Trajtok Trajvitv2

TrajViT-v2 (small-scale image+video pretrain)

TrajViT-v2 wraps the TrajTok-v2 segmenter with a ViT-Large transformer and trains it under a CLIP-style image-text contrastive objective. Trajectory tokens (K=128 per clip from the segmenter) are contextualised by a ViT-Large, then the [CLS] feature is contrasted against BERT text embeddings via InfoNCE.

This release is a proof-of-concept checkpoint — it shows the trajectory-CLIP architecture trains end-to-end and produces reasonable embeddings. It is not a final-scale model and should not be used as a production retrieval encoder. See the limitations section.

Architecture

video / image  (T, 3, 224, 224)
     │
     ↓
SimpleSegmenter (DINOv3 + perceiver)         →  trajectory tokens  z  (K=128, D=512)
     │                                                              ↑ warm-started from
     │                                                              TrajTok-v2 segmenter
     ↓
ViT-Large transformer over z                  →  contextualised z' (K+1=129, D=512)
     │
     ↓
[CLS]-pool → vision_proj (linear) ────────────→ image embedding for InfoNCE
                                                  ↕
                                            text embedding ← BERT(caption)

Total parameters: ~316 M (segmenter ~59 M + ViT-Large ~303 M + BERT text encoder ~110 M + small projections).

Intended use

• Sanity-check baseline for trajectory-CLIP architectures — fine-tune on larger data + with better recipes to get competitive numbers.
• Starting point for fine-tuning on specialised retrieval or classification tasks.
• Demonstration of the architecture for research / teaching purposes.

How to use

import torch, yaml
from easydict import EasyDict as edict
from trajtok_segmenter.model.model_pretrain import SegmentCLIP
from trajtok_segmenter.text.tokenization_bert import BertTokenizer

cfg = edict(yaml.safe_load(open("trajtokv2/trajvitv2/configs/pretrain.yaml")))
tokenizer = BertTokenizer.from_pretrained(cfg.text_encoder)
model = SegmentCLIP(config=cfg, tokenizer=tokenizer).cuda().eval()

state = torch.load("path/to/latest.pth", map_location="cpu", weights_only=False)
model.load_state_dict(state["model"], strict=False)

# Encode an image (treat as T=1 clip)
import numpy as np; from PIL import Image
img = np.asarray(Image.open("cat.jpg").convert("RGB"))
# (preprocess to (1, 1, 3, 224, 224), then:)
# image_feat = model.vision_proj(model.encode_image(...)[1][:, 0])

# Encode text
tok = tokenizer(["a cat sleeping on a couch"], padding="max_length",
                truncation=True, max_length=77, return_tensors="pt").to("cuda")
text_feat = model.text_proj(model.encode_text(tok)[1][:, 0])
text_feat = torch.nn.functional.normalize(text_feat, dim=-1)

Full retrieval / zero-shot scripts: see trajvitv2/scripts/.

Training data

filteredmixdata_new — a small image+video mixture (~1.3 M samples):

Source	Samples	Type
big_image_new	~300 K	filtered image-caption pairs
big_video_new	~1 M	filtered video-caption pairs

Important caveat: this is a much smaller corpus than what production CLIP-style models (CLIP, OpenCLIP, SigLIP) train on (typically 100 M – 5 B pairs). The numbers this checkpoint achieves on standard benchmarks will be correspondingly lower.

Training configuration

Knob	Value
Vision backbone	DINOv3-small ConvNeXt (init from Meta)
Trajectory transformer	ViT-Large (random init, our CustomTransformer wrapper)
Text encoder	bert-base-uncased
Trajectory tokens K	128
Embedding dim (segmenter)	512
Contrastive embedding dim	256
Input resolution	224×224
Loss	InfoNCE (loss_weight.itc=1.0) + segmentation loss (loss_weight.icl=1.0) jointly
Optimizer	AdamW (lr=1e-4, wd=0.02)
Schedule	cosine, 1-epoch warmup
Epochs	20
Per-modality batch size	image=64, video=8
Hardware	1 node × 8 × H100 (80 GB)
Segmenter warm-start	from TrajTok-v2 released segmenter ckpt

Limitations

• Small-scale training data (~1.3 M pairs) — expect retrieval R@1 and zero-shot ImageNet numbers well below CLIP / SigLIP. This is a research proof-of-concept, not a production model.
• Frame count cap: trained at T ≤ 8 frames per clip.
• BERT text encoder is a deliberate choice for parity with the original segmenter training pipeline; modern alternatives (T5, OpenCLIP's text encoder) would likely lift retrieval performance.
• The ViT-Large transformer is trained from scratch (random init) — initialising from a pretrained ViT and adapting it to trajectory tokens could close some of the gap to large-scale baselines.

Citation

@article{zheng2026trajtokv2,
  title   = {TrajTok-v2: Trajectory-aware visual tokenization for vision-language models},
  author  = {Zheng, Chenhao and others},
  journal = {arXiv preprint arXiv:2602.22779},
  year    = {2026},
}

License

Apache-2.0. The wrapped DINOv3 and BERT components retain their original Apache-2.0 / Apache-2.0 licenses respectively.