable implicit shape representation. A common theme is to perform distribution alignment which does not explicitly model important shape priors. As a result many synthetic shapes are not connected. Other synthetic shapes present problems of physical stability and geometric feasibility. This paper introduces a novel latent diffusion shape-generative model regularized by a quality checker that outputs a score of a latent code. The scoring function employs a learned function that provides a geometric feasibility score and a deterministic procedure to quantify a physical stability score. The key to our approach is a new diffusion procedure that combines the discrete empirical data distribution and a continuous distribution induced by the quality checker. We introduce a principled approach to determine the tradeoff parameters for learning the denoising network at different noise levels. Experimental results show that our approach outperforms state-of-the-art shape generations quantitatively and qualitatively on ShapeNet-v2.
Enhancing Quality of Compressed Images by Mitigating Enhancement Bias Towards Compression Domain
Authors: Qunliang Xing; Mai Xu; Shengxi Li; Xin Deng; Meisong Zheng; Huaida Liu; Ying Chen
Venue: CVPR 2024 (main)

Abstract: Existing quality enhancement methods for compressed images focus on aligning the enhancement domain with the raw domain to yield realistic images. However these methods exhibit a pervasive enhancement bias towards the compression domain inadvertently regarding it as more realistic than the raw domain. This bias makes enhanced images closely resemble their compressed counterparts thus degrading their perceptual quality. In this paper we propose a simple yet effective method to mitigate this bias and enhance the quality of compressed images. Our method employs a conditional discriminator with the compressed image as a key condition and then incorporates a domain-divergence regularization to actively distance the enhancement domain from the compression domain. Through this dual strategy our method enables the discrimination against the compression domain and brings the enhancement domain closer to the raw domain. Comprehensive quality evaluations confirm the superiority of our method over other state-of-the-art methods without incurring inference overheads.
LangSplat: 3D Language Gaussian Splatting
Authors: Minghan Qin; Wanhua Li; Jiawei Zhou; Haoqian Wang; Hanspeter Pfister
Venue: CVPR 2024 (main)

Abstract: Humans live in a 3D world and commonly use natural language to interact with a 3D scene. Modeling a 3D language field to support open-ended language queries in 3D has gained increasing attention recently. This paper introduces LangSplat which constructs a 3D language field that enables precise and efficient open-vocabulary querying within 3D spaces. Unlike existing methods that ground CLIP language embeddings in a NeRF model LangSplat advances the field by utilizing a collection of 3D Gaussians each encoding language features distilled from CLIP to represent the language field. By employing a tile-based splatting technique for rendering language features we circumvent the costly rendering process inherent in NeRF. Instead of directly learning CLIP embeddings LangSplat first trains a scene-wise language autoencoder and then learns language features on the scene-specific latent space thereby alleviating substantial memory demands imposed by explicit modeling. Existing methods struggle with imprecise and vague 3D language fields which fail to discern clear boundaries between objects. We delve into this issue and propose to learn hierarchical semantics using SAM thereby eliminating the need for extensively querying the language field across various scales and the regularization of DINO features. Extensive experimental results show that LangSplat significantly outperforms the previous state-of-the-art method LERF by a large margin. Notably LangSplat is extremely efficient achieving a 199 x speedup compared to LERF at the resolution of 1440 x 1080. We strongly recommend readers to check out our video results at https://langsplat.github.io/.
MoST: Multi-Modality Scene Tokenization for Motion Prediction
Authors: Norman Mu; Jingwei Ji; Zhenpei Yang; Nate Harada; Haotian Tang; Kan Chen; Charles R. Qi; Runzhou Ge; Kratarth Goel; Zoey Yang; Scott Ettinger; Rami Al-Rfou; Dragomir Anguelov; Yin Zhou
Venue: CVPR 2024 (main)

