DINOv2

image · discriminative · anchor

Findings

• IC-069EVA-CLIP's dense patch features are semantically contaminated by surrounding context, degrading their spatial quality [context]
• IC-071DINOv2's dense features are dominated by global context, impairing fine-grained spatial detail
• IC-1290CLIP, DINO, and DINOv2 as zero-shot natural baselines score below the 50% chance level on Bongard-OpenWorld due to adversarial query selection
• IC-1469Progress on standard ImageNet generalization benchmarks is 2.5x faster than progress on crowdsourced global data (DollarStreet, GEODE) across 98 vision models
• IC-1470Geographic disparities (Europe-Africa accuracy gap) are large across all 98 models and have more than tripled between least and best performing models on DollarStreet
• IC-1472DINOv2 (86M parameters) achieves the smallest GEODE geographic disparity (2.46% Europe-Africa gap) among all 98 models in the testbed
• IC-1619DINOv2's patch-level features outperform CLIP and MAE for cross-image semantic feature matching
• IC-215DINOv2's zero-shot attention maps focus on irrelevant foreground objects (vehicles, advertisements) rather than scene structure, degrading its VPR recall on challenging datasets
• IC-216DINOv2's value (V) facet from self-attention at layer n-1 encodes the most effective local features for VPR re-ranking, outperforming query and key facets, and layer n-1 outperforms the final layer n
• IC-257327 DNNs approach or exceed human accuracy on object depth order but are near chance on VPT-basic, while humans show the opposite pattern
• IC-357Off-the-shelf foundation models (DINO, CLIP, DINOv2, ViT) exhibit higher variance in their cosine similarity distributions than dataset-specific models, reducing the discriminative power of cosine similarity retrieval
• IC-499ViT patch embeddings contain local semantic information beyond the [cls] token, as shown by performance degradation when restricting the output head to [cls] only or removing positional embeddings
• IC-646DINOv2, DeiT-III, and OpenCLIP repurpose approximately 2% of patch tokens in low-informative background areas as internal registers, discarding local patch information while aggregating global image information; DINO does not exhibit this behaviour
• IC-647DINOv2's feature-map artifacts cause it to be incompatible with the LOSt unsupervised object discovery method, scoring far below DINO
• IC-666DINOv2, CLIP-vision, and VGG-19 representations all align with MEG brain responses, with DINOv2 showing particularly high retrieval performance for late brain activity after image offset
• IC-667CLIP's intermediate layer features encode object boundaries recoverable by k-means clustering, a property absent in shallow and deep layers [compared-to]
• IC-669DINOv2's features, when clustered, produce smooth semantic regions but lack instance-level boundary delineation

Shared mechanisms

• Depth-dependent structurealso in Baichuan 2, BERT, BLIP-2, BLOOM, Chameleon, CLIP / CLIP-ViT (LC), DeepFloyd IF, DeiT-III, DINO, Falcon, Gemma, Gemma 2, GPT-2, GPT-J, GPT-NeoX-20B, Griffin, I3D, Idefics, InstructBLIP, LLaMA, Llama 2 / Llama 2 base, Llama 3, Llama 3.1, Llama 3.2, Llama-3.2-3B, LLaVA, LLaVA-1.5 / LLaVA-v1.5, LLaVA-Phi, MAE, MAE-B/16, Mamba, MiniGPT-4, Mistral 7B / Mistral / Mistral 3 7B / Mistral-0.2-7B / Mistral-v0.1, Mixtral 8x7B / Mistral 8x7B Instruct / Mixtral 46.7B / Mixtral 8x7B Instruct / Mixtral-instruct-8x7b, mPLUG-Owl, MPT, MultiBERTs, MViT V2, OLMo / OLMo base, OpenCLIP, OPT, Phi-2, Pythia, Qwen 2, Qwen2-VL, Qwen2.5, RoBERTa / RoBERTa-L, RWKV, SALMONN, SAM, SlowFast, Stable Diffusion, Swin Transformer, TerraMind, TimesFormer, TSM, Uniformer, Vicuna, VideoMAE, ViT, X3D, Yi
• Distance preservationalso in CLIP / CLIP-ViT (LC), CoPlace, DINO, Gemma, Gemma 2, ImageBind, LanguageBind, LLaMA, Llama 2 / Llama 2 base, Llama 3, Llama 3.1, Llama-3.2-3B, LLaVA-1.5 / LLaVA-v1.5, LLaVA-Med, MAE, MAE-B/16, OpenCLIP, Pythia, ResNet / ResNet-152 / ResNet-101 / ResNet-50-BN, SigLIP, SLIP, TerraMind, ViT
• Failure modealso in AASIST, ADM, Aegis-Guard-Defensive, Alpaca, AnyLoc, AutoTikZ / DataTikZ, Baichuan, Baichuan 2, Baichuan2-13B, BakLLaVA, Bard, BEiT, BERT, BingChat, BLIP, BLIP-2, BLOOM, BSRNet, CF2, Chat-UniVi-7B, ChatGLM-6B / ChatGLM-6b-2, ChatGLM2, ChatGPT, CLAP, Claude 1.3, Claude 2.0, Claude 2.1, Claude 3, Claude 3.5, CLEAR, CLIP / CLIP-ViT (LC), CLIP4Clip, CLIPBERT, CLIPCap, CLMBR-T-BASE, CloFNet, Code Llama, CodeGeex2, CodeGen, CodeLlama-13B, CodeLlama-34B, CogVLM2, Cohere Command R, CoMEt, Command R+, CONCH, CycleGAN, DALL-E, DALL·E 2, DALL·E 3, DASR, DECAF, DeepSeek-2-Chat, DeepSeek-2-Coder, DeepSeek-V2-0628, DeepSeek-VL, DeepSeek-VL2, DeiT, DeiT-III, Depth Anything, DETR-R50, DimeNet++, DINO, DINO-FocalNet-Large, EGNN, Emu2, EquiformerV2, ESCN, ESM-2, ESM3, ESRGAN, EVA-CLIP, EVE, Falcon, Faster R-CNN / Faster R-CNN R50 / Faster R-CNN X101, FLAN-T5, Florence-2, FLUX / FLUX1, FoveaBox, Fuyu, Galactica-6.7B, GAT, 3D Gaussian Splatting, GCN, Gemini, Gemini 1.5 / Gemini Pro 1.5, Gemma, Gemma 2, GIN, GLIDE, GLM-4, GLM-4V, GloVe, GP-UNIT, GPT-2, GPT-3 / GPT base, GPT-3.5 / ChatGPT-3.5, GPT-4 / ChatGPT4 / GPT-4 Code Interpreter / GPT-4 Technical Report, GPT-4.1, GPT-4o, GPT-J, GPT-NeoX-20B, GraphSAGE, Grounding DINO, Guanaco, GVP, Hawkeye, HiFaceGAN, HPS, HuggingGPT, IDDPM, Idefics, Idefics2, ImageBind, ImageBind-LLM-7B, Imagen Video, ImageReward, 12-in-1, InstructBLIP, InstructGPT, InternLM-2.5-7B, InternLM-XComposer2-VL, InternVideo, InternVL-1.5, InternVL2, Koala, LegalBERT, LLaMA, Llama 2 / Llama 2 base, Llama 3, Llama 3.1, Llama 3.2, Llama-3.2-3B, Llama-3-2-Vision, LLaMA-Adapter v2, Llama Guard, Llama Guard 2, Llama-Guard 3, Llama-VID, LLaVA, LLaVA-1.5 / LLaVA-v1.5, LLaVA-Med, LLaVA-NeXT / LLaVA 1.6, LLaVA-OneVision, LongVA-7B, LOVT, LWM-1M-JAX, MACE, MAE, Med-Flamingo, Merlot Reserve, MGCA, MiDaS, MiniCPM-V, MiniGPT-4, Mip-Splatting, Mistral 7B / Mistral / Mistral 3 7B / Mistral-0.2-7B / Mistral-v0.1, Mistral Large 2, Mistral Large V2, Mixtral, Mixtral 8x7B / Mistral 8x7B Instruct / Mixtral 46.7B / Mixtral 8x7B Instruct / Mixtral-instruct-8x7b, MobileNetV2, Molmo, MolmoE-7B, Momentor, Moondream2, Moonshot-v1-8k, mPLUG-2, mPLUG-Owl, mPLUG-Owl3, mPLUG-Owl2, MPT, MSA Transformer, MultiBERTs, Nova Canvas, Nova Lite, Nova Pro, O1 / OpenAI-o1-preview, O3, O4-mini, OLMo / OLMo base, OneLLM, OpenAI Moderation, OpenChat-3.5-0106, OpenCLIP, OpenFlamingo, OPT, Otter, Otter-7B, PaLM 2, PaLM 62B, PandaGPT-7B, PerSAM, Phi-3, Phi-3.5 Mini Instruct, PickScore, PLIP, Prismatic, ProGen-2, Pythia, Qwen1.5, Qwen 2, Qwen 2.5 72B Instruct, Qwen2-VL, Qwen-Audio, Qwen-VL, Qwen2.5, Qwen2-Audio, R2D2, RadFM, RCExplainer, RD-SR, RealESRNet, Reprover, ResNet / ResNet-152 / ResNet-101 / ResNet-50-BN, RetinaNet-R50, RivaGAN, RS-LDS, RT-1, RT-2, SALMONN, SAM, SAM 2, SAULLM 54B, Scaffold-GS, SchNet, Seed-LLaMA-8B, SGC, SIREN, Sketch Transformer, SLD-max, SLD-medium, SLD-strong, SLDS, SLIMG, SpeechGPT, SphereNet, SRResNet, Stable Diffusion, StackLLaMA, Starcoder, StegaStamp, StyleGAN2-ADA, Swin Transformer, T5, TD-MPC, TerraMind, TimeChat, TranceptionEVE, TreeRing, Tulu 2, UnifiedQA, UniPerceiver, UNITER, UniVL, Van Gogh Diffusion, VERA, VGG / VGG13, Vicuna, Video-Chat-7B, Video-ChatGPT, Video-LLaMA, Video-LLaMA-2-13B, Video-LLaVA, VideoCLIP, ViLA-8B, ViLBERT, VindLU, VioLET, ViRTex, ViT, ViV1T, VTG-LLM, WildGuard, Wizardlm, X-CLIP, X-InstructBLIP-7B, XGen-MM, Xlm-R, Zephyr-7B-beta
• Linear representationalso in BLOOM, Cambrian-1, Chameleon, CLIP / CLIP-ViT (LC), EVA-CLIP, Falcon, Gemma, Gemma 2, GPT-2, GPT-J, HPSv2, ImageBind, InstructBLIP, LanguageBind, LLaMA, Llama 2 / Llama 2 base, Llama 3, Llama 3.1, Llama-3.2-3B, LLaVA-1.5 / LLaVA-v1.5, LLaVA-NeXT / LLaVA 1.6, MAE, Mamba, Mistral 7B / Mistral / Mistral 3 7B / Mistral-0.2-7B / Mistral-v0.1, OLMo / OLMo base, OpenCLIP, Phi-3, PickScore, Pythia, Qwen2-VL, Qwen2.5, ResNet / ResNet-152 / ResNet-101 / ResNet-50-BN, SALMONN, SAM, SigLIP, TerraMind, Tulu 2, Vicuna, ViT
• Register tokensalso in DeiT-III, DINO, GPT-2, MAE, OpenCLIP, TerraMind
• Scale-dependent behaviouralso in Aquila-7B, BEiT, BERT, BLOOM, Claude 2.1, Claude 3, Claude 3.5, CLIP / CLIP-ViT (LC), Code Llama, CodeGen, Cohere Command R, DeepSeek LLM, DeepSeekMoE, DeiT-III, DINO, EquiformerV2, ESCN, Falcon, FLAN-T5, Gemini 1.0 Pro, Gemini 1.5 / Gemini Pro 1.5, Gemma, Gemma 2, GPT-2, GPT-3 / GPT base, GPT-3.5 / ChatGPT-3.5, GPT-4 / ChatGPT4 / GPT-4 Code Interpreter / GPT-4 Technical Report, GPT-4o, GPT-J, GPT-Neo, I3D, Idefics, InternLM-2.5-7B, InternLM-XComposer2-VL, InternLM2, InternVL-1.5, InternVL2, LLaMA, Llama 2 / Llama 2 base, Llama 3, Llama 3.1, Llama 3.2, Llama-3.2-3B, LLaVA-1.5 / LLaVA-v1.5, LLaVA-NeXT / LLaVA 1.6, LongVA-7B, MAE, Mistral 7B / Mistral / Mistral 3 7B / Mistral-0.2-7B / Mistral-v0.1, Mixtral, Mixtral 8x7B / Mistral 8x7B Instruct / Mixtral 46.7B / Mixtral 8x7B Instruct / Mixtral-instruct-8x7b, Moirai, MPT, MViT V2, O1 / OpenAI-o1-preview, OLMo / OLMo base, OpenCLIP, OpenFlamingo, OpenLLaMA, OPT, PaLM 2, Phi-3, Platypus2-Instruct-70B, Pythia, Qwen, Qwen1.5, Qwen 2, Qwen 2.5 72B Instruct, Qwen2-VL, Qwen-Audio, Qwen2.5, RedPajama-INCITE, ResNet / ResNet-152 / ResNet-101 / ResNet-50-BN, SlowFast, Solar 10.7B, Stable Diffusion, StableLM, Swin Transformer, T5, TerraMind, text-ada-001, TigerBot-70B-Chat, TimesFormer, TSM, Tulu 2, Uniformer, Vicuna, VideoMAE, ViLA-8B, Wizardlm, X3D, XGLM, Xwin-LM-70B-v0.1, Yi