Abstract: Many existing motion prediction approaches rely on symbolic perception outputs to generate agent trajectories such as bounding boxes road graph information and traffic lights. This symbolic representation is a high-level abstraction of the real world which may render the motion prediction model vulnerable to perception errors (e.g. failures in detecting open-vocabulary obstacles) while missing salient information from the scene context (e.g. poor road conditions). An alternative paradigm is end-to-end learning from raw sensors. However this approach suffers from the lack of interpretability and requires significantly more training resources. In this work we propose tokenizing the visual world into a compact set of scene elements and then leveraging pre-trained image foundation models and LiDAR neural networks to encode all the scene elements in an open-vocabulary manner. The image foundation model enables our scene tokens to encode the general knowledge of the open world while the LiDAR neural network encodes geometry information. Our proposed representation can efficiently encode the multi-frame multi-modality observations with a few hundred tokens and is compatible with most transformer-based architectures. To evaluate our method we have augmented Waymo Open Motion Dataset with camera embeddings. Experiments over Waymo Open Motion Dataset show that our approach leads to significant performance improvements over the state-of-the-art.
PIGEON: Predicting Image Geolocations
Authors: Lukas Haas; Michal Skreta; Silas Alberti; Chelsea Finn
Venue: CVPR 2024 (main)

Abstract: Planet-scale image geolocalization remains a challenging problem due to the diversity of images originating from anywhere in the world. Although approaches based on vision transformers have made significant progress in geolocalization accuracy success in prior literature is constrained to narrow distributions of images of landmarks and performance has not generalized to unseen places. We present a new geolocalization system that combines semantic geocell creation multi-task contrastive pretraining and a novel loss function. Additionally our work is the first to perform retrieval over location clusters for guess refinements. We train two models for evaluations on street-level data and general-purpose image geolocalization; the first model PIGEON is trained on data from the game of GeoGuessr and is capable of placing over 40% of its guesses within 25 kilometers of the target location globally. We also develop a bot and deploy PIGEON in a blind experiment against humans ranking in the top 0.01% of players. We further challenge one of the world's foremost professional GeoGuessr players to a series of six matches with millions of viewers winning all six games. Our second model PIGEOTTO differs in that it is trained on a dataset of images from Flickr and Wikipedia achieving state-of-the-art results on a wide range of image geolocalization benchmarks outperforming the previous SOTA by up to 7.7 percentage points on the city accuracy level and up to 38.8 percentage points on the country level. Our findings suggest that PIGEOTTO is the first image geolocalization model that effectively generalizes to unseen places and that our approach can pave the way for highly accurate planet-scale image geolocalization systems. Our code is available on GitHub.
Improving Spectral Snapshot Reconstruction with Spectral-Spatial Rectification
Authors: Jiancheng Zhang; Haijin Zeng; Yongyong Chen; Dengxiu Yu; Yin-Ping Zhao
Venue: CVPR 2024 (main)

Abstract: How to effectively utilize the spectral and spatial characteristics of Hyperspectral Image (HSI) is always a key problem in spectral snapshot reconstruction. Recently the spectra-wise transformer has shown great potential in capturing inter-spectra similarities of HSI but the classic design of the transformer i.e. multi-head division in the spectral (channel) dimension hinders the modeling of global spectral information and results in mean effect. In addition previous methods adopt the normal spatial priors without taking imaging processes into account and fail to address the unique spatial degradation in snapshot spectral reconstruction. In this paper we analyze the influence of multi-head division and propose a novel Spectral-Spatial Rectification (SSR) method to enhance the utilization of spectral information and improve spatial degradation. Specifically SSR includes two core parts: Window-based Spectra-wise Self-Attention (WSSA) and spAtial Rectification Block (ARB). WSSA is proposed to capture global spectral information and account for local differences whereas ARB aims to mitigate the spatial degradation using a spatial alignment strategy. The experimental results on simulation and real scenes demonstrate the effectiveness of the proposed modules and we also provide models at multiple scales to demonstrate the superiority of our approach.
Self-correcting LLM-controlled Diffusion Models
Authors: Tsung-Han Wu; Long Lian; Joseph E. Gonzalez; Boyi Li; Trevor Darrell
Venue: CVPR 2024 (main)

Abstract: Text-to-image generation has witnessed significant progress with the advent of diffusion models. Despite the ability to generate photorealistic images current text-to-image diffusion models still often struggle to accurately interpret and follow complex input text prompts. In contrast to existing models that aim to generate images only with their best effort we introduce Self-correcting LLM-controlled Diffusion (SLD). SLD is a framework that generates an image from the input prompt assesses its alignment with the prompt and performs self-corrections on the inaccuracies in the generated image. Steered by an LLM controller SLD turns text-to-image generation into an iterative closed-loop process ensuring correctness in the resulting image. SLD is not only training-free but can also be seamlessly integrated with diffusion models behind API access such as DALL-E 3 to further boost the performance of state-of-the-art diffusion models. Experimental results show that our approach can rectify a majority of incorrect generations particularly in generative numeracy attribute binding and spatial relationships. Furthermore by simply adjusting the instructions to the LLM SLD can perform image editing tasks bridging the gap between text-to-image generation and image editing pipelines. Our code is available at: https://self-correcting-llm-diffusion.github.io.
PACER+: On-Demand Pedestrian Animation Controller in Driving Scenarios
Authors: Jingbo Wang; Zhengyi Luo; Ye Yuan; Yixuan Li; Bo Dai
Venue: CVPR 2024 (main)

Abstract: We address the challenge of content diversity and controllability in pedestrian simulation for driving scenarios. Recent pedestrian animation frameworks have a significant limitation wherein they primarily focus on either following trajectory or the content of the reference video consequently overlooking the potential diversity of human motion within such scenarios. This limitation restricts the ability to generate pedestrian behaviors that exhibit a wider range of variations and realistic motions and therefore restricts its usage to provide rich motion content for other components in the driving simulation system e.g. suddenly changed motion to which the autonomous vehicle should respond. In our approach we strive to surpass the limitation by showcasing diverse human motions obtained from various sources such as generated human motions in addition to following the given trajectory. The fundamental contribution of our framework lies in combining the motion tracking task with trajectory following which enables the tracking of specific motion parts (e.g. upper body) while simultaneously following the given trajectory by a single policy. This way we significantly enhance both the diversity of simulated human motion within the given scenario and the controllability of the content including language-based control. Our framework facilitates the generation of a wide range of human motions contributing to greater realism and adaptability in pedestrian simulations for driving scenarios.
LTM: Lightweight Textured Mesh Extraction and Refinement of Large Unbounded Scenes for Efficient Storage and Real-time Rendering
Authors: Jaehoon Choi; Rajvi Shah; Qinbo Li; Yipeng Wang; Ayush Saraf; Changil Kim; Jia-Bin Huang; Dinesh Manocha; Suhib Alsisan; Johannes Kopf
Venue: CVPR 2024 (main)

Abstract: Advancements in neural signed distance fields (SDFs) have enabled modeling 3D surface geometry from a set of 2D images of real-world scenes. Baking neural SDFs can extract explicit mesh with appearance baked into texture maps as neural features. The baked meshes still have a large memory footprint and require a powerful GPU for real-time rendering. Neural optimization of such large meshes with differentiable rendering pose significant challenges. We propose a method to produce optimized meshes for large unbounded scenes with low triangle budget and high fidelity of geometry and appearance. We achieve this by combining advancements in baking neural SDFs with classical mesh simplification techniques and proposing a joint appearance-geometry refinement step. The visual quality is comparable to or better than state-of-the-art neural meshing and baking methods with high geometric accuracy despite significant reduction in triangle count making the produced meshes efficient for storage transmission and rendering on mobile hardware. We validate the effectiveness of the proposed method on large unbounded scenes from mip-NeRF 360 Tanks & Temples and Deep Blending datasets achieving at-par rendering quality with 73x reduced triangles and 11x reduction in memory footprint.
Don't Drop Your Samples! Coherence-Aware Training Benefits Conditional Diffusion
Authors: Nicolas Dufour; Victor Besnier; Vicky Kalogeiton; David Picard
Venue: CVPR 2024 (main)

Abstract: Conditional diffusion models are powerful generative models that can leverage various types of conditional information such as class labels segmentation masks or text captions. However in many real-world scenarios conditional information may be noisy or unreliable due to human annotation errors or weak alignment. In this paper we propose the Coherence-Aware Diffusion (CAD) a novel method to integrate confidence in conditional information into diffusion models allowing them to learn from noisy annotations without discarding data. We assume that each data point has an associated confidence score that reflects the quality of the conditional information. We then condition the diffusion model on both the conditional information and the confidence score. In this way the model learns to ignore or discount the conditioning when the confidence is low. We show that our method is theoretically sound and empirically effective on various conditional generation tasks. Moreover we show that leveraging confidence generates realistic and diverse samples that respect conditional information better than models trained on cleaned datasets where samples with low confidence have been discarded.